human curiosity essay

Ideas and insights from Harvard Business Publishing Corporate Learning

The Importance Of Being Curious

“Why do I feel cold and shiver when I have a fever?”

I knew the day would come when my little girl would learn to talk and inevitably start asking those much-anticipated questions. The questions themselves weren’t worrying me.  I was actually looking forward to seeing where her curiosity would lie.

What was bothering me was whether or not I would know the answers.

In the age of the smartphone, this may seem like a silly worry.  Surely, the answers to almost everything would be just one Google away.

Still, I struggled with how I was going to prepare to become an all-knowing mother. Then one day it struck me: I didn’t need to have all the answers. What a great example I could set if I let my daughter know that I, too, am still learning. And I realized how much more I could learn if I took another look at things I thought I already knew the answer to with the curiosity of a child. My little girl’s mind is a beginner’s mind – curious, open to new ideas, eager to learn, and not based on preconceived notions or prior knowledge. I decided that I would approach her questions with a beginner’s mind, too.

Once I decided to become more curious, I started noticing that curiosity was becoming more prominent in the workplace, too. Leaders, it seems, don’t need to have all the answers, either. But they do need to be curious.

Curious about curiosity, I searched for answers, and found frequent references to Albert Einstein’s famous words, “I have no special talent. I am only passionately curious.” We might well quibble with the notion that Einstein had no “special talent,” but he wouldn’t have solved the riddles of the universe if not for his passionate curiosity. Then I came across another Einstein quote: “The important thing is not to stop questioning. Curiosity has its own reason for existence.”

Curiosity’s reason for existence in the workplace

Decades ago, management thinker Peter Drucker placed knowing the right questions to ask at the core of his philosophy on strategic thinking. Many of today’s leaders have adopted Drucker’s “be (intelligently) curious” philosophy, an approach that is becoming more salient as the world increases in complexity.

Warren Berger, in “ Why Curious People Are Destined for the C-Suite, ” cited Dell CEO Michael Dell’s response to a PwC survey that asked leaders to name a trait that would most help CEOs succeed. Dell’s answer? “I would place my bet on curiosity.” Dell was not alone. Alan D. Wilson, then CEO of McCormick & Company, responded that those who “are always expanding their perspective and what they know – and have that natural curiosity – are the people that are going to be successful.”

Leaders don’t need to know everything. In fact, it’s an impossibility. Things change too rapidly for that. What worked yesterday can’t be guaranteed to work tomorrow. Disrupters are just around the corner. If you’re not one of them, you may well end up a disruptee. Today’s leaders need to be curious, and know how to ask the questions that lead them to consider new ideas.

How we can all develop curiosity

Becoming a mum has taught me how to handle my little girl’s curiosity. It strikes me that leaders in new roles also have to learn what to do and how to act in ways that are new and different. What I find works best is approaching your new role with a curiosity mindset, completely open to new ideas and suggestions. Here are some ways to develop your curiosity:

• Apply a beginner’s mind:  Be open to and look for new and novel ways of doing things.
• Ask questions, listen and observe:  Seek first to understand, not to explain.
• Try something new:  Take a different route to work, read a book in a genre you usually avoid, go to an art gallery you wouldn’t normally go to. Each of these activities opens your mind to new points of view.
• Be inquisitive:  Ask others their opinions, perspectives, and their approaches to certain things. Everyone does things a bit differently, and there are potential new answers and solutions to problems hidden in other people’s thinking.

These are a few of my ideas. I’d be interested in hearing yours. How do you stay curious?

Dalia Molokhia is a senior learning solutions manager at Harvard Business Publishing Corporate Learning. Email her at  [email protected] .

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Why are humans so curious?

Curiosity is a hallmark of the human experience. But why?

The human craving to know and understand is the driving force behind our development as individuals and even our success as a species. But curiosity can also be dangerous, leading to stumbles or even downfalls, so why does this impulse so often compel us throughout life?

Put another way, why are humans so curious? And given curiosity's complexity, do scientists even have a definition for this innate drive?

Curiosity is so ingrained, it helps us learn as babies and survive as adults. As for the definition, there isn't one set in stone. Researchers across many disciplines are interested in curiosity, so it's no surprise there isn't a widely accepted definition of the term. William James, one of the first modern psychologists, called it "the impulse towards better cognition." Ivan Pavlov wrote that dogs (of course it was dogs) are curious about novel stimuli through the "what-is-it?" reflex that causes them to focus spontaneously on something new that comes into their environment.

Related: Why haven't all primates evolved into humans?

While pinning down a definition has proven tricky, "the general consensus is it's some means of information gathering," Katherine Twomey, a lecturer in language and communicative development at the University of Manchester in the United Kingdom, told Live Science. 

Psychologists also agree that curiosity isn't about satisfying an immediate need, like hunger or thirst; rather, it's intrinsically motivated. 

Making our way in the world

Curiosity encompasses such a large set of behaviors, there probably isn’t any single "curiosity gene" that makes humans wonder about the world and explore their environment. That said, curiosity does have a genetic component. Genes and the environment interact in many complex ways to shape individuals and guide their behavior, including their curiosity. 

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Researchers did identify changes to a specific gene type that is more common in individual songbirds that are especially keen on exploring their environment, according to a 2007 study published in the journal Proceedings of the Royal Society B, Biological Science . In humans, mutations in this gene, known as DRD4 , have been associated with a person’s propensity to seek novelty.

Regardless of their genetic makeup, infants have to learn an incredible amount of information in a short window of time, and curiosity is one of the tools humans have found to accomplish that gargantuan task.

"If infants weren't curious, they'd never learn anything and development wouldn't happen, Twomey said. 

Hundreds of studies show that infants prefer novelty. In a classic 1964 study , a psychologist showed that infants between 2 months and 6 months old grew less and less interested in a complex visual pattern the more they looked at it. A 1983 study in the journal Developmental Psychology of slightly older children (ages 8 months and 12 months old) indicated that once babies got used to familiar toys, they preferred new ones, a scenario that caregivers likely know all too well. 

This preference for novelty has a name: perceptual curiosity. It's what motivates non-human animals, human infants and probably human adults to explore and seek out new things before growing less interested in them after continued exposure. 

As these studies show, infants do this all the time. Babbling is one example. 

"The exploration they do is systematic babbling ," Twomey said. When most babies are just a few months old, they start making vowel and repetitive, speech-like sounds as they learn how to speak. Babbling demonstrates the utility of perceptual curiosity. It begins as a completely random exploration of what their vocal anatomy can do.

Eventually "they'll hit on something and think 'That sounds like something my mum or dad would do,'" she said. And then they do it again. And again. 

But it isn't just infants. Crows are famous for using perceptual curiosity as a means of learning. For instance, the drive to explore their environment probably helps crows learn to fashion the simple tools they use to fish larvae out of hard-to-reach crevices. Moreover, experiments with robots programmed to be curious have shown that exploration is a powerful way to adapt to a new environment.

Making the world work for us

Another kind of curiosity is distinctively human. Psychologists call it epistemic curiosity, and it's about seeking knowledge and eliminating uncertainty. Epistemic curiosity emerges later in life and might require complex language, Twomey said. 

For Agustín Fuentes, a professor of anthropology at Princeton University, this form of curiosity has set humans — and probably all members of the genus Homo — apart from other animals and paved the way for us to populate nearly every corner of the world, inventing technologies from hand axes to smart phones. 

"Humans, in our distinctive lineage, went beyond simply tweaking nature to imagining and inventing whole new possibilities that emerge from that kind of curiosity," Fuentes told Live Science. 

Related: Can you learn anything while you sleep?

But curiosity comes with a cost. Just because humans can imagine something doesn't mean it will work, at least not at first. In some situations, the stakes are low and failure is a healthy part of growth. For instance, many babies are perfectly proficient crawlers, but they decide to try walking because there’s more to see and do when they stand upright, according to Twomey. But this milestone comes at a small cost. A study of 12- to 19-month-olds learning how to walk documented that these children fell down a lot. Seventeen times per hour, to be exact. But walking is faster than crawling, so this "motivates expert crawlers to transition to walking," the researchers wrote in the 2012 study, published in the journal Psychological Science .

— Why don't we remember being babies?

— Why do people have different personalities?

— Why can't we remember our dreams?

Sometimes, however, testing out a new idea can lead to disaster.

"Curiosity probably led to the vast majority of human populations going extinct," Fuentes said. 

For instance, the Inuit of the Arctic regions of Greenland, Canada and Alaska, and the Sámi people of Europe’s northern reaches have "created incredible modes to deal with the challenges" of living in northern climates, but "what we forget about are the probably tens of thousands of populations that tried and failed to make it" in those challenging landscapes, he said. 

Ultimately, curiosity is about survival. Not all curious humans lived to pass their penchant for exploration on to their descendants, but those who did helped create a species that can't help but think, "Huh, I wonder what would happen if ..." 

Originally published on Live Science.

Grant Currin is a freelance science journalist based in Brooklyn, New York, who writes about Life's Little Mysteries and other topics for Live Science. Grant also writes about science and media for a number of publications, including Wired, Scientific American, National Geographic, the HuffPost and Hakai Magazine, and he is also a contributor to the Discovery podcast Curiosity Daily. Grant received a bachelor's degree in Political Economy from the University of Tennessee. 

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Essays About Curiosity: Top 5 Examples and 10 Prompts

Are you writing essays about curiosity? Then, read our guide of helpful essay examples and writing prompts.

Curiosity refers to the strong desire and active interest to learn something. It could start with a burning question that leads to more questions. This series of questioning can evolve into a pursuit that paves the way for discoveries. Curiosity can change how we perceive life and our world. While everyone is inherently curious, how we use our curiosity, for good or bad, shows who we are as people.

Check out our essay examples and topic prompts for your curiosity essay , and stay curious till the end. And when your essay is complete, check out our best essay checkers and take the slog out of proofreading.

1. Curiosity: Why It Matters, Why We Lose It, And How To Get It Back by Christy Geiger

2. did curiosity really kill the cat by mario livio, 3.  why curiosity, diversity, and inclusion are the secrets to successful business transformation by beatriz sanz saiz, 4. the five dimensions of curiosity by todd b. kashdan et. al, 5. curiosity: we’re studying the brain to help you harness it by ashvanti valji and matthias gruber, 1. how has curiosity helped you in life, 2. the benefits of curiosity, 3. how does curiosity lead to scientific discoveries, 4. encouraging curiosity in the classroom, 5. diverse vs. specific curiosity, 6. can curiosity be practiced, 7. curiosity in early civilization, 8. curious animals: what are they thinking, 9. the curiosity rover, 10. negative effects of curiosity.

“…[A]s an adult, we can reach a learning plateau. We feel good to get to a point of understanding and knowledge, but begin to lose our curiosity. We find it easier to live as the expert who knows than the student who grows.”

Adulthood can have a negative impact on our levels of wonder and curiosity. Geiger believes it’s time to regain our childlike curiosity as we move to a tech-driven industrial world where constant innovation and adoption of technologies are required. You might also be interested in these essays about critical thinking.

“Curiosity is the best remedy for fear. What I mean by that is that often we are afraid of the unknown, of those things we know very little about. Becoming curious about them, and making an effort to learn more, usually acts to relieve that fear.”

Who would’ve thought an essay could be weaved out from a common expression of curiosity? This curiosity essay finds that the saying “curiosity killed the cat” started quite differently than we know it today. Its meaning now evolves to echo parts of history when conventional and extremist ideologies would silence inquisitive minds to avoid being challenged and overturned.

“To be a leader in a context of superfluid markets, where everything is connected, an organization needs to constantly explore which are the new “needs,“ which technologies exist, how they can be maximized and where they can be used to innovate boldly to create new experiences, goods and services.”

Curiosity will drive businesses to survive and thrive in this digital age. But, they also need to seek assistance from diversity and an inclusive organization. With these two, businesses can stimulate new thinking and perspectives that can feed into the curiosity of the organization on the ways it can reach its goals and be the market’s next disruption.

“Rather than regard curiosity as a single trait, we can now break it down into five distinct dimensions. Instead of asking, ‘How curious are you?’ we can ask, ‘How are you curious?’”

Kashdan builds on existing curiosity research to identify five dimensions of curiosity : joyous exploration, deprivation sensitivity, stress tolerance, social curiosity, and thrill-seeking. Once you’ve assessed the right curiosity type for you, it might do wonders in catalyzing your curiosity into progress and development outcomes for your goals and well-being.

“It might seem obvious that if you are curious about something, you pay more attention to it, making it easier to remember later – but the effects of curiosity on memory are more complex than this.”

The essay presents new research on how a type of curiosity aiming to bridge information gaps connects with brain functions associated with enhanced learning. As far as education is concerned, the discovery strongly supports the need to create an environment to encourage students to ask questions rather than just give children a set learning program to consume.

10 Writing Prompts For Essays About Curiosity

Narrate an instance in your life when curious questions led to positive findings and experiences that helped you in life. Whether it was acing an exam, learning a new language, or other aspects of everyday life. Elaborate on how this encouraged you to be more interested and passionate about learning. See here our storytelling guide to help you better narrate your story. 

Research shows that curiosity can stimulate positive emotions. Many research studies outline the other benefits of curiosity to our health, relationships, happiness, and cognitive abilities. Gather more studies and data to elaborate on these advantages. To create an engaging piece of writing, share your experience on how curiosity has influenced your outlook on life. 

Albert Einstein is renowned worldwide as a famous theoretical physicist. Throughout his research, he used curious thinking and openmindedness to write his theoretical papers, changing the world as we know it. Curiosity is an essential attribute of scientists, as they can look for solutions to problems from a whole new angle. For this essay, look a the role of curiosity in the scientific process. How does a curious mindset benefit scientific discoveries? Conduct thorough research and use real-life examples to show your findings and answer this question.

School classrooms can be the playground of a student’s imagination and curiosity. In your essay, write about how your school and teachers encourage students to ask questions. Next, elaborate on how the learning prompts promote curiosity. For example, some teachers tell students that it is okay to fail sometimes. This assurance helps students think with new perspectives and solutions without the fear of failure.

When researching the different kinds of curiosity, you will find two categories- diverse and specific curiosity. Look into the different attributes of these curiosity types, and identify which one, in your opinion, is the better type of curiosity to foster. For an interesting argumentative essay, you can research which kind of curiosity you have and discuss whether you have a better or worse approach to curious thinking. Pull facts from online research to support your argument and include personal anecdotes to engage your readers.

Curiosity is an inherent human trait. We are all curious. But like any trait, we can practice being curious to improve our thinking. In this writing prompt, provide your readers with strategies that enhance curiosity. For example, meditation can help stimulate more curious thoughts. 

In early civilization, people answered many of life’s questions with religion. How did humanity shift from heavily relying on gods to believing in science? What part does curiosity play in this shift? Try piquing your curious mind and answer these questions in your essay for an exciting piece of writing. 

If animals solely relied on their basic instincts and functions, there is a high chance they would not survive in our world. According to Primatologist Richard Bryne in his paper Animal Curiosity , some animals can demonstrate curious behaviors that lead to new learning and survival skills. For this writing prompt, peer into curiosity in the animal kingdom and cite animals known to have high intelligence. Is curiosity at the foundation of their high IQs? Discuss this question in your essay.

This essay prompt is about the car-sized Curiosity Rover of NASA. The rover was designed to navigate the Gale crater on Mars and collect rock and soil samples for analysis. In your essay, research and write about why it was named “Curiosity” and its significant contributions to the Mars exploration mission.

Curiosity can have negative undertones from the expression “curiosity killed the cat.” Get to the heart of the matter and look through existing literature on the adverse outcomes of curiosity. One example to cite could be this study which concluded that one kind of curiosity is associated with errors, confusion, lack of humility, and vulnerability to fake news and so-called pseudo-profound bullshits. 

Curious to learn more about effective writing? Check out our guide on how to write an argumentative essay .  If writing an essay sounds like a lot of work, read our guide on how to write a five-paragraph essay .

The Science of Curiosity

Why are humans curious? Are other animals curious? How does curiosity work? Take a dive with us into the expansive history of curiosity and how it has shaped humankind. Explore the neuroscience that drives curiosity. And discover how your brain actually rewards you for being curious!

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Perhaps it was a bolt of lightning that piqued the early human’s curiosity; perhaps it was a raging wildfire. But once upon a time, an early human channeled inspiration into pure ingenuity and figured out how to start a fire. The control of fire supplemented humankind’s first invention, the stone tool. Next came boats, and then spears; then language, glue, clothing, and even the flute.

Each of these incredible inventions came to fruition in the mind of early humans many tens of thousands of years ago. Some sort of special spark drove humans to explore, discover, and later, to invent. That special spark lives within each of us, too. It makes us eager to learn things and to solve problems. Whenever you’re listening to music, reading a book, or watching TV, it’s there, helping your imagination soar. This special spark is curiosity , the desire to seek out new knowledge and learn how things work.

Why are we curious? How does curiosity ‘work’ in the brain? If there’s one thing that stimulates our curiosity most, it’s a complex topic shrouded in mystery. So where do we start?

One way to begin exploring curiosity is to understand ‘information seeking’. This behavior is observable across the entire animal kingdom – from apes and dolphins all the way down to crabs and tiny nematode worms. ‘Information seeking’ means that every animal seeks information about their environment. This is so they know how to navigate it. In fact, it’s why sensory organs exist – to supply the brain with information that helps you understand your environment and make better choices.

But when does information seeking qualify as curiosity? The difference, we now believe, is in the motivation. If you’re seeking knowledge because of an external motivation, like school or work, then it does not qualify as curiosity. But if you’re seeking knowledge because you’re internally motivated – because you just want to know the answer – that’s curiosity. Think about the early human, 35,000 years ago, who made the first flute. They were not driven by a need to stay warm or eat food; instead, they were internally motivated to make an instrument that could make a beautiful sound.

When something piques your curiosity – say, an interesting fact, or an unexpected noise in the other room – your brain enters into what’s called the “curiosity state.” First, the parts of the brain that are sensitive to unpleasant conditions light up. This shows that you are slightly uncomfortable, because you recognize you are lacking certain knowledge. Then, the parts of your brain responsible for learning and memory kick into high gear, so that you can learn, and remember what you’ve learned, more efficiently. It is at this point that you are primed to begin your search for answers. And when you actually begin learning new facts in your curiosity state, something even more interesting than heightened memory happens: your reward circuitry kicks in.

That’s right – your brain rewards you for being curious, and for pursuing that curiosity. Researchers have determined that dopamine, the brain’s reward chemical, is intricately linked to the brain’s curiosity state 1 . When you explore and satisfy your curiosity, your brain floods your body with dopamine, which makes you feel happier. This reward mechanism increases the likelihood that you’ll try and satisfy your curiosity again in the future.

The idea that our brains reward us for learning actually lines up with the hypothesis that curiosity helped our early human ancestors survive, too. Think about the usefulness of a stone tool or a boat. Humans needed to understand the environment, and manipulate it, in order to survive. Whether it meant knowing the best routes to flank animals on a hunt, where the best caves were for shelter, or how to find edible plants and berries, curiosity about the environment led to better survival. Our most curious ancestors had an advantage over those who weren’t curious. Over thousands of years, only the most curious people reproduced, leading to the characteristic curiosity of modern-day humans.

Today, our curiosity isn’t so useful in terms of survival. But it is useful when we think about education, or even what makes us happy. And when it comes to education or satisfying your curiosity, scientists say there are two distinct types of curiosity that we can express.

The first is called epistemic curiosity. Have you ever gone down a rabbit hole of link-clicking on the Internet? Or gotten so obsessed with a favorite book series or TV show that you had to research everything you could about it? That’s epistemic curiosity: the drive to eliminate information gaps and learn new explicit information. When you feel that thirst to acquire new knowledge, your brain actually responds as if you are actually thirsty or hungry – that’s where the areas sensitive to unpleasant conditions light up. And that’s why it feels so great to satisfy your curiosity (thanks, dopamine)!

The second type of curiosity you might find yourself expressing is empathic curiosity. Human life is built on relationships and interactions between people, and empathic curiosity is the drive to know more about what other people think and feel. When you are in comfortable social situations, your ‘curiosity state’ is especially pleasurable, according to research, and again, that’s when your dopamine releases in high levels.

Encouraging both types of curiosity in yourself is an important step in becoming a well-rounded learner. Greater knowledge about yourself and how you express your curiosity can help you with that process. This is why Britannica has developed a quiz about curiosity (that you can take right now!) and a whole host of information and resources on the subject.

In today’s world, being curious can enrich your life massively. Pursuing your passions is satisfying both in the short term and in the long term. Whether you go exploring your curiosity through social events or study, and whether you dive into biology, philosophy, psychology, your environment, or beyond, remember that different approaches will suit different people – and that it’s what each of us does with the information that matters.

10 Ways to Improve Your Curiosity

Power up your passion, ask awesome questions, teach and be taught, what kind of curious are you.

Have you ever wondered what your unique inquiry abilities are, or how your curiosity style aligns with the greatest minds in history? Discover your Curiosity Type through a series of thought-provoking questions, from who inspires you most, to what you’d most like to understand. The Curiosity Quiz will reveal which of the four Curiosity Types (the Artist, Inventor, Explorer, or Scientist) you align with. Is your curiosity piqued?

Definition of Curiosity, Its Causes and Importance Essay

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Introduction

Importance of curiosity.

One might ask, “What is curiosity?” Curiosity is an observable feeling, usually portrayed by people and some specific animals and appears like a force that drives them into knowing, meeting, or seeing new things. It arouses their emotional behaviour. Though claims are that it killed the cat, it has been proved that, it is the force behind all scientific inventions. It has brought about the most expensive and interesting discoveries by both scientists and artists.

It is a natural trait whose signs become evident right from birth when a baby shows the desire to explore not only its mother, but also anything within its proximity. Any trait is categorised based on its impact to the individual and the entire society. Though it cannot be wholly supported by all, majority will go for it owing to its position in the global technology. Based on these expositions, I believe curiosity, is a character that needs to be grown and developed in the minds of all people who believe that they can be great.

The desire for knowledge serves as the root cause of curiosity. Straight from childhood to old age, there is always a visible yearning depicted by all people and some categories of animals. A child will crawl or cry as an expression of its want for something while old people will always be questioning themselves about nature, demanding to know why it has to deprive them of the energy to carry out various jobs. This is none but curiosity.

Another cause of curiosity is the urge to satisfy ones senses. The need to see, hear, touch, among others, has been proved to arouse ones desire, forcing him/her to satisfy them. Practically, when people get rumours about something, be it a funny place, an interesting story, or a weird animal, they desire to actualise the rumours.

If it calls for them to see, in order to be satisfied, they must see failure to which an unmet requirement is registered in the person’s mind. He/she will be experiencing some sort of a force or an inner voice telling him/her to rise up for that need. This has to do with nothing else, but curiosity.

One of the major areas curiosity serves a vital role is education. Its contribution towards the performance of students is quite significant. It has become so crucial that some colleges have opted to introduce it as subject compulsory to every student. Through it, learners have made long steps as far as inventions are concerned.

Moreover, it serves as a motivational tool by learners. For instance, if ones teacher is a professor, he/she feels motivated and wants to experience the feeling of professors. He/she opts to know the steps the fellow followed into achieving such a high level of education. In their minds, learners create imaginary figures that act as role models whom they desire to take after, given the time and opportunity. By so doing, they end up boosting education status through their curiosity.

In addition, curiosity plays a major role in nurturing patience. As the claim goes, where there is patience, there is payment. This stands out in people who desire to be, or to own something that takes time before it happens. For instance, a young boy hoping to become a pilot has to develop patience within him because, he has to wait until he gets what it takes, for one to be a pilot and this calls for a serious view of education as the only way through. Hence, curiosity is a trait, crucial in education and cannot be avoided.

Another crucial importance of curiosity is that, it engages ones mind, making him/her active rather than passive. It has been proved that where there is activity of the mind, there is God’s dwelling place. It also makes ones mind alert of new ideas and methods of doing things. This is so because whoever is curious, he/she is ever learning day by day. It opens up the mind of people making them believe in the possibility of everything they do.

It also plays a key role in boosting ones enjoyment. Those who are curious are ever enjoying the interests of what they encounter everyday. The human mind is always welcoming when it comes to new ideas or things. Therefore, the fact that curiosity makes one learn new things on a daily basis, it is clear that he/she is always joyful.

In conclusion, Curiosity can overturn the world in terms of inventions and developments. Owing to what it has done for the few who developed it before, it stands out as a character that needs to be acquired by all, who believe in living up to the top of their dreams.

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IvyPanda. (2018, July 16). Definition of Curiosity, Its Causes and Importance. https://ivypanda.com/essays/curiosity/

"Definition of Curiosity, Its Causes and Importance." IvyPanda , 16 July 2018, ivypanda.com/essays/curiosity/.

IvyPanda . (2018) 'Definition of Curiosity, Its Causes and Importance'. 16 July.

IvyPanda . 2018. "Definition of Curiosity, Its Causes and Importance." July 16, 2018. https://ivypanda.com/essays/curiosity/.

1. IvyPanda . "Definition of Curiosity, Its Causes and Importance." July 16, 2018. https://ivypanda.com/essays/curiosity/.

Bibliography

IvyPanda . "Definition of Curiosity, Its Causes and Importance." July 16, 2018. https://ivypanda.com/essays/curiosity/.

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The psychology and neuroscience of curiosity

Curiosity is a basic element of our cognition, yet its biological function, mechanisms, and neural underpinning remain poorly understood. It is nonetheless a motivator for learning, influential in decision-making, and crucial for healthy development. One factor limiting our understanding of it is the lack of a widely agreed upon delineation of what is and is not curiosity; another factor is the dearth of standardized laboratory tasks that manipulate curiosity in the lab. Despite these barriers, recent years have seen a major growth of interest in both the neuroscience and psychology of curiosity. In this Perspective, we advocate for the importance of the field, provide a selective overview of its current state, and describe tasks that are used to study curiosity and information-seeking. We propose that, rather than worry about defining curiosity, it is more helpful to consider the motivations for information-seeking behavior and to study it in its ethological context.

Curiosity is such a basic component of our natures that we are nearly oblivious to its pervasiveness in our lives. Consider, though, how much of our time we spend seeking and consuming information, whether listening to the news or music, browsing the internet, reading books or magazines, watching TV, movies, and sports, or otherwise engaging in activities not directly related to eating, reproduction, and basic survival. Our insatiable demand for information drives a much of the global economy and, on a micro-scale, motivates learning and drives patterns of foraging in animals. Its diminution is a symptom of depression, and its overexpression contributes to distractibility, a symptom of disorders such as attention-deficit/hyperactivity disorder. Curiosity is thought of as the noblest of human drives, and is just as often as it is denigrated as dangerous (as in the expression “curiosity killed the cat”). And despite its link with the most abstract human thoughts, some rudimentary forms of it can be observed even in the humble worm C. elegans.

Despite its pervasiveness, we lack even the most basic integrative theory of the basis, mechanisms, and purpose of curiosity. Nonetheless, as a psychological phenomenon, curiosity—and the desire for information more broadly—has attracted the interest of the biggest names in the history of psychology (e.g., James, 1913 ; Pavlov, 1927 ; Skinner, 1938 ). Despite this interest, only recently have psychologists and neuroscientists begun widespread and coordinated efforts to unlock its mysteries (e.g., Gottlieb et al., 2013 ; Gruber, Gelman & Ranganath, 2014 ; Kang et al., 2009 ). The present Perspective aims summarize this recent research, motivate new interested in the problem, and to tentatively propose a framework for future studies of the neuroscience and psychology of curiosity.

DEFINITION AND TAXONOMY OF CURIOSITY

One factor that has hindered the development of a formal study of curiosity is the lack of a single widely accepted definition of the term. In particular, many observers think that curiosity is a special type of the broader category of information-seeking. But carving out a formal distinction between the curiosity and information-seeking has proven difficult. As a consequence, much research that is directly relevant to the problem of curiosity does not use the term curiosity and instead focuses on what are considered to be distinct phenomena. These phenomena include, for example, play, exploration, reinforcement learning, latent learning, neophilia, and self-reported desire for information (e.g., Deci, 1975 ; Gruber, Gelman & Ranganath, 2014 ; Jirout & Klahr, 2012 ; Kang et al., 2009 ; Sutton & Barto, 1998 ; Tolman & Gleitman, 1949 ). Conversely, studies that do use the term curiosity range quite broadly in topic area. In laboratory studies, the term curiosity itself is broad enough to encompass both the desire for answers to trivia questions and the strategic deployment of gaze in free viewing ( Gottlieb et al., 2013 ).

We consider this diversity of definitions to be both characteristic of a nascent field and healthy. Here we consider some classic views with an aim towards helping us think about how to study curiosity in the future.

Classic descriptions of curiosity

Philosopher and psychologist William James (1899) called curiosity “the impulse towards better cognition,” meaning that it is the desire to understand what you know that you do not. He noted that, in children, it drives them towards objects of novel, sensational qualities—that which is “bright, vivid, startling”. This early definition of curiosity, he said, later gives way to a “higher, more intellectual form”—an impulse towards more complete scientific and philosophic knowledge. Psychologist-educators G. Stanley Hall and Theodate L. Smith (1903) pioneered some of the earliest experimental work on the development of curiosity by collecting questionnaires and child biographies from mothers on the development of interest and curiosity. From these data, they describe children’s progression through four stages of development, starting with “passive staring” as early as the second week of life, on through “curiosity proper” at around the fifth month.

The history of studies of animal curiosity is nearly as long as the history of the study of human curiosity. Ivan Pavlov, for example, wrote about the spontaneous orienting behavior in dogs to novel stimuli (which he called the “What-is-it?” reflex) as a form of curiosity ( Pavlov, 1927 ). In the mid 20th century, exploratory behavior in animals began to fascinate psychologists, in part because of the challenge of integrating it into strict behaviorist approaches (e.g. Tolman, 1948 ). Some behaviorists counted curiosity as a basic drive, effectively giving up on providing a direct cause (e.g. Pavlov, 1927 ). This stratagem proved useful even as behaviorism declined in popularity. For example, this view was held by Harry Harlow—the psychologist best known for demonstrating that infant rhesus monkeys prefer the company of a soft, surrogate mother over a bare wire mother. Harlow referred to curiosity as a basic drive in and of itself—a “manipulatory motive”—that drives organisms to engage in puzzle-solving behavior that involved no tangible reward (e.g., Harlow, Blazek, & McClearn, 1956; Harlow, Harlow, & Meyer, 1950 ; Harlow & McClearn, 1954).

Psychologist Daniel Berlyne is among the most important figures in the 20th century study of curiosity. He distinguished between the types of curiosity most commonly exhibited by human and non-humans along two dimensions: perceptual versus epistemic, and specific versus diversive ( Berlyne, 1954 ). Perceptual curiosity refers to the driving force that motivates organisms to seek out novel stimuli, which diminishes with continued exposure. It is the primary driver of exploratory behavior in non-human animals and potentially also human infants, as well as a possible driving force of human adults’ exploration. Opposite perceptual curiosity was epistemic curiosity, which Berlyne described as a drive aimed “not only at obtaining access to information-bearing stimulation, capable of dispelling uncertainties of the moment, but also at acquiring knowledge”. He described epistemic curiosity as applying predominantly to humans, thus distinguishing the curiosity of humans from that of other species ( Berlyne, 1966 ).

The second dimension of curiosity that Berlyne described informational specificity. Specific curiosity referred to desire for a particular piece of information, while diversive curiosity referred to a general desire for perceptual or cognitive stimulation (e.g., in the case of boredom). For example, monkeys robustly exhibit specific curiosity when solving mechanical puzzles, even without food or any other extrinsic incentive (e.g., Davis, Settlage, & Harlow, 1950 ; Harlow, Harlow, & Meyer, 1950 ; Harlow, 1950 ). However, rats exhibit diversive curiosity when, devoid of any explicit task, they robustly prefer to explore unfamiliar sections of a maze (e.g., Dember, 1956; Hughes, 1968 ; Kivy, Earl, & Walker, 1956). Both specific and diversive curiosity were described as species-general information-seeking behaviors.

Contemporary views of curiosity

A common contemporary view of curiosity is that it is a special form of information-seeking distinguished by the fact that it is internally motivated ( Loewenstein, 1994 ; Oudeyer & Kaplan, 2007 ). By this view, curiosity is strictly an intrinsic drive, while information-seeking refers more generally to a drive that can be either intrinsic or extrinsic. An example of an extrinsic type of information-seeking is paying a nominal price to know the outcome of a gamble before choosing it in order to make a more profitable choice. In other words, contexts in which agents seek information for immediately strategic reasons are not considered curiosity in the strict sense. While this definition is intuitively appealing (and most consistent with the use of the term curiosity in everyday speech), it is accompanied by some problems.

For example, it is often difficult for an external observer to know whether a decision-maker is motivated intrinsically or extrinsically. Animals and preverbal children, for example, cannot tell us why they do what they do, and may labor under biased theories about the structure of their environment or other unknown cognitive constraints. Consider a child choosing between a safe door and a risky one ( Butler, 1953 ). If the child chooses the risky option, should we call her curious or just risk-seeking? Or consider a rhesus monkeys who performs a color discrimination task to obtain the opportunity to visually explore their environment. Perhaps the monkey is laboring under the assumption that the view of the environment offers some actionable information, and we should put him in the same place on the curiosity spectrum as the child (whatever that place is). To make things more complicated, perhaps the monkey has decided—or even experienced selective pressure—to favor a policy of information-seeking in most contexts. It would be a challenging philosophical problem to classify this behavior as true or ersatz curiosity by the intrinsic definition.

Thus, for the moment, we favor the rough and ready formulation of curiosity as a drive state for information. Decision-makers can be thought of as wanting information for several overlapping reasons just as they want food, water, and other basic goods. This drive may be internal or external, conscious or unconscious, or slowly evolved, or some mixture of the above. We hope that future work will provide a solid taxonomy of different factors that constitute our umbrella term.

Instead of figuring out the taxonomy, we advocate a different approach: we suggest that it is helpful to think about curiosity in the context of Tinbergen’s Four Questions. Named after Dutch biologist Nikolaas Tinbergen, these questions are designed to provide four complementary scientific perspectives on any particular type of behavior ( Tinbergen, 1963 ). These questions in turn offer four vantage points from which we can describe a behavior or a broad class of behaviors, even if its boundaries are not yet fully delineated. In this spirit, our Perspective will discuss current work on curiosity as seen through the lens of Tinbergen’s Four Questions, here simplified to one word each: (1) function, (2) evolution, (3) mechanism, and (4) development.

THE FUNCTION OF CURIOSITY

Although information is intangible, it has real value to any organism with the capacity to make use of it. The benefits may accrue immediately or in the future; the delayed benefits require a learning system. Not surprisingly then, the most popular theory about the function of curiosity is to motivate learning. George Loewenstein (1994) described curiosity as “a cognitive induced deprivation that arises from the perception of a gap in knowledge and understanding.” Lowenstein’s information gap theory holds that curiosity functions like other drive states, such as hunger, which motivates eating. Building on this theory, Loewenstein suggests that a small amount of information serves as a priming dose, which greatly increases curiosity. Consumption of information is rewarding but, eventually, when enough information is consumed, satiation occurs and information serves to reduce further curiosity.

Loewenstein’s idea is supported by a recent study by Kang and colleagues ( Figure 1B , Kang et al., 2009 ). They found that curiosity about the answer to a trivia question is a U-shaped function of confidence about knowing that answer. Decision-makers were least curious when they had no clue about the answer and if they were extremely confident; they were most curious when they had some idea about the answer, but lacked confidence. In these circumstances, compulsion to know the answer was so great that they were even willing to pay for the information even though curiosity could have been be sated for free after the session. (The neural findings of this study are discussed below.)

A: Data from Kinney & Kagan (1976) . Attention to auditory stimuli shows an inverted U-shaped pattern, with infants making the most fixations to auditory stimuli estimated to be moderately discrepant from the auditory stimuli for which infants already possessed mental representations. B: Data from Kang et al. (2009) : Subjects were most curious about the answers to trivia questions for which they were moderately confident about their answers. This pattern suggests that subjects exhibited the greatest curiosity for information that was partially—but not fully—encoded.

Kang and colleagues also found that curiosity enhances learning, consistent with the theory that the primary function of curiosity is to facilitate learning. This idea also motivated O’Keefe and Nadel’s thinking about the factors that promote spatial learning in rodents ( O’Keefe and Nadel, 1978 ). This idea is also popular in the education literature (e.g., Day, 1971 ; Engel, 2011 , 2015 ; Gray, 2013 ), and has been for quite some time, as evidenced by attempts by education researchers to develop scales to quantify children’s degree of curiosity, both generally and in specific learning materials (e.g., Harty & Beall, 1984 ; Jirout & Klahr, 2012 ; Pelz, Yung, & Kidd, 2015 ; Penney & McCann, 1964 ). One potential benefit of such research would be to improve education. More recently, the role of curiosity in enhancing learning is gaining adherents in cognitive science (see Gureckis & Markant, 2012 , for a review). The idea is that allowing a learner to indulge their curiosity allows them to focus their effort on useful information that they do not yet possess. Further, there is a growing body of evidence suggesting that curiosity enables even infant learners to play an active role in optimizing their learning experiences ( Oudeyer & Smith, in press ). This work suggests that allowing a learner to expose the information they require themselves—which would be inaccessible via passive observation—may further benefit the learner by enhancing the encoding and retention of the new information.

THE EVOLUTION OF CURIOSITY

Information allows for better choices, more efficient search, more sophisticated comparisons, and better identification of conspecifics. Acquiring information, of course, is the primary evolutionary purpose of the sense organs, and has been a major driver of evolution for hundreds of millions of years. Complex organisms actively control their sense organs to maximize intake of information. For example, we choose our visual fixations strategically to learn about the things that are important to us in the context ( Yarbus 1956 ; Gottlieb et al., 2012 , 2013 , 2014 ). Given its important role, it is not surprising that our visual search is highly efficient. It is nearly optimal when compared to an ‘ideal searcher’ that uses precise statistics of the visual scene to maximize search efficiency ( Najemnik & Geisler 2005 ). Moreover, the strong base of information we have about the visual system makes it an appealing target for studies of curiosity ( Gottlieb et al., 2013 , 2014 ). Just as eye movements can be highly informative, our overt behaviors, including choice, can provide evidence for and against specific theories about how we seek information, which can in turn help us understand the root causes of evolution. In this section we discuss the spectrum of basic information-seeking behaviors.

Elementary information-seeking

Even very simple organisms trade off information for rewards. While their information-seeking behavior is not typically categorized as curiosity, the simplicity of their neural systems makes them ideally suited for studies that may provide its foundation. For example, C. elegans is a roundworm whose nervous system contains 302 neurons and that actively forages for food, mostly bacteria. When placed on a new patch (such as a petri dish in a lab), it first explores locally (for about 15 minutes), then abruptly adjusts strategies and makes large, directed movements in a new direction ( Calhoun, Chalasani, & Sharpee, 2014 ). This search strategy is more sophisticated and beneficial than simply moving towards food scents (or guesses about where food may be); instead, it provides better long-term payoff because it provides information as well. It maximizes a conjoint variable that includes both expected reward and information about the reward. This behavior, while computationally difficult, is not too difficult for worms. A small network of three neurons can plausibly implement it. Other organisms that have simple information-seeking behavior include crabs ( Zeil, 1998 ), bees ( Gould 1986 ; Dyer, 1991) ants ( Wehner et al., 2002 ), and moths (Vergasolla et al., 2007). Information gained from such organisms can help us to understand how simple networks can perform information-seeking.

Information-tradeoff tasks

In primates (including humans), one convenient way to study information-seeking is the k-arm bandit task ( Gittins & Jones, 1974 , Figure 2 ). In this task, decision-makers are faced with a series of choices between stochastic rewards ( Whittle, 1988 ). The optimal strategy requires adjudication between exploration (sampling to improve knowledge, and thus future choices) and exploitation (choosing known best options). Sampling typically gives lower immediate payoff but can provide information that improves choices in the future, leading to greater overall performance. Humans and monkeys can do quite well at this task ( Daw et al., 2006 ; Pearson et al., 2009 ). One particular advantage of such tasks is that they allow for sophisticated formal models of information tradeoffs; this level of rigor is often absent in conventional curiosity studies ( Averbeck, 2015 ).

A. In a four-arm restless bandit task, subjects choose on each trial from one of four targets. B. The value associated with each option changes in value (uncued) stochastically on each trial. Consequently, when the subject has identified the best target, there is a benefit to occasionally interspersing trials where an alternative is chosen (exploration) into the more common pattern of choosing the known best option (exploitation). For example, the subject may choose option A (red color) for several trials but would not know that blue (B) will soon overtake A in value without occasionally exploring other options. C. In this task, neurons in posterior cingulate cortex show higher tonic firing on explore trials than on exploit trials.

Daw and colleagues showed that humans performing a 4-arm bandit task choose options probabilistically based on expected values of the options (a “softmax” policy, Daw, 2006 ). This probabilistic element causes them to occasionally explore other possibilities, leading them to better overall choices. The frontopolar cortex and intraparietal sulcus are significantly more active during exploration, whereas striatum and ventromedial prefrontal cortex (vmPFC) are more active during exploitative choices ( Daw et al., 2006 ). These are canonical reward areas, thus these results link curiosity to the reward system (a theme that we will return to). They proposed that the activation of higher-level prefrontal regions during exploration indicates a control mechanism overriding the exploitative tendency.

In a similar task, neurons in the posterior cingulate cortex (PCC) have greater tonic firing rates on exploratory trials than on exploitative trials (even after controlling for reward expectation, Pearson et al., 2009 , Figure 2 ). Firing rates also predict adjustments from exploitative to exploratory strategy and vice versa. These results highlight the contribution of the PCC, a critical yet mostly mysterious hub of the reward system, in both the transition to exploration and in its maintenance ( Pearson et al., 2011 ). PCC is linked to both reward and regulation of learning, thus underscoring the possible linkage between these processes and curiosity ( Heilbronner & Platt, 2013 ; Hayden et al., 2008 ). PCC responses are also driven by the salience of an option, a factor that relates directly to its ability to motivate interest, rather than reward value per se ( Heilbronner et al., 2013 ). The precuneus, a region adjacent to, and closely interconnected with, the PCC, was also associated with curiosity in one study: it is enlarged in capuchins that are particularly curious ( Phillips, Subiaul, & Sherwood, 2012 ).

Above and beyond the strategic benefit of exploration, we have a tendency to seek out new and unfamiliar options, which may offer more information than familiar ones. The bandit task can be modified to measure this tendency ( Wittmann et al., 2008 ). In one case, subjects chose between four different images on each trial; the identity of the images was arbitrary and served to distinguish the options. The value of each image was stable but stochastic, so sampling was required to learn its value. Some images were familiar, others were novel; however, image novelty had no special meaning in the context of the task. Nonetheless, subjects were more likely to choose novel images (that is, they motivated exploratory choices). This bias towards choosing novel images was mathematically expressible as a novelty bonus ( Gittins & Jones, 1974 ). Interestingly, this novelty bonus increased the expected reward for the novel images (as measured by an increase in reward prediction error (RPE) signal in ventral striatum). These results support the idea that novelty-seeking reflects an injection into choice of motivation provided by the brain's reward systems.

Bandit tasks can also be used to measure the effect of strategic context on information-seeking. For example, if the information relates to future events that may not happen, it ought to be discounted. Thus, the horizon (the number of trials available to search the environment before it changes dramatically) matters ( Wilson et al., 2014 ; see also Averbeck, 2015 ). Humans can adjust appropriately to changes in horizon: with longer horizons, subjects were more likely to choose an exploratory strategy than an exploitative one. Together, these results highlight the power and flexibility of bandit tasks as a way of studying information-seeking in a rigorous and highly quantifiable way.

Temporal resolution of uncertainty tasks

What about when the drive for information has no clear benefit? One convenient way to study this is to take advantage of the preference for immediate information about the outcome of a risky choice ( Kreps & Porteus, 1978 ; Lieberman et al., 1997 ; Luhmann et al., 2008 ; Prokasy, 1956 ; Wyckoff, 1952 ). In a temporal resolution of uncertainty task, monkeys choose between two gambles with identical probabilities (50/50) and identical payoffs (a large or a small squirt of juice delayed by 2.25 seconds, Bromberg-Martin & Hikosaka, 2009 ). The only difference between the two gambles is that one offers immediate information about win vs. loss (that is, immediate temporal resolution of uncertainty) while in the other the information is delayed. The reward is delayed in both cases, so preference for sooner reward would not affect choice. Despite the brevity of the delay, monkeys reliably choose the option with the immediate resolution of uncertainty (the informative option, Bromberg-Martin & Hikosaka, 2009 , 2011 ; Blanchard et al., 2015 ). This preference for earlier temporal resolution of uncertainty is not strategic because the information cannot improve choices. Thus, these tasks satisfy a stricter notion of curiosity.

We modified this task to quantify the value of information by titrating the values of the rewards ( Blanchard, Hayden, & Bromberg-Martin, 2015 , Figure 3 ). In the curiosity tradeoff task , by determining the indifference point between informative and uninformative options, we found that the value of information about a reward is about 25% of the value of the reward itself—surprisingly high. This finding indicates that monkeys choose information even when it has a measurable cost. In addition, the value of information increases with the stakes. In other words, monkeys will pay more for information about a high stakes gamble than for information about a low stakes gamble. These results are similar to some recent findings observed in pigeons ( Stagner and Zentall, 2010 ). Pigeons will choose a risky option that provides an average of 2 pellets over one that provides an average of 3 pellets as long as the one that provides 2 also provides what they call a discriminative cue—meaning a cue that reliably predicted whether a reward would come (see also Gipson et al., 2009 ).

A. In the curiosity tradeoff task, subjects choose between two gambles that vary in informativeness (cyan vs. magenta) and gamble stakes (the size of the white inset bar). On each trial, two gambles appear in sequence on a computer screen (indicated by a black rectangle); when both options appear, subjects shift gaze to one to select it. Then, following 2.25 seconds, they receive a juice reward. Following choice of the informative option, they receive a cue telling them whether they get the reward (50% chance); following choice of the uninformative option, subjects get not valid information. B. Two subjects both showed a preference for informative options (indicated by a left shift of the psychometric curve) over uninformative ones, despite the fact that this information provided no strategic benefit. C. In this task, OFC does not integrate value due to information (vertical axis) with value due to reward size (horizontal axis).

Zentall and colleagues did make the link between their risk-seeking pigeons and human gamblers ( Zentall & Stagner, 2011 ). This link is potentially important: curiosity is often mooted as an explanation for risk-seeking behavior ( Bromberg-Martin & Hikosaka, 2009 ). Rhesus monkeys, for example, are often risk-seeking in laboratory tasks ( Blanchard & Hayden, 2014 ; Heilbronner & Hayden 2013 ; Monosov & Hikosaka, 2012 ; O’Neill & Schultz, 2010 ; So & Stuphorn, 2012 ; Strait et al., 2014 and 2015 ). Risky choices provide information about the status of uncertain stimuli in the world, so animals may naturally seek such information. We trained monkeys to perform a gambling task in which both the location and value of a preferred high-variance option are uncertain; knowing the location of that option allowed the monkeys to perform better in the future, but knowing its value was irrelevant (Hayden, Pearson, & Platt, 2009). We found that, following choices of the low variance (and thus non-preferred) option, when it was too late to change anything, monkeys will spontaneously shift gaze to its position, suggesting they want to know information about it.

These findings demonstrate the power of the desire for temporal resolution of uncertainty as a motivator for choice, and thus as a potential tool for the study of information-seeking. This phenomenon is particularly useful because the information sought is demonstrably useless, making it a good potential model for more basic and fundamental (i.e. non-strategic) forms of information seeking than the bandit task. It is also, like the bandit task, one that works well in animals (meaning behavior is reliable and stable across large numbers of trials), so it has potential utility in circuit-level studies.

THE (NEURAL) MECHANISMS OF CURIOSITY

Tinbergen’s third question is about the proximate mechanism of a behavior. The mechanism of any behavior is in device that produces it—the brain.

As noted above, Kang and colleagues used a curiosity induction task to test Loewenstein’s hypothesis that curiosity reflects an information gap ( Loewenstein, 1994 ). Human subjects read trivia questions and rated their feelings of curiosity while undergoing fMRI ( Kang et al., 2009 ). Brain activity in the caudate nucleus and inferior frontal gyrus (IFG) was associated with self-reported curiosity. These structures are activated by anticipation of many types of rewards, so these results suggest that curiosity elicits an anticipation of a reward state—consistent with Loewenstein’s theory ( Delgado et al., 2000 , 2003 , 2008 ; De Quervain et al., 2004 ; Fehr & Camerer, 2007 ; King-Casas et al., 2005 ; Rilling et al., 2002 ). Puzzlingly, the nucleus accumbens, which is one of the most reliably activated structures for reward anticipation, was not activated ( Knutson et al., 2001 ). When the answer was revealed, activations generally were found in structures associated with learning and memory, such as parahippocampal gyrus and hippocampus. Again this is a bit puzzling, because classic structures that respond to receipt of reward were not particularly activated. In any case, the learning effect was particularly strong on trials on which subjects’ guesses were incorrect—the trials on which learning was greatest.

Jepma et al. (2012) showed subjects blurry photos with ambiguous contents that piqued their curiosity; curiosity activated the anterior cingulate cortex and anterior insula - regions sensitive to aversive conditions (but to many other things too); resolution of curiosity activated striatal reward circuits. Like Kang and colleagues, they found that resolution of curiosity activated learning structures and also drove learning. However, the differences between the two studies were larger than the similarities. In the Jepma study, curiosity is a fundamentally aversive state, while in the Kang study it is pleasurable. Specifically, curiosity is seen as a lack of something wanted (information) and thus unpleasant, and this unpleasantness motivates information, which will alleviate it.

Gruber and colleagues (2014) measured brain activity while subjects answered trivia questions and rated their curiosity for each question. They were also shown interleaved photographs of neutral, unknown faces which acted as a probe for learning. When tested later, subjects recalled the faces shown in high curiosity trials better than faces shown on low curiosity trials. Thus, the curiosity state led to better learning, even for the things people weren’t curious about. Curiosity drove activity in both midbrain (implying the dopaminergic regions) and nucleus accumbens; memory was correlated with midbrain and hippocampal activity. These results suggest that, although curiosity reflects intrinsic motivation, it is mediated by the same mechanisms as extrinsically motivated rewards.

Single unit recordings from the temporal resolution of uncertainty task further support this overlap. In this task, dopamine neuron activity (DA) is enhanced by the prospect of both a possible reward and early information. Dopamine neurons provide a key learning and motivation signal that is critical for many types of reward-related cognition ( Redgrave & Gurney, 2006 ; Bromberg-Martin, Matsumoto & Hikosaka, 2010 ; Schultz & Dickinson, 2000 ). The phasic dopamine response is thought to serve as a general reward prediction error—indicating rewards or reward prospects of any type that are greater than expected ( Schultz et al., 1997 ). Information is not a primary reward (as juice or water would be in this context), but is a more indirect kind of reward. The fact that dopamine neurons signal both primary and informational reward suggests that the dopamine response reflects an integration of multiple reward components to generate an abstract reward response. This finding further suggests that dopamine responses not associated with rewards—such as surprising and aversive events—may reflect the value that information provides ( Horvitz, 2000 ; Matsumoto & Hikosaka, 2009 ; Redgrave & Gurney, 2006 ;).

These results suggest that, to subcortical reward structures, informational value is treated the same as any other valued good. To further test this idea, the authors asked whether midbrain neurons encode information prediction error ( Bromberg-Martin & Hikosaka, 2011 ). While the positive RPE is carried by DA neurons, its inverse, the negative RPE, is carried by neurons in the lateral habenula (LHb). They made use of this fact in task in which there was an option to choose a stochastically informative gamble, meaning it would provide (50/50 chance) valid or invalid information about the upcoming reward. They found that neurons in the LHb encode the unexpected occurrence of information and the unexpected denial of information—just as they do with basic rewards (water and juice).

Where does the domain-general curiosity signal come from? It has recently been proposed that the dopamine reward signal is constructed out of input signals originating in the orbitofrontal cortex (OFC), which in turn receives input from basic sensory and association structures ( Öngür & Price, 2000 ; Schoenbaum et al., 2011 ; Takahashi et al., 2011 ; Rushworth et al., 2011 ). If OFC is an input to the evaluation system, then it should carry information about the reward value of curiosity but may not carry a single general reward signal. In other words, OFC may serve as a kind of workshop that represents elements of reward that can guide choice, but not a single domain general value signal. In the curiosity tradeoff task (see above and Figure 3 ), OFC neurons encode both the stakes of the gamble, and also the information value of the options ( Blanchard, Hayden, & Bromberg-Martin, 2015 ). But it doesn't integrate them into a single value signal. Thus, at least within this one task, curiosity is computed separately from other factors that influence value and combined at a specific point (or points) in the pathway between the OFC and the DA nuclei.

THE DEVELOPMENT OF CURIOSITY

The fourth of Tinbergen’s questions concerns development of a behavior. Curiosity has been central to the study of infant and child attention and learning, and a major focus in research on early education for decades (e.g., Berlyne, 1978 ; Dember & Earl, 1957 ; Kinney & Kagan, 1976 ; Sokolov, 1960). The world of infants is full of potential sources for learning, but they possess limited information-processing resources. Thus, infants must solve what is known as the sampling problem : their attentional mechanisms must select a subset of material from everything available in their environments in order to make learning tractable. Furthermore, they must sample in a way that ensures that learning is efficient, which is tricky considering the fact that what material is most useful changes as the infant gains more knowledge.

Infants enter the world with some simple, low-level heuristics for guiding their attention towards certain informative features of the world. Haith (1980) argued that these organizing principles for visual behavior are fundamentally stimulus-driven. For example, infants’ gaze is pulled towards areas of high contrast, which is useful for detecting objects and perceiving their shapes (e.g., Salapatek & Kessen, 1966 ), and motion onset, which is useful for detecting animacy (e.g., Aslin & Shea, 1990 ). Infants also have an innate bias to orient towards faces (e.g., Farroni et al., 2005 ; Johnson et al., 1991 ), which relay both social information and cues that guide language learning (e.g., Baldwin, 1993 ). While this desire for information is surely intrinsic, whether or not these low-level mechanisms that guide infants’ early attentional behavior could be explained with curiosity depends on the chosen definition. If curiosity requires an explicit mental representation of the need for new information, these low-level heuristics do not qualify. However, a broader definition, which sees curiosity as any mechanism that guides an organism towards new information, regardless of mental substrate, they certainly do. Regardless of how you classify them, these attentional biases get the infant started down the road of knowledge acquisition.

Externally driven motivation is not sufficient. Learners also must adapt to changing needs as they build up and modify their mental representations of the world. Many early researchers posited that novelty was the primary stimulus feature of relevance for infants (e.g., Sokolov, 1960). Infants prefer novel stimuli in many paradigms, such as those used by Fantz (1964), the high-amplitude sucking procedure (Siqueland & DeLucia, 1969), and head-turn preference procedure ( Kemler Nelson et al., 1995 ). Novelty preference is also seen in habituation procedures, in which infants’ attention to a recurring stimulus decreases with lengthened exposure. Novelty theories, however, cannot account for infants’ attested familiarity preferences, such as their affinity for their native languages and familiar faces (e.g., Bushnell et al., 1989 ; DeCasper & Spence, 1986 ).

Later theories sought to unify infants’ novelty and familiarity preferences by explaining them in terms of infants’ changing knowledge states. In other words, an infant’s interest in a particular stimulus was theorized to be determined by that infant’s particular mental status. For example, as infants attempt to encode various features of a visual stimulus, the efficiency or depth of this encoding process determines their subsequent preferences. Infants were theorized to exhibit a preference for stimuli that were partially—but not fully—encoded into memory (e.g., Dember & Earl, 1957 ; Hunter & Ames, 1988 ; Kinney & Kagan, 1976 ; Roder, Bushnell, & Sasseville, 2000 ; Rose et al., 1982 ; Wagner, S.H., & Sakovitsjkk, 1986 ). This idea recalls the fact that we are curious for things that we are moderately certain of ( Kang et al., 2009 ).

Among these theories was Kinney and Kagan’s moderate discrepancy hypothesis , which suggested that infants preferentially attend to stimuli that were “optimally discrepant,” meaning those that were just the right amount of distinguishable from mental representations that the infant already possessed ( Kinney & Kagan, 1976 ). Under Dember and Early’s theory of choice/preference , learners seek stimuli that match their preferred level of complexity, which increases over time as they build up mental representations and acquire more knowledge ( Dember & Earl, 1957 ). Berlyne, similarly, noted that complexity-driven preferences could represent an optimal strategy for learning ( Berlyne, 1960 ). Such processing-based theories of curiosity predict that learners will exhibit a U-shaped pattern of preference for stimulus complexity, where complexity is defined in terms of the learner’s current set of mental representations. The theories predict that learners will preferentially select stimuli of an intermediate level of complexity—material that is neither overly simple (already encoded into memory) nor overly complex (too disparate from existing representations already encoded into memory).

Recent infant research supports these accounts (e.g., Kidd, Piantadosi, & Aslin, 2012 , 2014 , Figure 4 ). We showed 7- and 8-month-old infants visual event sequences of varying complexity, as measured by an idealized learning model, and measured points at which infants’ attention drifted (as indicate by looks away from the display). We found that infants’ probability of looking away was greatest to events of either very low information content (highly predictable) or very high information content (highly surprising). This attentional strategy holds in multiple types of visual displays ( Kidd, Piantadosi, & Aslin, 2012 ), for auditory stimuli ( Kidd, Piantadosi, & Aslin, 2014 ), and even within individual infants ( Piantadosi, Kidd, & Aslin, 2014 ). These results suggest that infants implicitly decide to direct attention in order to maintain intermediate rates of information absorption. This attentional strategy likely prevents them from wasting cognitive resources on overly predictable or overly complex events, thus helping to maximize their learning potential.

A: Example display from Kidd, Piantadosi, & Aslin (2012) . Each display featured 3 unique boxes hiding 3 unique objects that revealed themselves one at a time according to one of 32 sequences of varying complexity. The sequence continued until the infant looked away for 1-second. B: Infant look-away data plotted by complexity (information content) as estimated by an ideal observer model over the transitional probabilities. The U-shaped pattern indicates that infants were least likely to look away at events with intermediate information content. Infants probability of looking away was greatest to events of either very low information content (highly predictable) or very high information content (highly surprising), consistent with an attentional strategy that aims to maintain intermediate rates of information absorption.

Related findings show that children structure their play in a way that reduces uncertainty and allows them to discover causal structures in the world (e.g., Schulz & Bonawitz, 2007 ). This work is in line with earlier theories of Jean Piaget (1930) that asserted that the purpose of curiosity and play was to “construct knowledge” through interactions with the world. If curiosity aims to reduce uncertainty in the world, we would expect learners to exhibit increased curiosity to stimuli in the world that they do not understand. In fact, this is a behavior that is well attested in recent developmental psychology studies, such as work by Bonawitz and colleagues ( Bonawitz, van Schijndel, Friel, & Schulz, 2012 ) that demonstrates that children prefer to play with toys that violate their expectations. Children also exhibit increased curiosity outside of pedagogical contexts, in the absence of explicitly given explanations ( Bonawitz et al., 2011 ). In an experiment in which Bonawitz and colleagues gave children a novel toy to explore, either prefaced or not with partial instruction of how the toy works, children played for longer and discovered more of the toys’ functions in the non-pedagogical conditions.

In line with the idea that the function of curiosity is to reduce uncertainty, children exhibit increased interest in situations with high degrees of uncertainty, such as preferentially playing with toys whose underlying mechanisms are not yet understood. Perhaps even more impressively, Schulz and Bonawitz (2007) found that children preferentially engaged with toys that allowed them to deconfound potential causal variables underlying toys’ inner workings. In these experiments, Schulz and Bonawitz had children play with toys consisting of boxes and levers. In both the confounded and unconfounded conditions, the researcher would help a child play with a red box with two levers. In the confounded condition, the researcher and the child each pressed down on a lever at the same time and, in response, two small puppets (a chick and a pom-pom) popped out of the top of the red box ( Figure 5 ). The puppets’ location—dead center—was not informative about which of the two levers caused each one to rise. In the unconfounded conditions, the researcher and child took turns pressing down on their respective levers one at a time or the researcher demonstrated each lever independently; thus, in both cases, it was clear which lever controlled each puppet. After this demonstration, the researcher uncovered a second, yellow box. After the demonstration and yellow-box reveal, children were left alone and instructed to play in the researcher’s absence for 60 seconds. During this period, children in the confounded condition preferentially explored the demonstrated red box over the novel yellow one.

Experimental stimuli from Schulz & Bonawitz (2007) . When both levers were pressed simultaneously, two puppets (a straw pompom and a chick) emerged from the center of the box. In this confounded case, the evidence was not informative about which of the two levers caused each puppet to rise. In the unconfounded conditions, one lever was pressed at a time, making it clear which lever caused each puppet to rise. During a free-play period following the toy's demonstration, children played more with the toy when the demonstrated evidence was confounded.

The idea that children structure their play in a way that is sensitive to information gain is further bolstered by a recent study by Cook and colleagues ( Cook, Goodman, & Schulz, 2011 ). They manipulated the ambiguity of various causal variables for a toy box that played music when certain—but not all—beads were place on top of it. A researcher initially demonstrated how the box worked by placing a pair of connected beads on top, thereby making it ambiguous which of the two beads was causally responsible for the music playing. Children were effective at both selecting and designing informative interventions to figure out the underlying causal structure when it was unclear from the demonstration. When given ambiguous evidence, children tested individual beads when possible and—even more impressively—when bead pair was permanently stuck connected together, children held them such that only one side was touching the box in order to isolate the effect of that particular bead on the box.

This hypothesis-testing behavior is now widely attested in the developmental psychology literature. Children appear to structure their play in order to deconfound variables when causal mechanisms at play in the world are unclear (e.g., Denison et al., 2013 ; Gopnik, Meltzoff, & Kuhl, 1999 ; Gweon et al., 2014 ; Schulz, Gopnik, & Glymour, 2007 ; van Schijndel et al., 2015 ), and also make efficient use of information that they encounter in the world to learn correct causal structures (e.g., Gopnik & Schulz, 2007 ; Gopnik at al., 2001 ). These findings are important because they highlight the fact that children’s curiosity appears specifically well suited to teaching them about the causal structure of the world. Thus, these strategic information-seeking behaviors in young children are far more sophisticated than the simple attentional heuristics that characterize early infant attention.

Curiosity has long fascinated laymen and scholars alike, but remains poorly understood as a psychological phenomenon. We argue that one factor impeding our understanding has been too much focus on delineating what is and is not curiosity. Another has been too much emphasis on taxonomy. These divide-then-conquer approaches are premature because they do not rely on empirical data. Perhaps the plethora of definitions and schemes attests more to differences in scholars’ intuitions than to differences in their data. Thus we recommend that the definition stage follow a relatively solid characterization of curiosity, defined as broadly as possible. For this reason, we are reluctant to commit to a strict definition now. This approach has risks, of course. It means that there will be a variety of studies using similar terms to describe different phenomena, and different terms to describe the same phenomena, which can be confusing. Nonetheless, we think the benefits of open-mindedness outweigh the costs.

Broadening the scope of inquiry has several advantages. First, it allows us to study information-seeking in non-humans, including monkeys, rats, and even roundworms. Animal techniques allow for a granular view of mechanism, allows a greater range of manipulations, and allows cross-species comparisons. Second, it allows us to temporarily put aside speculation about decision-makers’ motivations and focus on other questions. Third, by refusing to isolate curiosity from other cognitive processes, we can make bridges with other phenomena, especially reward and learning. Finally, it lets us take advantage of powerful new tasks invented in the past decade for studying the cognitive neuroscience of information-seeking.

Tinbergen’s Four Questions are designed to provide a way to explain the causes of any behavior. This approach already provides a convenient framework for considering the knowledge we have so far. In the domain of function , it seems clear that curiosity serves to motivate acquisition of knowledge and learning. In the domain of evolution , it seems that curiosity can tentatively be said to improve performance, yielding fitness benefits to organisms with it, and is likely to be an evolved trait. In the domain of mechanisms , it seems that the drive for information augments internal representations of value, thus biasing decision-makers towards informative options and actions. It also seems that curiosity activates learning systems in the brain. In the domain of development , we can infer that curiosity is critical for learning and that it reflects both external features and internal representations of own knowledge.

In the future we hope to see answers to some of these questions:

• In what ways does curiosity resemble other basic drive states? How does it differ? To what extent is curiosity fundamentally different from drives like hunger and thirst?
• What is the most useful taxonomy of curiosity? How well does Berlyne’s categorization hold up? What factors unite distinct forms of curiosity?
• How is curiosity controlled? What factors govern curiosity, and how does the brain integrate these factors into decision-making to produce decisions? To what extent is curiosity context-dependent?
• To what extent does curiosity in nematodes overlap (if at all) with curiosity in children? How useful is it to think of curiosity as being a single construct across a broad range of taxa?
• Does our continuing curiosity in adulthood serve a purpose or is it vestigial? Does continued curiosity serve to maintain cognitive abilities throughout adulthood?
• What is the link between curiosity and learning?
• Why and how is curiosity affected by diseases like depression and ADHD? Can sensitive measures of curiosity be used to predict and measure cognitive decline, senility, and Alzheimers’ Disease?

We can already sketch out rough guesses about how some of these questions will be answered. For example, we anticipate that, although useful in the past, Berlyne’s categories will be replaced with other, differently-formulated subtypes, and that these newer ones will be motivated by new neural and developmental data. We suspect that curiosity serves a similar purpose in adulthood as it does in childhood, albeit in perhaps a more refined way. Even as adults we need to continue to adjust our understanding of the world. Finally, we are optimistic that scientists will eventually uncover a consistent set of principles that characterize curiosity across a wide range of taxa.

Acknowledgments

This research was supported by a R01 (DA038615) to BYH. We thank Sarah Heilbronner, Steve Piantadosi, Shraddha Shah, Maya Wang, Habiba Azab, and Maddie Pelz for helpful comments.

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Curiosity: The neglected trait that drives success

Exploring your curiosity can be incredibly good for your mind, with benefits for learning, creativity and even job enjoyment.

On 7 January 1918 at New York’s Hippodrome, the incredible illusionist Harry Houdini unveiled one of his most famous tricks – the vanishing elephant – in front of thousands of spectators.

The beast in question, Jennie,  reportedly weighed 10,000 pounds (4,536kg). She raised her trunk in greeting, before a stagehand led her into a huge cabinet and closed the doors behind them. After a dramatic drum roll, the doors reopened – and the cabinet was now empty. To the thousands of spectators, it seemed that she had vanished into thin air. 

How could Houdini have managed to hide such an enormous animal? No one at the time could provide  a definitive explanation of what had happened , though there is one predominant theory.

I’ll tell you what that is before the end of the article, but you might want to avoid the temptation of skipping straight to that section – since a wealth of scientific research shows that allowing your curiosity to be piqued in this way can be incredibly good for your mind. Research shows that not knowing the answer to an intriguing puzzle can, for example, increase your creativity on subsequent tasks, as well as priming your brain for learning. Curiosity in the workplace, meanwhile, increases engagement and enjoyment of your job and reduces your risk of burnout.

It is little wonder, then, that scientists have now been looking for ways to cultivate more curiosity in our lives – and even simple interventions could reap enormous benefits. 

Memory boosts

Given that the dictionary definition of curiosity is “the desire to know something”, it may be of little surprise that much research has concerned its benefits for education. Using questionnaires that ask people how much they desire new information and consider novel problems, various studies have shown that people’s curiosity can  predict their academic success , independently of IQ.

The most recent research suggests that the benefits for learning may arise from changes at a neurological level. When we feel curious about a subject, the facts that we are studying become more deeply encoded, and more accessible when they are later needed.

Consider  a study at the University of California at Davis in 2014 . The researchers first asked each participant to rate their curiosity about learning the answers to a series of questions, such as “Who was the president of the United States when Uncle Sam first got his beard?” or “What does the term ‘dinosaur’ actually mean?” The participants then lay in an fMRI brain scanner while the same questions were presented, followed shortly after by the answers. The participants were then tested on their recollection of the facts an hour later. 

The effects of curiosity on later recall were striking. When the participants were highly curious about a fact, they were 30% more likely to recall it. And this seemed to correspond to heightened activity in areas of the midbrain that release the neurotransmitter dopamine. Dopamine is normally associated with reward, but animal studies suggest that it can also enhance the formation of new neural connections . It looked as if the feeling of curiosity was helping prepare the brain to absorb the new and important information, and this then resulted in a more stable memory.

Intriguingly, the researchers found that the dopamine hit, arising from initial curiosity, could even enhance the memory of incidental information that had no direct relevance to the primary question. To demonstrate this, they had presented random faces alongside the answers to the questions and, an hour later, checked whether the participants still recognised the faces. The analyses showed that the participants were far more likely to remember the face if it had accompanied one of the more interesting trivia questions that had spiked their curiosity. 

This additional, and unexpected, memory boost could be extremely useful whenever we’re trying to learn something new and complicated. We’re unlikely, after all, to find every single element of our studies fascinating. But if we can cultivate some curiosity about at least some of the facts, we may find that the rest of the material also sticks far more easily.

Curiosity can also increase our patience. A  recent, currently unpublished, study  by Abigail Hsiung, a PhD student at Duke University in North Carolina, showed that heightened curiosity meant that people were more willing to wait to find out the solution to a puzzle. Less curious people, in contrast, were more impatient to get through the task quickly, and so they asked to jump straight to the answers. “High curiosity meant that people wanted to have that moment of realisation and discovery on their own,” says Hsiung.

In education, greater patience and prolonged engagement are likely to lead to extended research, deeper learning and understanding, particularly for complex topics – which may also help to explain why curiosity is such a strong predictor of academic success.  

Idea linking  

Similarly profound effects can be found in studies of creative problem solving, with signs that curiosity helps people to build more exciting and original ideas.

Evidence for this process comes through the story of the vanishing elephant. In  a series of experiments , researchers exposed participants to one of two versions of the tale. Half read a version that amped up the element of mystery – the fact that other illusionists have struggled to work out how the trick worked. These participants were then asked to describe how they thought Houdini had hidden Jennie. Whatever their answer, they were told that they were “close but not completely right”, which left a gap in their knowledge, creating further uncertainty and intrigue. 

The rest of the group read a less intriguing description of Houdini's trick that seemed to suggest that the workings of the illusion were already well understood, with a giveaway clue that Houdini had hidden the elephant behind a curtain in the cabinet. (This is, indeed, the favoured  theory .) 

Afterwards, the participants rated how curious they were to know more about Houdini’s trick. They were given a few minutes to design their own magic tricks, which were later rated by independent judges.

The researchers found that the participants who had read the first, more mysterious version of the story were indeed more curious, and this resulted in significantly more innovation during the subsequent magic-trick task.

This seemed to come through a process called “idea linking”, in which participants would continually build on their initial thoughts through an iterative process. For example, a participant from this group could start out thinking about a way to make a whale disappear, explains co-author Spencer Harrison a professor of managerial and organisational cognition at INSEAD, Fontainebleau, France. They might then go on to think about the possibility of making a dinosaur skeleton disappear from a museum, “And then the [skeleton] is not disappearing, it’s dancing,” he says. Step after step, they were modifying and expanding their answers, “pushing the idea into a realm that feels it’s wholly new”, adds Harrison.

Those who had seen the less intriguing version of the Houdini story, in contrast, tended to just settle on the first idea that popped into their head, which was generally less ambitious or interesting.

Engagement and wellbeing

The benefits of curiosity do not end here. In his recent book The Art of Insubordination, Todd Kashdan, a professor of psychology at George Mason University in Fairfax, Virginia, US, points out that greater curiosity can also make people  more open to hearing others’ opinions, even if they differ from their own . That’s essential if we want to have productive disagreements and avoid issues like confirmation bias and groupthink.

Kashdan’s own research has demonstrated that  curiosity brings comprehensive benefits to the workplace . The study included more than 800 participants from the USA and Germany from a range of industries, who rated a series of statements about their experiences of curiosity in their day-to-day lives, such as: 

• I get excited thinking about experimenting with different ideas 
• I do not shy away from the unknown or unfamiliar even if it seems scary 

The participants also completed questionnaires about their job satisfaction and engagement, their social relationships with their colleagues, their feelings of burnout and their use of innovation at work. In each of these measures, the more curious participants tended to report better experiences, compared with the less curious participants.

Cultivating curiosity

According to Kashdan and Harrison, many organisations could encourage greater curiosity in the workforce with a few changes to their corporate culture. Managers might consider giving their employees a little more independence, for example, with various studies showing that a sense of autonomy increases curiosity. Even if there are only a limited number of options available,  a project is more likely to stimulate someone’s interest if they have selected it voluntarily , rather than having the choice imposed on them by someone else.

Where relevant, employers might also encourage workers to look beyond the narrow confines of their primary expertise. “We really need to get rid of this notion of ‘staying in your lane’,” suggests Kashdan. The increased interest in the new domain could then spill over into their own area, he says, energising their thinking and allowing them to spot new connections and lines of enquiry.

On an individual level, there is also some evidence that you can actively train your curiosity. The first step is to make it personally relevant;  research by Rachit Dubey, a cognitive scientist at Princeton University , has shown that reminding people of the usefulness of the new knowledge can boost their curiosity when it’s lagging. So, try to keep your ultimate goals in your focus, if feelings of frustration or confusion have caused you to forget why you were interested in the first place.

For similar reasons, you might compile a list of questions that you would like to answer in the days or weeks ahead. Studies show that this simple step of identifying the current holes in your knowledge  naturally sparks more curiosity , and subsequent engagement, in the relevant material. Your queries do not need to be profound: there is no such thing as a stupid question as long as your query prompts a desire to know more.

The physicist Richard Feynman may have put it best when he said: “Nearly everything is really interesting if you go into it deeply enough.” And with the proven benefits for your learning, creativity and general well-being, you may be pleasantly surprised where this new-found curiosity eventually leads you.  

David Robson is a science writer and author of  The Expectation Effect: How Your Mindset Can Transform Your Life , published by Canongate (UK) and Henry Holt (USA) in early 2022. He is  @d_a_robson  on Twitter.

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The Power of Curiosity

Francesca Gino, a professor at Harvard Business School, shares a compelling business case for curiosity. Her research shows allowing employees to exercise their curiosity can lead...

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Francesca Gino, a professor at Harvard Business School, shares a compelling business case for curiosity. Her research shows allowing employees to exercise their curiosity can lead to fewer conflicts and better outcomes. However, even managers who value inquisitive thinking often discourage curiosity in the workplace because they fear it’s inefficient and unproductive. Gino offers several ways that leaders can instead model, cultivate, and even recruit for curiosity. Gino is the author of the HBR article “ The Business Case for Curiosity .”

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CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. I’m Curt Nickisch.

In 2011, a fourteen-year-old Chinese-American girl named Clara Ma walked into a high-tech laboratory in California. She was wearing a white outfit with a mask and gloves to protect against contamination. Then she leaned over a piece of space-age machinery costing two-point-five billion dollars, and she pulled out a permanent pen.

CLARA MA: I signed my name in English, and I decided to sign it in Chinese as well – in Mandarin – and I also wrote the word “curiosity.”

CURT NICKISCH: Curiosity. The name of NASA’s third Mars Rover. Clara had the honor of signing the rover, because she had suggested that name in an essay contest when she was 11 years old.

CLARA MA: The picture of curiosity that I had as a kid, was just one that involved asking a lot of questions. I’m an extremely, extremely inquisitive person! Like, people will get annoyed with me with how many questions that I ask.

CURT NICKISCH: The human trait of curiosity is universal in children. But it’s less common in adults and often hard to find in the workplace. The fear being that curiosity cripple your career by leading you out of line. However, new research shows that curiosity can drive an organization’s performance. It improves engagement and collaboration and inspires novel solutions.

Here to talk about the performance power of curiosity and what managers can do to bolster it is Francesca Gino. She’s a professor at Harvard Business School and the author of the article, “The Business Case for Curiosity.” It’s in the September-October 2018 issue of Harvard Business Review. Francesca, thanks so much for coming on the show.

FRANCESCA GINO: Thank you for having me. It’s a pleasure to be here.

CURT NICKISCH: Why are children so good at curiosity?

FRANCESCA GINO: We are all born with a lot of curiosity and in fact, if you look at the data, curiosity peaks at the age four and five, then unfortunately declines steadily from there. And having children, I can totally see it – it’s constant wondering, constantly exploring and the type of questioning [that] never ends is: Why is the sky blue? Why do we have to pay receipts for stuff or why is it that we need to wear clothes when we get out of the house? And after learning more about curiosity, I try to be more careful about ways in which I can encourage it rather than shutting it down, given what the data suggests.

CURT NICKISCH: Starting at five years old or you’re thinking like, five years into your job?

FRANCESCA GINO: Starting earlier. So when I think about moments where I see a lot of curiosity, the thing that comes to mind is 6:00 AM in our kitchen. So the children are usually running around from cabinet to cabinet, opening them up, trying to look for interesting things. And I used to stop them because I was fundamentally worried that we would have a lot of mess or that I would arrive late at work.

And nowadays, instead, I either sit down, sip my coffee and just enjoy watching them having fun exploring, or I join in on the exploration, asking them questions about the things that they found. And I have to tell you things don’t end up in mess – most of the time. I think there was an exception with my 14-month-old daughter finding the salt and the little bottle was open, so she was shaking it around the kitchen, like a priest, and a little bit of salt ended up on the floor.

But again, the entertainment value was much higher. And it’s interesting because the fear I felt of ending up in a mess, it’s a fear that I’ve seen in a lot of leaders who want to stop explorations because they think is going to get in the way of efficiency or is going to lead to a mess.

CURT NICKISCH: That’s true to some extent though, right?

FRANCESCA GINO: Well, in organizations that really foster curiosity, it’s pretty clear to people when it is that it’s time to ask questions, to really wonder, to explore and when it’s time to put our heads down and just execute on the work. So I have not seen organizations getting in trouble for encouraging curiosity when it’s done well.

CURT NICKISCH: So you have to be willing to clean up the salt, but you’re not going to get the benefits of it without doing that…

FRANCESCA GINO: You need to start from a mindset that shutting down curiosity leads to problems, and it’s costly to both the individuals, but also for the organization that are reaping the benefits of curiosity. And that shift in mindset as an important first step. And when it’s there, you can be clear on when and how curiosity is valued. And also making sure that if people explore, the explorations are intelligent ones.

So one example that comes to mind is Intuit. Intuit has innovation awards that they give to people every year, and these are for explorations that led to some interesting new products or new processes. They also have failure awards, and these go to explorations that did not lead to new products, but in fact led to important learnings for the team. And what’s interesting about this example is that the failure awards come with a failure party.

And so you get the sense that exploration – when it’s intelligent exploration – even if it leads to failure, is something that the company values.

CURT NICKISCH: It’s interesting hearing you talk about curiosity and you also use words like exploration, wonder, and part of what makes this so difficult is because you’re sort of at the intersection of people and also organizations, right? So certain people can be explorer types where others are more like driver types. And so, you know, fostering curiosity in certain people might be easier than in others. And then of course you have this added layer of just getting the organization to go, and we all know we want diversity, and we all known we want creativity, but what are the things that stop us from getting there?

FRANCESCA GINO: So you’re right that some of us are naturally more curious than others. And so when we think about organizations or leaders really encouraging curiosity, some people might need an extra or bigger push than others. But despite the fact that by default we come to the table where the more or less curiosity, encouraging it is possible. And that’s often what leaders forget. That no matter what your tendencies are, there is something that I can do as a leader – whether starting by modeling behavior or really thinking carefully about the environment that I’m putting you in – what it is that I can do to make sure that even the people who might not be as naturally curious, feel and get the benefit of curiosity.

It’s not often talked about as much as creativity or innovation and there is a difference there. In fact, one of the things that we know about curiosity is that it’s key to creativity, it’s key to innovation. And so for organizations and leaders out there who want to foster innovation and creativity constantly in their organizations, curiosity is a good way to get there.

CURT NICKISCH: Yeah, you wrote in your article, you gave the example of Edwin Land’s daughter, who as a three year old, asked why she had to wait for film to get processed and that question led to the innovation of fast-developing film, right? An instant camera. One other positive outcome that you saw in your research is that with greater curiosity in organizations, there was actually less conflict and that seemed like it didn’t make sense. It seems counterintuitive.

FRANCESCA GINO: Thank you for pointing out that my research doesn’t make sense.

CURT NICKISCH: Right, sorry.

FRANCESCA GINO: Well, let’s see if I can help with bringing that to a little bit more sense, if you will. What is interesting about curiosity, if you think about it in a team context, is that I’m much more likely to ask questions, I’m much more open to a different view on the same task or problems. And what that does is allowing us to have more of an open mind conversation about what problem we’re trying to solve or how to get to the solution. And so what you end up with is less conflict just because there is more openness to other people’s perspective.

And so, we’re are better at taking other people’s perspective. We’re better at saying how is this issue – how is it that others could view it from a different standpoint? And so that’s why you ended up seeing less conflict in a way that improves performance if we’re working together in teams and also improves our decision making.

CURT NICKISCH: Let’s talk a little bit about with all these benefits, and if curiosity is kind of a key ingredient for innovation and growth and being competitive, why there isn’t more of it and why it’s hard to bring into organizations? Where were the stumbling blocks?

FRANCESCA GINO: We often have the wrong mindset, when we think about the value that curiosity can bring to us and to our organization. We tend to believe that letting people explore, letting people be curious, comes at the cost of efficiency. So stuff is not going to get done if you’re there exploring. Or, if you think about encouraging curiosity in a meeting, then the meeting is going to take three hours rather than one hour because we’re not going to get to a conclusion.

And as I said earlier, this is actually the wrong mindset. There is no evidence from organizations I’ve studied that suggests that when you allow curiosity to stay alive, that efficiency becomes a problem, that somehow people are less productive.

CURT NICKISCH: Yeah, there definitely is this conflict between efficiency and exploration. There’s also, just for individuals, a sense of whether or not there’s psychological safety to say things, right? Because even your article here – I’m looking at the print version of your article in the magazine – and there’s a picture of a cat with a Kleenex box, a tissue box on its head. Which we laugh at now, you’re smiling now, right? And none of us at work want to look like that cat, because everybody’s laughing at the cat: “Oh, it’s just an empty box. Isn’t that silly? Isn’t that funny?”

And there is a sense of vulnerability somehow to ask a question like, “I wonder?” because it seems like you’re admitting that you don’t know that. And so, how can you get over that so that you really do foster curiosity in a way that it’s gonna move your team or organization forward?

FRANCESCA GINO: One of the ways in which that can be done is that something that comes from a core principle of improv comedy – this idea of the “Yes, and.” And it’s a core principle and a really important one that teaches you to always add your questions or your contributions or an additional sentence after the “yes.”

And that’s important because you’re starting from a point of acceptance, so you’re not being threatening to other people. It’s much easier to come into the conversation when you accept what’s there, and the “and” adds to it in a way that might take the scene in a totally different direction, but helps you bring in your contributions.

So often organizations have used similar procedures, like an organization that comes to mind is Pixar Animation Studios. They use a similar technique they call it “Plus-ing” because you’re always adding on. And at the very basic, plus-ing is using the “Yes, and.”

CURT NICKISCH: These seem like some of the same best practices that we would hear if we’re talking about empathy or brainstorming – like write every idea on the board, don’t not write something up there, and decide at that point that it’s not a good idea. One other term that I really liked in the article was “intellectual humility,” right? That that’s another way of expressing your ability to ask more questions. And so, how important is curiosity in the sort of cocktail of, you know, positive traits and outlooks that well-performing teams have?

FRANCESCA GINO: When we approach decisions in life or at work with curiosity, we’re something from an assumption that we don’t have the answer and we are ready to learn. And what that means is that we are approaching the same situations with a lot of intellectual humility. The two often go together for the very fact that they require explorations, and they start from the assumptions that we still don’t have that knowledge.

And what is interesting is that intellectual humility is something that despite the fact that we have a lot of experience, a lot of knowledge, a lot of skills, it allows us to always stay focused on what’s left to learn.

CURT NICKISCH: How important is the leader there?

FRANCESCA GINO: It is quite important. I think leaders that can set the way by giving the right examples – so being the first one asking questions like, “Why do we do it this way?” or “What if we were to look at the situation differently?” People are paying attention to what the leader is doing and so those are small ways in which they might feel that curiosity is encouraged in the team or in the workplace more generally.

Having said that, I have the chance to talk to a lot of employees across organizations that are unsure about whether the top leadership is really encouraging curiosity and my answer there is to start small. Still ask questions in a polite and respectful way, if you think that there is a different way of looking at a problem or approaching a decision. It’s not threatening when the approach is a respectful one.

CURT NICKISCH: Yeah. The worry there is that if the CEO may not be exhibiting those behaviors or senior leaders are not, that the people who look up to those or report to those people aren’t going to value it either. And so the first people to hear some of those ideas will be, you know, your manager or your skip-level manager. So, what do you tell people who feel like the people directly above them may not welcome that and are just thinking about, you know, making sure that they can show that their team is performing well.

FRANCESCA GINO. So what I tell them usually in situations like that is to think about ways in which in the small [scale], they can start adopting strategies that are going to allow them to stay curious. So one of them, which I think is simple to adopt, even if you don’t have a boss that agrees with itt, is having learning goals in addition to performance goals.

Another one is to find ways to broaden your interests, maybe outside of work. Again, better if it’s the top leadership or the CEO directly who are supporting the idea, but even if that’s not the case, you’re not going to be disruptive in bad ways to the workplace, if you yourself thought about ways in which you could do just that.

CURT NICKISCH: Francesca, you said that teams – or people – just have a really good sense of knowing like, okay, this is a time for exploration and now that we’ve decided what to do, this is the time to get the work done. But for leaders who were worried about exploring stuff too much or asking too many questions or testing and experimenting too many things, how do you know when it’s time to say this is to be decisive? Like, what’s the magic balance there?

FRANCESCA GINO: When I think about the way we usually think about leadership or leaders more specifically, we seem to think in terms of a dichotomy, where on one side you’re the leader who is always asking questions, really trying to get the input from everybody at the table – or you’re the person who is very dictatorial. Maybe you’re not asking a lot of questions and you’re the one making the calls. And in reality, leaders can do both.

So they can decide when it’s the time where they really need the information, they need people to bring out ideas, new perspectives, and then once they are informed – or if there is a little really a lot of time pressure that they’re under – they’re the one making the call. So the two are not necessarily inconsistent.

Now, some people might say, “Okay, I seem to be doing too much of the questioning. I might be the one actually slowing things down.” As a leader, I think it’s important to be clear and transparent on when it is that we’re going to explore, ask questions, experiment, and when instead it’s time to in fact make decisions and move forward.

And that is something that I saw in a lot of leaders in a lot of organizations that in fact encourage curiosity. There is much more clarity and transparency about places in which you’re just putting your head down and going ahead with the decisions or executing on the task – versus staying curious and exploring.

CURT NICKISCH: Well, we’ve talked about how leaders can model it and then some of the barriers that are there for just kind of spreading that down through teams. But what other tactics can you take from an organizational perspective that are beyond leadership?

FRANCESCA GINO: So one is to think about it right from the start, right at the beginning of the employment relationship. And what that means is to hire for curiosity – or at least ask questions during the interviews that get at curiosity. So some people say: “Oh, sometimes in interviews I’ve been asked questions about my personal interests outside of work.” As it turns out, that’s a question that can help us understand whether a person is curious or not. Or a student of mine a while back told me that in an interview he was asked what he would do if he was on an assignment in a different city, arrived at the hotel – would he have dinner in the hotel or explore?

And again, that might be a really strange question, but one that helps you understand whether the person is naturally more or less curious.

CURT NICKISCH: You also mentioned in your article how Google put up a billboard with basically a little bit of a riddle to solve, but it wasn’t even associated with Google. It was just a question on a billboard along a highway. And they used that as a recruiting technique.

FRANCESCA GINO: That’s right. The funny story about that example is that it was my husband telling me about what it meant. At some point I saw the board the billboard in Harvard Square and not even wondered. And it was not a moment of pride since I thought of myself as a naturally curious person. And it’s an interesting example of how a company is really interested in hiring curious individuals and where that is valued.

Try to figure out a method – a little bit unusual to see if by seeing something on a board you would be triggered to go find the answer for it. And so engineers or other people who in fact understood that that was a riddle, explored it and every time they found the answer to the riddle, that was another one that the company presented to them. And after a while you would end up with the opportunity to upload your resume to see if you would get an interview at Google.

CURT NICKISCH: That’s amazing. It sounds also very smart in the sense that they selected potentially for the type of sort of analytical minds that they were looking for, right?

FRANCESCA GINO: That’s right.

CURT NICKISCH: Right, so it’s not necessarily a black mark on your curiosity, but different companies could look at that some way and say, “How would we do that for the type of people we’re looking for?”

FRANCESCA GINO: That’s right. A different company had thought about it a little bit differently. So they’re in a totally different industry, they are a fast-food chain. And when the CEO interviews candidates, especially when the candidates come in and they’re going to be in a role equivalent to a general manager of a store.

He has books on his desk, and he has multiple appointments with the candidates that are being interviewed. And one of the things that he’s looking for is whether the person who’s in front of his eyes has the curiosity of looking at the book and saying, “Oh, I wonder what the CEO is reading?” And actually explore and read the book prior to the next appointment.

CURT NICKISCH: Oh, that’s great. Yeah.

FRANCESCA GINO: So very subtle, but quite interesting.

CURT NICKISCH: What other neat ways have you come across that in your research and your work, where you found organizations doing a good job of sort of fostering and celebrating curiosity in their workforces?

FRANCESCA GINO: So one other strategy is this idea of broadening employees interests. And again, organizations can do this quite differently. One of my favorite examples is an example that is a bit dated, but is about the first manufacturer of typewriters in Italy. This is a time – we are back in the 1930s and 1940s – where every organization is very focused on how to get the most out of people.

So it was the time of Taylor and Fordism. And yet, this CEO – his name is Adriano Olivetti – took over for his father. He did that after being in the factory – so really seeing the experience of working there, right on the manufacturing floor. And what he decided to do is, one, make the working day shorter rather than longer; and two, he actually extended the time for lunch.

So from one hour he went to two hours and he used to say to the first hour was to eat lunch and the second hour was to eat culture. So he would have novelists, poets, musicians come in right after lunch and give talks or play music as a way to broaden the interest of the people working there.

Another great example from Olivetti, this company that thought really carefully about keeping curiosity alive, was the following one. At some point some workers went to the CEO and said, “There is a person who is stealing, you should fire him.” And they saw this worker leaving the factory on multiple occasions with pieces of iron and other materials directly from the factory. And the CEO, rather than firing the guy, decided to have a meeting with him. And at the end of the meeting –

CURT NICKISCH: Which is an example of curiosity…

FRANCESCA GINO: That’s exactly right. So the leader deciding to explore rather than just make a decision. And at the end of the meeting, the guy left with a promotion. So he had been promoted to be head of production of a new process.

So he discovered during the meeting that this guy was bringing pieces of materials home because he didn’t have time to experiment at work. And so he was doing that at night and over the weekends. And so it’s just a great example of rather than taking a suggestion for granted, fire the worker, you explore it and you start asking question to the person and then you are actually allowing him to have more time to explore his interest.

And what that led to was the invention of a machine that was one of the most successful products for Olivetti. In fact, at the time it was put on the market, it was selling at the same price of a Fiat Cinquecento, so a really high margin for the product.

CURT NICKISCH: Yeah, no kidding. That’s an amazing story. And the list of of products and companies and ideas that have walked out the doors of places because they haven’t had the freedom to explore their curiosity about something that’s related to the work that they were doing for the company, you know, is kind of a long list. When you think about all the time that companies try to come up with new ideas, or buy companies, the stuff that’s right in-house if you just let it go, it’s kind of amazing.

FRANCESCA GINO: And you’re right, it just started with curiosity on the part of the CEO when seeing something that looks strange, rather than just accepting the most obvious answer, go and explore and see if the intentions of the person actually were different.

CURT NICKISCH: What’s the biggest misunderstanding about curiosity that you think people have out there that you want to clear up?

FRANCESCA GINO: That fundamentally curiosity is messy. That it’s going to lead to explorations that have either no one or nothing productive coming out of them. I think that that is one of the issues and one of the barriers why we don’t see a lot of curiosity at work. And one of the reasons and big motivations for working on this article is that leaders often also don’t see the business case for it, so a lot of the research on curiosity is research that has taken place over the last decade or so, and so I think that leaders are still not aware of all the sorts of benefits that curiosity can bring about in the workplace.

CURT NICKISCH: Francesca, this has been super interesting. Thanks so much.

FRANCESCA GINO: Thank you so much for having me. It was a fun discussion.

CURT NICKISCH: That’s Francesca Gino. She’s a professor at Harvard Business School. She’s also the author of the article “The Business Case for Curiosity.” You can find it in the September-October 2018 issue of Harvard Business Review or at HBR.org.

This episode was produced by Mary Dooe. We get technical help from Rob Eckhardt. Adam Buchholz is our audio product manager.

Thanks for listening to the HBR IdeaCast. I’m Curt Nickisch.

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Curiosity - Essay Examples And Topic Ideas For Free

Curiosity, a fundamental human trait, drives the quest for knowledge and the exploration of the unknown. Essays on curiosity might delve into its psychological underpinnings, its evolutionary significance, and its role in learning and creativity. Discussions could explore the various dimensions of curiosity, such as trait curiosity, state curiosity, and diverse curiosity, along with the psychological and neurological mechanisms that fuel curious behavior. The discourse might extend to the examination of curiosity in the realms of science, art, and innovation, showcasing how curiosity-driven endeavors have propelled human progress. Furthermore, essays might delve into the cultivation of curiosity in education, the workplace, and personal development, exploring the benefits and potential drawbacks of a curious mind. The broader societal implications of nurturing curiosity, fostering a culture of lifelong learning, and promoting open inquiry could also be riveting aspects of exploration in understanding the profound impact of curiosity on individual and collective advancements. A substantial compilation of free essay instances related to Curiosity you can find in Papersowl database. You can use our samples for inspiration to write your own essay, research paper, or just to explore a new topic for yourself.

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Is Curiosity Uniquely Human?

2017, IEEE CDS Newsletter

Related Papers

Elias Baumgarten

Coltan Scrivner

Since Berlyne's groundbreaking work in the 1960's, curiosity has been a popular topic for psychological research. Despite a rich history of research, scientists have not been able to agree upon a single definition or taxonomy of curiosity. These diverging perspectives have led to a breadth of research that has yet to be integrated under one framework. Moreover, most research on curiosity has focused on neural mechanisms and ontogenetic characteristics, while the evolutionary aspects of curiosity have received little attention. I propose that research on curiosity can benefit from an evolutionary perspective, and more broadly from a biological perspective on information-gathering behavior. In this chapter, I synthesize the literature on curiosity from the perspective of behavioral biology-i.e., Tinbergen's four questions. The behavioral biology framework provides a powerful lens through which questions about behavior can be asked and iterative empirical work and theoretical construction can take place. In particular, I argue that evolutionary perspectives on curiosity can help identify the "joints" of nature at which curiosity may be carved. By identifying the function of different types of curiosity, a more robust and universal taxonomy of curiosity can be created.

In this paper I respond to and elaborate on some of the ideas put forth on my book The Philosophy of Curiosity (2012) as well as its follow-up “Curiosity and Ignorance” (2016) by Nenad Miščević, Erhan Demircioğlu, Mirela Fuš, Safiye Yiğit, Danilo Šuster, Irem Günhan Altıparmak, and Aran Arslan.

Educational Theory

Reza Lahroodi

A bstract In this essay, Frederick Schmitt and Reza Lahroodi explore the value of curiosity for inquiry and knowledge. They defend an appetitive account of curiosity, viewing curiosity as a motivationally original desire to know that arises from having one's attention drawn to the ...

Curiosity Studies

Perry Zurn , Arjun Shankar

Carla Cesare

Curiosity is commonly referred to as a way of being, or an object of curiosity. How curiosity is part of our daily lives, how we engage with curiosity intellectually has a long and interesting history. Since the sixteenth century it has been manifest in cabinets of curiosity, museums and curio cabinets; exercises in collecting, self-reflection and discovery. However, the end of the twentieth-century has altered our sense of the world, through the speed and accessibility of information leaving a changed relationship with wonder. This paper discusses the role of curiosity in research as a "habit of curiosity", (Benedict 2001, 2) a method for discovery. It reviews its historical manifestations and concerns, locating it through objects and actions, and questions what new meanings the twenty-first century brings with it. Is curiosity at risk? Is it still risky? The relationship between the individual and their interior and exterior socio-cultural landscape continually creates n...

penultimate version of a paper published in Ilhan Inan, Lani Watson, Denis Withcomb and Safie Yigit, The moral Psychology of Curiosity, Rowman and Littlefield , 265-290

Pascal Engel

Curiosity is the epistemic desire, libido sciendi. It is presented in the tradition both as a good thing, a passion for learning which can become a virtue (studiositas) – without it could science exist? - and, most of the time, as a bad thing – a kind of akratic desire to know, a major vice, at best a form of intellectual and ethical illness. As the large literature on curiosity through the centuries testifies1, it is not easy to assess where the topic belongs. This is largely due to the fact that we do not know exactly what curiosity is, but also it is hard to dissociate an investigation into the nature of curiosity from an investigation about its value or disvalue for learning, and for life in general. My objective here is not to give a definition. I try to locate curiosity on a map of the mind, and on this part of the map where knowledge is located.

The Moral Pschology of Curiosity

Safiye Yigit

Educational Psychology Review

Reinhard Pekrun

Curiosity and interest are at the core of human inquiry. However, controversies remain about how best to conceptualize these constructs. I propose to derive definitions by attending to the common core of typical usages of the two terms. Using this approach, curiosity can be defined as a psychological state that includes three components: recognition of an information gap, anticipation that it may be possible to close it, and an intrinsically motivated desire to do so. Interest can be more broadly defined as intrinsically motivated engagement with any specific object, content, or activity. The two definitions imply that curiosity is a special case of interest. Furthermore, I propose to use the state-trait distinction to distinguish between momentary and enduring forms of both curiosity and interest, which makes it possible to treat state versus trait curiosity and interest in conceptually parallel ways. To make further progress in understanding the two constructs, research is needed ...

Axel Gelfert

This paper lays out in detail how, on David Hume’s account, curiosity relates to the other (direct and indirect) passions that guide our actions, and in particular how curiosity is able to identify potential targets and make inquiry self-perpetuating. This is followed by a discussion of the risks associated with the unbridled pursuit of knowledge and the role of sympathy in anchoring curiosity to shared conceptions of value and epistemic worth. The overall picture, I argue, is one in which curiosity is recognized as indispensable in carrying our inquiries beyond the narrow range of our immediate practical interests; at the same time, it is seen as being naturally constrained by the way it is embedded within the wider network of the various passions, which in turn reflect aspects of our own social and cognitive situatedness. By tracing the workings of curiosity in this way, and by relating it to the rich tapestry of passions that animate our mental lives, Hume’s theoretical philosophy provides a subtle and insightful framework for thinking about the moral psychology of curiosity, or so I argue.

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Human Curiosity

How it became the primal force to create and drive the modern world 1.

I'd like to start by telling you how I got to this subject, because this helps understand how I got to the conclusions I drew. It all began in a history seminar. We were discussing the Industrial Revolution which, as is well known, basically started in the latter half of the eighteenth century. As we were talking about it, I began to ponder the question: Why did it happen in the late 18 th century? Why didn't it happen a lot earlier? After all, the components of the Industrial Revolution had been in place since ancient times. For example, the theory of mechanical machines was something that had been thoroughly elaborated in ancient physics. People knew how to multiply effort. Also, there exists an ancient Greek drawing of a rudimentary steam turbine engine from which it is clear that the principles behind such a device were quite well known. Furthermore, there were factories in the Roman empire where people produced large quantities of goods for distribution and sale. All this made me wonder why it took until the 18 th century for the Industrial Revolution to get launched. Clearly one needed more than technology, which is widely considered the primary essential factor that drove the Industrial Revolution. The question was: what was that "more"? When I finally realized what the answer was, I came up with a much deeper understanding of the driving forces behind productivity and behind economic activity in general.

So let me take you back to my initial probings on the subject. The basic spur to making anything, from the earliest time in human history, is to satisfy the physical needs for survival. We don't think about this much because we in the developed world are way beyond the survival mode of existence, but we all know that the three basic physical needs are food, clothing and shelter. For most of human history, for the overwhelming majority of people, meeting these three basic needs even minimally was a lifetime project that absorbed all of their energy. Indeed, the prehistoric archeological record reveals a wealth of human ingenuity focused on creating the means of satisfying these three basic needs, and on competition over meeting those needs, mostly in the form of war.

Let's just look at some examples of how far the ancients got with their technology when it was focused specifically just on those three needs. We know that they were tremendously successful in finding ways to enhance food production. I remember how amazed I was when I first learned about the incredible irrigation projects that were built all over the ancient world. In the Mesopotamian Valley, currently known as Iraq, there were huge irrigation projects spanning the entire country. In Egypt, the waters of the Nile were spread through ingenious systems of pumps and water wheels and canals that were carefully maintained to provide water to large tracts of land. There is the so-called "hydraulic civilization", which is prehistoric and which spanned southeast Asia, Indonesia and even came up to the Arabian Peninsula. Its name derives from the fact that we have virtually no other evidence of its existence except for enormous waterworks that were created for irrigation – dams, reservoirs, and pipelines that were built out of stone, without mortar, and that were fitted so perfectly that they were leakproof.

We know that the ancients showed tremendous ingenuity in making clothing. People learned how to tan leather. I don't know how many of you have ever tanned leather, but let me assure you it's quite a process. Cleaning the skins and devising the means to make them last – I often ponder how anybody ever thought that up. Or how people invented yarn for weaving. I could look at a sheep from now until doomsday and it would never occur to me to do the things you have to do to make yarn. Or linen: taking a particular grass, soaking it in a river forever, and then pulling it out and processing it.

I don't have to elaborate on how technologically advanced the ancients were in creating the tools of war. In that connection, they developed metallurgy to a high art without really knowing very much about what we today consider the basic chemistry of metallurgy. 2 Perhaps the most interesting focus for a lot of creative ingenuity in pre-modern times was in that key domain for survival, religion , because above all people thought from the very earliest times that in order to maintain their survival they had to have the blessings of the cosmic powers that ruled the earth. So they put a tremendous amount of effort into trying to make sure that they were on the right side of those cosmic powers.

The point I'm trying to make is that pre-modern times achieved an immensely sophisticated technological state in all of these areas that are directly connected with survival – but basically only up to the point where survival was necessary. One never really had to go beyond that. There was no need to tinker with success if you could manage to feed your population so they didn't starve, if you could manage to have them clothed enough so they didn't freeze to death, and if you could manage to have them housed enough so that they didn't die from exposure. Life was precarious anyway, and if you lived to the grand old age of thirty or forty, you were really doing well.

But there is a key additional factor that enters and muddies the waters. That factor was first noted explicitly by Aristotle, who starts his central book – the book on which all of his thinking is based, his Metaphysics – with the remarkable statement that "human beings are naturally curious." Now, that is a really odd statement to make because curiosity is really counter-intuitive . Curiosity is activity without a predetermined purpose. Or, to put it in a different way: curiosity says, "If it ain't broke, fix it anyway." It's totally bizarre. It means that people explore the unknown by virtue of the fact that they are human. This is something that Aristotle claimed to recognize as a universal human trait. He understood that it meant looking for new experiences, searching for the unknown; and of course when you're searching for the unknown it means taking risks. There's a lot of talk nowadays about how teenagers are risk-takers, because we look around us and see teenagers doing that, but what Aristotle says is that all human beings are risk-takers, not just teenagers. And they're doing it all the time for no other reason than that they're human.

The other thing Aristotle said which was key to understanding what happens later is that the opportunity for curiosity to function freely and to be indulged in depends on the availability of leisure ; and because for Aristotle "culture" meant all human activity that was over and above the basic bare bones of survival, what he was saying was that for people to create culture, they have to have leisure. Since in his day almost nobody had time to spare, the availability of leisure was very limited.

Summarizing: leisure and curiosity and culture were three factors that Aristotle saw as being intimately linked. That immediately opens a window into the lives of the rich and famous in the ancient world and the middle ages. Throughout that time, there existed a small group of people who were especially well-endowed with wealth. This elite had the power and ability to be able to have leisure and be supported by the rest of the population. And they're the ones who created culture, all of it! It's quite a list of accomplishments, produced by a very thin veneer of society. Just think about it. They created art. I'm not talking about the fact that even busy people every now and then can scribble on walls; I'm talking about really sophisticated art. Great edifices. Great murals. Great mosaics. All sponsored by people whose sole interest was to have something beautiful to look at, something really delightful. People who were never satisfied. No pharaoh ever said, "Well, there are a lot of nice pyramids out there with all kinds of pretty stuff; I don't need to build another one." Pharaohs built not just because they too wanted a place in history, but because they wanted something new, something different. They wanted their workmen to make something more interesting, something awe-inspiring.

They created literature. It's hard to think of anything that is more a "waste of time" than literature. Especially in ancient times, where you had to scribble everything out longhand and almost nobody could read. Imagine a person who was moved in ancient times to write a play. Who did he write the play for? Who's going to read it? How lucky we are that any of them survived because there were only a handful of manuscripts of each one! In fact, most of what was written in ancient times didn't survive. The great repertory of ancient literature – the library of Alexandria – went up in flames in the 8 th century. With that, most of the writing of the ancient world disappeared.

They had entertainment. Not only theater, but games. They had fun, they had parties. If you have read the "Symposium" by Plato, which is nothing other than the story of a really wild drunken party featuring Socrates and his buddies, you'll get an idea of how the elite caroused. Cuisine. We have cookbooks from ancient times, for the elite. Do you think the average person looked at a cookbook? Finally, the elite were the ones who engaged in science and philosophy, who went to little academies and listened to the masters speak. Unfortunately, the only extensive surviving record we have of this is Plato's Dialogues , which is a terrible shame because we know these academies existed all over the place. 3 Plato's Dialogues provide a wonderful picture of these small knots of people who had lots of time on their hands, who obviously were very wealthy, and who chatted about all the important philosophical questions that we still talk about today.

I have to tell you about just one such question, because it has to do with curiosity, and it shows you how bold Aristotle was when he talked about curiosity. There's a dialogue of Plato's in which Socrates asks the question, "How can you ever look for anything new?" – which is the essence of curiosity. The problem, as he saw it, was that if it's new, you don't know that it's there, so you can't look for it; and if you're looking for it, then it's not new, because you know it's there. He tangles himself in his quest for an answer page after page, until he comes up with the only answer he could figure out, which is: you can't ever seek something new; rather, everybody is born with all the knowledge of everything within him, but they forget it at birth. Thereafter, all of the search for ostensibly new things involves trying to recollect what we once knew.

Aristotle didn't buy that at all. He was much more practical. He said, in effect, that people by nature look for new things all the time. They have no idea what they're looking for; they're just looking, pretty much at random.

One thing Aristotle missed that is a major contributor to cultural development is communication . Communication pushes the boundaries of what you can explore. You don't have to reinvent the wheel if you can communicate with people and find out what they've already found out. So a tremendously important piece of the curiosity factor in human development is being able not just to grope around the world on your own, but to engage in some kind of exchange with other people, to call upon the collective experience of the group, in order to learn more about where you're headed. Remember, virtually all communication in early times was oral. People developed the talent for memorizing huge quantities of information. In fact, much of the literature that has survived from the ancient world was transmitted from one generation to another by memorization before it was committed to writing. There were professionals who specialized in this. Then writing came into play, and made it easier. Writing, as we all know, is one of the great cultural breakthroughs, even though not a lot of people wrote and not a lot of people could read, and everything had to be passed around hand-to-hand in manuscript form. Still, if you compare the situation before writing and after writing, you realize that writing made possible the availability of a lot more base-line knowledge from which curiosity could take off and advance into new territories instead of starting from scratch.

Let's summarize the human condition in pre-modern times. Briefly, the overwhelming majority of people struggled for existence. They were satisfied if they could meet their basic needs. They didn't have time or energy to deal with the broader culture. There was a small elite that had the leisure to create and transmit culture from generation to generation. The rate of cultural development was limited by the small number of people who belonged to the elite; by barriers to communication, due to the lack of mobility which made face-to-face contact between people who lived far apart rare; and by the difficulty of diffusing information through the written word. But I want to add a key point: the curiosity-driven culture of the elite was a consumer-driven culture . It was the elite that demanded new experiences, and led to the creation of all of the cultural treasures that we now treasure so much. They wanted novelty, innovation. They were never satisfied with what they had. They were never satisfied with what existed. They always wanted more. They always wanted prettier. They always wanted variety. And they created all that. We can see some of the results in the museums we go to today, where we can enjoy the products of this ancient, elite, consumer-driven culture.

Now we can return to our original question: what about the Industrial Revolution? How did it come about, and how is it related to all the groundwork I've been laying? The tie-in occurs in what I like to think of as history's first "big bang" – the explosive early modern era. The time between the 15 th and 18 th centuries is a really tiny span of time historically, just three centuries compared to the hundreds of thousands of years that human beings existed, the tens of thousands of years that urban civilizations existed, and the thousands of years that writing existed. During these 300 years there was a series of upheavals that occurred particularly in the Western world. Each one was largely accidental, and their concurrence was equally accidental. All in all, they constituted a set of historical coincidences of staggering proportions, which led to unintended and entirely unanticipated consequences. We're all aware of them individually, but it's only when you put them together against the background that I've just outlined that you get a sense of the explosive impact they had on Western culture. I will discuss them no particular order, because they didn't take place in any special order.

The invention of movable type printing . That was basically invented as a way to save on money for scribes. Scribes were expensive, they got sick, they were a bother to deal with. Gutenberg figured out a way to save on scribal time by assembling movable type and making replications of it. At the time, no one realized the incredible fallout that would follow from that little invention. I don't know how many of you ever saw early printing presses. They were incredibly difficult to operate. The letters had to be individually carved out of wood or cast out of metal. Then they had to be set line by line, after which they had to be laid out on a page and held together firmly. Then somebody came over with a huge ink roller and rolled it along the top of the type, after which a huge sheet of paper was laid on top and pressed against the type. Have you seen pictures of presses? There's a large screw with a big block of wood on its end. As you lower the screw, the wood gets lowered onto the paper, then it is raised, the paper extracted and hung up to dry, and the process is repeated for each sheet. The point I'm trying to make is this: as tedious as this process is, it still enables you to replicate hundreds of times in a day. Can you imagine how long it took to write that sheet longhand – if you could find a scribe? And how expensive it was? All of a sudden, literacy becomes something worthwhile. It didn't make any sense to read before. What was the point of reading? There were hardly any books. Now there's something to read. Human beings are naturally curious. They thirst for new information. The availability of books fosters independent research – and thinking – for everybody who could lay their hands on a book and mull over its contents. It was worth pursuing even if they only had a few minutes of spare time, because books became relatively cheap and plentiful now.

Printing, books, and literacy constituted a time bomb for religion. The first book ever printed was the Bible. Virtually nobody ever actually read the Bible. How did people find out what was in the Bible? The preacher told them, and the preacher in turn was told by his teacher in the seminary. The preacher probably never read the Bible either. Now all of a sudden Gutenberg printed Bibles and anybody could read them. The result: many people were motivated to learn how to read, and when they did they often discovered that it didn't jibe with what they had been told. Before you could turn around, 1500 years of Roman Catholic monopoly on religion in Europe was shot to smithereens, and it was never restored. All because of human curiosity, all because people wanted to know what was actually in that holy book. Did they have to read the Bible? After all, life was rolling along as it had for centuries. The preacher told them what to do, how to go to heaven, what would get them sent to hell, and all that important stuff. Life had gone on that way for 1500 years – what was the problem? And now, all of a sudden, they had an opportunity to see for themselves. Why did they bother to read? It just created problems for them. It was risky to read. They did it anyway .

Let's look at another event: the European discovery of the New World . Now if there was ever an accident, that was it. The story behind this is fascinating. We all know that Columbus went to open a trade route to China. No problem; the earth is round. Everybody intelligent knew the earth was round. All this business about the earth being considered flat is a fairy tale. Aristotle clearly explains that the earth is a sphere, and that knowledge was part and parcel of ancient science. In fact, the ancient Greek scientist Eratosthenes measured the diameter of the earth and got it pretty right. The result showed that the earth was a huge sphere – too big to navigate sailing West from Europe. Columbus, however, thought the radius of the globe was much smaller than what Eratosthenes had determined. He had reasons to think so, which Thor Heyerdahl outlined in a brilliant tour de force . 4

Columbus also had good reason to seek a route to the Far East by sailing West. You see, European trade with the Far East had been monopolized by the Italians for centuries. The Italians, just like the Romans in ancient times, controlled the Mediterranean, so they controlled the flow of goods from the Far East via Asia and the Near East. To be sure, the trade was directed at the wealthy elite, but the demand was considerable, which made the Portuguese want to get a piece of the action. So the Portuguese figured that since they can't go through the Mediterranean, they might be able to sail around Africa. After many brave journeys, they made their way around the Cape of Good Hope and they found themselves in India, and hence in possession of a good chunk of the Far Eastern trade. This, in turn, left the poor Spanish king in the dust. They couldn't go around Africa, because that would precipitate a war with Portugal, and they couldn't go through the Mediterranean, because the Italians controlled that route. So here comes this eccentric Italian navigator, Columbus, who says to the Spanish court, "I'll get you into the Chinese trade. We'll go due West." And he made his case and got the necessary funds. So in effect it was an accident of history that the Spanish were forced to seek a different route if they had any hope of reaching the Far East by avoiding the Italian and Portuguese monopolies.

They didn't find China; they found the New World instead. The irony is that in no time flat, it became clear that they had found something even better than China. They found a bunch of people in the New World who had amassed gold and silver. Talk about accidents of history! And what does a good, healthy European do when he sees somebody with gold and silver? He takes it. So, they took the gold and silver – shiploads and shiploads of it.

Here once again we encounter another accident of history. Did you ever stop to think of why anybody gives a damn about gold or silver? Actually the Native Americans who mined it didn't use it as money. For them it was something decorative. In the beginning, when Columbus met them on his first journeys, they wanted his decorative stuff. As a kid I was taught that the Europeans initially conned the natives by giving them beads for gold, and our teacher would say, "That's a terrible thing. They really took the natives for a ride." But that wasn't the way that the Native Americans saw it. It's another example of human curiosity at work. They had lots of gold, but look at these beautiful beads! Take our gold, give us some beads in exchange. The point is that it's another accident of history that gold and silver are Europeans' mediums of exchange. What happens as a result of all this? Europe is flooded with money, and there is a tremendous increase in the leisure class. That's what we've been talking about all along. Leisure suddenly becomes available to an extent that it never, ever had been before. It's as if everybody won the lottery! Money just flows in. It enriches the nobility, and creates a non-noble elite as well, later called the "middle class" or the "upper class." The elite want to indulge their curiosity. They want the kind of pretty clothes that the king has, because that looks really nice. So they hire a tradesman to make clothes, and now he's got a pile of gold, which he in turn wants to use. This is the "multiplication factor" economists talk about, and with its help, within a hundred or so years, what you get is a tremendously rich Western Europe, by accident – totally by accident. That's factor number two.

Factor number three that happens at the same time: the discovery of the cosmos . You might think that people knew it was out there; they weren't blind. But up until the 16 th century, people had common sense. Common sense dictates that when you look up at the sky, you know that you're sitting here on solid ground and the heavens are rotating around you with unperturbed regularity. The sensible thing to conclude is that the heavenly bodies, which rotate in unison, are stuck on some sort of rotating celestial sphere. Aristotle had scientific theories about it, religions had religious explanations for it. The most important conclusion about heavenly matter was that it wasn't like anything on earth. Earthly matter falls; if the stuff of which the heavens are made was anything like the earth, it would have all fallen down a long time ago and there wouldn't be anything left up there.

Now, optical lenses were common from the Middle Ages on. They were sold in markets; in fact, eyeglasses were sold all over Europe just like we sell them in drug stores. At some point, people playing around with lenses put a couple of them together, and figured out how to turn them into a telescope. Galileo was the first person to become famous for using one, because he made a big fuss about what he saw when he looked at the moon. He announced to the world that he could distinguish rivers, lakes, hills and valleys, just like the earth! And Mars – Mars has canals! People thought he was totally crazy. After all, if the moon was like the earth, it would have fallen down a long time ago. Anyway, how can anyone trust a telescope? Science is based on hard knowledge. The first thing you notice about a lens is that it distorts. A lens is a distortion machine. Here's this crazy fellow Galileo putting two distortion machines together, looking at the moon, and saying, "I see hills and valleys."

Give the Catholic Church credit for saying he was a nut. In context of their time, they had it right. But in a very short length of time lots of other people were reaching the same conclusion, and this is tremendously significant from the point of view of human aspirations. There is a qualitative difference when you feel that you can reach out to a cosmos that is identical in nature to our own planet, and that there is an endless variety of worlds to study and discover out there. Suddenly, the human spirit soars. You see it in the literature of the time. Writers are drunk with excitement about experiencing the universe.

The fourth thing that happened then – and this is related to the others although it doesn't follow from the others – was that organizations were created to foster creativity and promote the creation of culture : clubs, societies, salons, places where people got together over a meal or for an evening. Conversations were recorded and circulated to friends. Suddenly – I say "suddenly" because we're talking about a span of less than a hundred years, a blink of an eye in history – all kinds of societies were set up all over Europe: scientific societies, artistic societies, musical societies, cultural societies, salons, for purposes of collaboration and dissemination. That has a tremendous feedback effect; the more people do it, the more people want it. It becomes a buzz.

Finally, we have the invention of the financial infrastructure for modern trade , something absolutely essential for what happens in the Industrial Revolution. We can be curious, we can be inventive, but we aren't going to get anywhere if we can't do something with what he have created. That poses a problem: how do you conduct trade? If you start thinking about the basic things you need to create an environment in which people can produce in abundance, then you realize that there's a whole bunch of things we take for granted that didn't exist until just about that period of time; for example, the idea of a corporation. What an ingenious idea! What a tremendous boon to creativity! It enables you to create a persona that is not a person at all, that can take risks, raise and lose money, and never put you in debtor's prison (which was a highly populated place in those days). And reliable banks – or at least banks that are semi-reliable. Just the concept of a bank is impressive. We give somebody our money to hold. What's to keep them from running off? How do you create an institution that keeps that money relatively safe and yet enables the banker to lend it to other people so that they can create other institutions?

These are very pedestrian things I'm talking about, but every one of them had to be in place in order to have the infrastructure to support increased production. The industrial revolution is not about millions of people in cottage industries sitting and knitting at home and selling sweaters. It's about big companies producing large amounts of things, and you needed those infrastructures to do it.

One other thing about infrastructure: a stable system of laws and a fair judicial system to enforce them is extremely important for stable economic activity . If you cannot have clear rules of the game and the ability to enforce agreements, you won't have a thriving economy. Indeed, that is considered one of the main factors holding back large parts of the world today from reaching their full potential for prosperity.

I've listed five different major areas in which things happened during a relatively short stretch of time. In light of these, it becomes a little clearer why the industrial revolution happened when it did. You get a growing middle class, and with it a tremendous expansion of demand for innovation and exciting new experiences. People all over want stuff. That means they want lots of stuff produced and they want it produced fast and they want it produced in variety. That kind of demand was an open invitation to people to produce, to try to satisfy that need. Historically, there was never that level of demand before because you never had that big a leisure class thirsting for new experiences. Also there's an incessant demand for improvement in all kinds of communication, which is another hallmark of the industrial revolution. Railroads, shipping, telegraph, all are outcomes of people's demands to get hold of products, to know what they are and where they're located, to be able to send them anywhere, to be able to market them, to produce them, to ship them. This explosive growth of trade is the heart of the industrial revolution.

Let me move on briefly to the second "big bang"of history – the Information Age. The invention of computers led to staggering growth in a number of specific areas with which we are all familiar. I just want to review them rather quickly.

First of all, the information revolution led to the ability to produce in much larger quantities and with much higher quality. We're able now to control production in a way that couldn't even be dreamt of fifty years ago. The information revolution has enabled us to micro-manage production so that we can satisfy individualized demand. That's really important when you have a leisure class that is looking for new experiences, because the more people look for new experiences, the less they want to duplicate the experience that other people have. They not only want more clothes, they want more and different clothes. And they don't want exactly the same cars; everyone wants their own unique thing. With every passing year, the ability to micro-manage production has increased demand because the more variety you're able to introduce, the more people want of variety – because people are curious.

Today, you're able to disseminate information to a huge pool of recipients. You can put stuff out there and everybody can access it. Earlier, writing had made it possible not to have to reinvent the wheel, because people could share experiences. Now, you can pinpoint the target audience with whom you wish to share experiences. You can find the handful of people in the world who care about the stuff you care about. In a short time you can unearth them, you can talk to them, you can exchange information with them. The result is a potential for an unending flow of creativity.

All of these factors in the Information Age give a tremendous boost to curiosity, which in turn makes everybody have more leisure and enables them to be more creative. There is an enormous upward spiral of demand driven by leisure 5 and curiosity. In this sense, the Information Age really turns out to be an extension of the Industrial Age. The same kinds of things are happening from a socioeconomic point of view that happened in the Industrial Age, except they're happening on a much bigger scale because now all the things we could do in the Industrial Age we can now do that much better, that much more quickly, and with that much more variety.

I cannot emphasize enough that what's involved is curiosity driving human beings as consumers – the very thing that so many people decry. The consumer in people is not a person who for some base reason is looking to accumulate material things. It's a person who is looking to generate new, exciting experiences. That's human nature. You cannot stop it. It has only peripherally to do with money, or the accumulation of goods, or showing off. The key factor is: "I want something new and exciting. I want better video games. I want better TVs. I want better movies. I want lots of different kinds of movies. I want to create my own movies. I want to create my own animation. I want to be able to make my own music. I want to put together my own CDs." A desire for new experiences and creative activity that is driven by curiosity.

Because Western culture, as I've now described it, is so tied into this fundamental trait of human nature that Aristotle first described – curiosity – it has not only been successful, but it has become a source of emulation for other cultures over and over again that come into contact with it. That should come as no surprise. It has nothing to do with natives in the middle of the Amazon abandoning their splendid culture in order to follow the base influence of American rock music. It has to do with the members of these other cultures suddenly discovering that there's a whole lot of exciting stuff out there that they have never experienced, and that they want to know. Any culture that tries to block this process by setting up barriers and walls is destined to fight a losing battle.

The implication of all this for education is clear. You want children to grow up in an environment in which they can have their native curiosity unleashed. That's what the Information Age is about. Kids who are good at following their curiosity are ready to step into the modern world and lead a satisfactory life. It's inconceivable that anybody with his head screwed on right would take kids today and put them in an environment which says: "Don't ask questions."

That leads to the final thing I want to say. In general, "institutions of learning", so to speak, are devoted to preserving and transmitting the culture that exists. Their job is to take what's known and to make sure it doesn't get lost. Academicians are always worried that if they don't do this, the culture will get lost. If we don't teach kids Shakespeare, they're not going to read him and he will be lost; and Shakespeare, they are quite sure, is central to our culture. In general, if we don't teach A, B, C and D, they feel we will have lost the ability to maintain our culture. Within that view is embedded a semi-static view of culture that is perfectly suitable to most of history, up to modern times. As long as culture didn't change very fast, it was adequate to know what exists. But in the era of rapid change that began several hundred years ago and is now moving at full throttle, there is no such thing as "a culture" to be transmitted. The culture is not a stable entity. It's an open-ended search by all of humanity into whole new areas that have never been touched before. What they are transmitting in their academies is a corpse. Now, if pieces of that corpse are worth dissecting and maintaining in some kind of a laboratory, so be it. There will always be people who are interested in pathology. It's a great subject. "Let's dissect the corpse. What made it tick?" But I wouldn't make it an obligatory subject for everyone!

Let's step back for a minute and think about this issue. There was tons of culture in the ancient world: poems, plays, laws, religious tracts, philosophy, in Babylonia, Assyria, Persia, the Hittite Kingdom, Egypt, and elsewhere. These were all advanced cultures. Where are they? Why aren't they here? The thing is that the grand sweep of history enables the human race to filter out the things that they seek to perpetuate – not because somebody has made them do it. There's lots in our culture that I'm sure is worthy of survival but I don't know what it is. I'm not the person who decides. Time, and the culture itself, decide. If enough people like Shakespeare, they're going to read Shakespeare. If enough people don't like Shakespeare, we can shove it down their throats from now until doomsday – a hundred years from now nobody is going to read it, just like we don't read the Egyptian Shakespeare (and don't even know if he existed). We have to accept this. Over time, what different people in the leisure classes – who have the time to deal with culture – decide is worth keeping is what survives. All it takes is a few devotees to guarantee survival. The point is that as long as people are interested in something, they'll keep it alive. Otherwise, it will die.

I leave you with the thought that Aristotle had it right: human curiosity triumphs first, last, and always. Nothing can stop it. We're lucky to be living in an era when its free exercise benefits both the individual and society as never before.

1. This article is based on a talk presented at the school in March 2003.

2. Let's put it this way: we can read some ancient text about metallurgy and chuckle about it because they had it so wrong. I can imagine what people are going to think a thousand years from now when they read our "modern" chemistry texts.

3. Some scholars think that most of Aristotle's works are lecture notes taken by Aristotle's students in his Academy. Whether or not this is true, they lack the feel of direct personal contact that one has in Plato's works.

4. "Columbus and the Vikings," Chapter 5 in Early Man and the Ocean: A Search for the Beginnings of Navigation and Seaborne Civilizations (Vintage Books; New York, 1980), p.127.

5. It's important to keep in mind that Aristotle's definition of leisure is whatever time is left over after that devoted to basic survival. We need water to drink, food to eat, one set of clothes, and some shelter. When I first started working at Barnard College there was a young philosopher, scion of a very wealthy family, who had completely renounced his inheritance, and lived secretly in a room at the college. He had precious few clothes and he ate potatoes and onions that he boiled on a little gas stove in the room. That's all he ate. (Actually, it's amazing he never got caught, because I could smell it the minute I walked into the building in the morning.) He stayed for a year, happily writing his papers and books; and he subsisted on $1,000 a year. My point isn't that we should all be happy with $1,000 a year and a diet of potatoes and onions, but that we should recognize that the things that we feel today are not "extras" but are "really" the necessities of life are necessities in the sense that they satisfy that need that we have that I've been talking about – that need to have a rich life. Indeed, the richness consists of all the myriad things that we feel are essential.

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• Published: 13 October 2021

Humans monitor learning progress in curiosity-driven exploration

• Alexandr Ten   ORCID: orcid.org/0000-0002-9424-8757 1 ,
• Pramod Kaushik 1 ,
• Pierre-Yves Oudeyer 1 &
• Jacqueline Gottlieb   ORCID: orcid.org/0000-0001-6507-4375 2  

Nature Communications volume  12 , Article number:  5972 ( 2021 ) Cite this article

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• Human behaviour

Curiosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g., percent correct) and learning progress, that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on unlearnable activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a free-choice experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include a learning-progress component provide the best fit to task selection data. These results bridge the research in artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and introduce tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

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Introduction.

Curiosity, our desire to know, is a fundamental drive in human behavior and a topic of renewed interest in neuroscience and cognitive psychology 1 , 2 . The vast majority of recent research on curiosity has operationalized it as intrinsically motivated information demand, using tasks in which participants can request information about future events but do not have the opportunity to exploit (act on) the information. The studies have shown that humans and other animals seek to obtain information as a good in itself and this preference is encoded in neural systems of reward and motivation, suggesting that information is rewarding independently of material gains 3 , 4 , 5 , 6 .

While these findings tap into the intrinsic motivation behind curiosity, they are yet to capture the full scope of curiosity-driven investigations 7 . Specifically, in natural settings, humans investigate questions on much longer time scales relative to those tested in the laboratory. In contrast with tasks of information demand in which participants request information about brief unrelated events – e.g., a forthcoming reward or a trivia question – in natural behavior, learners maintain sustained focus on specific activities such as reading an article, conducting an online search, or taking a course. Operating from early infant development 8 , this ability for sustained investigations may underlie the most important ecological role of curiosity, as it allows people to develop individual interests and skills and, ultimately, discover explanatory models and latent structures of the world 9 , 10 , 11 .

Very little is known about how people self-organize investigations to achieve learning on longer time scales. Natural environments afford a practically infinite number of activities that a curious learner can in principle investigate. However, given the limited time and resources available for investigation, the learner must carefully select which activity to engage with to enable discovery. Formal treatment of this “strategic student” problem prescribe how learners should allocate study time to maximize learning across a set of the activities 12 , 13 but show that the optimal allocation is very sensitive to the shape of the expected learning trajectory, which is not available to learners in practice 12 .

A common proposal for how people resolve this conundrum is that they prioritize study items based on their perceived difficulty, i.e., their perceived level of knowledge or competence on a task, but the precise form of this prioritization is under debate. Several studies have shown that people prioritize tasks with high difficulty or high uncertainty 14 , 15 . In contrast, an expanding literature proposes that people prefer intermediate difficulty 16 in a range of conditions including curiosity about trivia questions 5 , 17 , choices among sensorimotor activities 18 , infant attention 19 and esthetic appreciation 20 , 21 .

Strategies that prioritize high versus intermediate difficulty activities may have different computational bases and ecological roles. A preference for high difficulty tasks may emerge from computational architectures that assign intrinsic utility to prediction errors or uncertainty, thus motivating agents to venture beyond familiar activities 15 , 22 , 23 , 24 . In contrast, a strategy prioritizing activities with intermediate difficulty may emerge from control architectures based on learning progress (LP) 25 , 26 , 27 , 28 , 29 , 30 that monitor the temporal derivative of performance - e.g., percent correct (PC) - and generate intrinsic rewards for activities in which the agent’s performance changes with practice.

LP-based algorithms are particularly important in naturalistic environments because they allow agents to avoid not only highly familiar tasks but also unlearnable tasks – i.e., activities that are intrinsically random or cannot be mastered with the learners’ current knowledge or skills 30 , 31 , 32 . Unlike PC-based algorithms that steer agents toward tasks of maximum difficulty, LP-based algorithms help to avoid random or too-difficult activities. Moreover, these algorithms provide realistic solutions for optimizing study time allocation - by maximizing the progress that an agent experiences in practice without precise knowledge of one’s future learning curve 12 , 13 - and have been applied to automate curriculum learning in difficult machine learning problems 27 , 33 , 34 and personalize sequences of learning activities in educational technologies 35 , 36 , 37 .

Despite the potential importance of LP-based control strategies, there is no empirical evidence of whether, and how, people use such strategies. In the studies conducted so far, people were asked to estimate the difficulty of study materials based on their familiarity with the topic (e.g., biographical text or foreign vocabulary) 38 . However, no study has tested whether participants can dynamically monitor their performance on an arbitrary activity and use dynamic estimates of PC or its temporal derivative (LP) as predicted by computational algorithms.

Here we examined this question using computational modeling and a behavioral task in which people self-organized their study curricula based on trial-by-trial feedback about their performance on a set of novel activities. We provide direct evidence that humans show bona fide sensitivity to LP – the change in performance on novel activities – which coexists with a sensitivity to PC and steers people away from unlearnable tasks consistent with computational theories.

We analyzed data from 382 participants who performed an online task in which they could freely engage with a set of learning activities (Fig.  1 a). Each trial started with a free-choice panel prompting the participant to choose one of 4 activities depicted as families of "monsters” (Fig.  1 a, (1)). After making a choice, the participant received a randomly drawn member from the chosen family, made a binary guess about which food that member liked to eat (Fig.  1 a, (2)), and received immediate feedback regarding their guess (Fig.  1 a, (3)). To understand how participants self-organized their learning curriculum, we required them to complete 250 trials but did not impose any other constraint on their choice of activity.

a Trial structure during free play. The panels show 3 example free-choice trials consisting of 3 steps each. Each trial began with a choice among 4 "monster families" depicted as visual icons (1). This was followed by the presentation of a randomly drawn individual from that family and a prompt to guess which of two possible foods the individual liked to eat (2). After guessing (2), the participant received immediate feedback (3) and the next trial began. Participants were free to repeat the previously sampled activity (e.g., trial t  + 2 in this figure) or switch to any other monster family (e.g., trial t  + 1) as they wished. b Performance during the forced-choice familiarization stage. Each box plot shows the %correct (PC) during the 15 familiarization trials in which participants had to play each activity for the IG (blue; N  = 186) and EG (red; N  = 196) groups. Horizontal bars inside boxes show the median values across all participants in a group; box boundaries show the 1st and the 3rd quartiles; whiskers show sample minima and maxima. Image credits ( a ): monster character designs by macrovector/Freepik; food-item designs by brgfx/Freepik. Source data for b are provided as a Source Data file.

Our key questions were (1) how people self-organize their exploration over a set of activities of variable difficulty, and (2) whether they spontaneously adopt learning maximization objectives when they do not receive explicit instructions. To examine these questions, we manipulated the difficulty of the available activities as a within-participant variable, and the instructions that participants received as an across-participant variable. Difficulty was controlled by the complexity of the categorization rule governing the food preferences. In the easiest activity (A1), individual monster-family members differed in only one feature and that feature governed their food preference (e.g., a red monster with big flame liked fries and a red monster with small flame liked salad; 1-dimensional categorization). In the next easiest level (A2), family members varied along two features but only one feature determined preference (1-dimensional with an irrelevant feature). In the most difficult learnable activity (A3) food preferences were determined by a conjunction of 2 variable features (2-dimensional categorization). Finally, the 4th activity (A4) was random and unlearnable: individual monsters had two variable features, but their food preferences were assigned randomly each time a new monster was sampled, and were thus unpredictable with either a rule-based or rote memorization strategy.

Learning objectives were manipulated across two randomly selected participant groups. Participants assigned to the “external goal” group (EG; N  = 196) were asked to maximize learning across all the activities and were told that they will be tested at the end of the session. In contrast, participants in the “internal goal” group (IG, N  = 186) were told to choose any activity they wished with no constraint except for completing 250 trials. Except for this difference in instructions (and the fact that the EG group received the announced test), the two groups received identical treatments. Each group started with 15 forced-choice familiarization trials on each activity, followed by a 250-trial free-play stage, and gave several subjective ratings of the activities before and after the free play stage (see Supplementary Fig.  2 ).

Performance on the forced-choice familiarization stage verified that these manipulations worked as intended. The EG and IG groups had equivalent performance during this stage (Fig.  1 b; mixed-design ANOVA on percent correct (PC) with group and difficulty as factors; EG vs IG, F (1, 380) = 1.829, p  = 0.177; group × difficulty interaction, F (3, 1140) = 0.820, p  = 0.483). For both EG and IG participants, performance on each activity was significantly different from all others, suggesting that both groups could use performance feedback as an index of activity difficulty (Fig.  1 b; mixed-design ANOVA, main effect of activity, F (3, 1140) = 158.400, p  < 0.001; post-hoc pairwise Tukey’s HSD tests between all activity levels within each group were significant with all p-values smaller than p  = 0.01). Additional evidence from the ratings obtained at the end of the task showed that the EG and IG groups provided similar retrospective ratings of time spent, progress made and interest in learning activities (Supplementary Fig.  2 ), suggesting that they had equivalent engagement and self-monitoring while performing the task.

Individuals show spontaneous self-challenge independently of instructions

Despite their equivalent learning ability, EG and IG participants showed different choice patterns and substantial individual variability in the extent to which they challenged themselves and mastered the available tasks.

Analysis of group-level activity choices showed that, while the EG group focused strongly on the most difficult activity (the unlearnable activity that had the lowest PC), the IG group showed a more uniform preference with only a slight bias toward the easiest activity (Fig.  2 , a). Across the entire session, the EG group had significant below-chance time allocation to the two easiest activities and above-chance allocation to the random (lowest-PC) activity (relative to 25%; linear model with sum contrasts: A1: 20.61%, t (1520) = −3.002, p  = 0.003; A2: 19.29%; t (1520) = −3.910, p  = 0.048; A4: 36.92%; t (1520) = 8.156, p  < 0.001). In contrast, the IG group had a significant above-chance allocation for the easiest (A1) activity (A1: 33.00%, t (1520) = 5.330, p  < 0.001) while spending less time on other activities (A2: 21.42%;  t (1520) = −2.387, p  = 0.017; A3: 22.16%; p  > 0.05; A4: 23.43%; p  > 0.05; Fig.  2 , a). According to a significant interaction between instruction-group × activity-type interaction, revealed by a 2-way mixed design ANOVA of time allocation, these differences were reliable ( F (3, 1140) = 14.578, p  < 0.001).

a The fraction of participants selecting each learning activity in the EG ( N  = 196) and IG ( N  = 186) groups (respectively, top and bottom panels) as a function of trial number during the free play stage (no smoothing) demonstrate that group differences in choice patterns persisted throughout the task. b Histograms of difficulty-weighted final performance (dwfPC) for each instruction group. The EG group ( N  = 196) achieved better dwfPC scores than the IG group ( N  = 186), but the distributions were broad and overlapping, highlighting important individual variability. The difference between groups was significant with both dwfPC and unweighted average PC scores. Source data are provided as a Source Data file.

Consistent with their higher self-challenge, average learning achieved by the end of the free-play stage was greater in the EG relative to the IG group (Fig.  2 b). A measure of difficulty-weighted final PC (dwfPC: the average PC in the last 15 trials spent on each activity scaled by its difficulty rank (Methods, Difficulty-weighted final performance) was significantly higher for the EG group (M = 0.756, SD = 0.127) relative to the IG group (Fig.  2 b; M = 0.721, SD = 0.126; t (379.4) = 2.679, p  = 0.008, Welch two-sample t -test), and the same result held if we used unweighted average PC (EG: M = 0.787, SD = 0.118; IG M = 0.756, SD = 0.120; t (378.1) = 2.539, p  = 0.011, Welch two-sample t -test).

Notwithstanding these group-level differences, participants showed substantial individual variability and, importantly, a subset of those in the IG group adopted levels of self-challenge similar to the EG group. To investigate this variability we categorized each participant based on the number of activities they mastered to a learning criterion - i.e., whether they mastered 1, 2 or all 3 learnable activities (NAM1, NAM2 or NAM3; see Methods, NAM designation). The dwfPC score within each NAM group was not affected by instructions, showing that the NAM designation effectively captured the variability in learning achievement (Fig.  3 a; pairwise contrasts IG vs. EG conditioned on NAM were nonsignificant, p  > 0.05, at all levels of NAM).

a Final performance was the same across instruction groups when accounting for the number of activities mastered (NAM). As expected, the NAM designation captured well the learning achievement of our participants. In light of ( b ), this demonstrates that many participants achieved a high performance across learning activities, even without an explicit instruction to learn. b Distributions of participants mastering 1, 2, or 3 activities in each instruction group. Whereas half of the participants in the EG group achieved high performance across learnable tasks, a sizable portion of the IG participants (almost 1/3) were motivated enough to self-challenge and learn without being asked to do so. Only 8 participants in the EG and 9 participants in the IG group failed to master even one activity. Thus, 99 participants mastered only 1 activity ( N EG  = 42; N IG  = 57), 126 mastered two ( N EG  = 58; N IG  = 68), and 140 mastered all three ( N EG  = 88; N IG  = 52) ( c ), Time allocation patterns differed by instruction and level of achievement. The three panels show the average time allocation patterns in IG ( N  = 177) and EG ( N  = 188) groups observed over the free-play trials separately for each level of NAM (from left to right, NAM1, NAM2, and NAM3). Circle (EG) and square (IG) symbols represent the average percentage of time spent on an activity in the respective NAM-instruction group; error bars indicate the standard error; the horizontal dashed lines show random time allocation (25%). Time allocation was consistent across the levels of NAM towards harder activities in the EG group. In contrast, only the best learners in the IG group displayed a similar preference, whereas NAM1&2 participants tended towards easier activities. Source data are provided as a Source Data file.

Importantly, despite not being instructed to study for a test, 64.52% of IG participants mastered more than one activity (NAM2 and NAM3) and 29.59% mastered all 3 activities (Fig.  3 b). These percentages were comparable to learning achievements in the EG group, where 74.49% mastered at least 2 activities, and 36.56% mastered all three. The relative proportions of participants at each achievement level were comparable between the two groups across a range of mastery criteria (see Supplementary Fig.  3 , for a detailed analysis). Thus, while changing the criterion modified the number of participants who achieved mastery, it left intact the relative fractions NAM subgroups in the IG and EG groups. This shows that our conclusions are independent from a specific definition of mastery.

While NAM1 and NAM2 participants in the IG group showed choices consistent with the group average – favoring the easiest activity – NAM3 participants showed a distinct preference for A3 and A4 activities that more closely resembled the EG group (Fig.  3 c). Two-way mixed ANOVAs of time allocation showed in the IG group, a marginally significant main effect of activity ( F (3, 525) = 8.847, p  < 0.001) and a highly significant interaction between activity and NAM ( F (3, 525) = 14.791, p  < 0.001). In the EG group there was also a significant main effect of activity ( F (3, 525) = 19.407, p  < 0.001) and a significant interaction with NAM ( F (3, 525) = 7.197, p  < 0.001). As Fig.  3 c shows, while participants in NAM1 and NAM2 groups differed in activity selection across the instruction conditions, those who mastered all 3 learnable activities allocated their time similarly. Importantly, a sizeable fraction of the IG group behaved in the same way as people who were instructed to learn and prepare for a test.

To further examine the relationship between learning achievement and activity choices, we created an index of self-challenge (SC) measuring the extent to which each participant tended to challenge themselves. This index was defined as the recent PC of the activity selected on each trial, normalized to the entire range of PC levels the participant experienced so far (Methods, Self-challenge index). Thus, SC values close to 0 denote participants who tended to choose the easiest of the activities they experienced; SC close to 1 denote participants who tended to choose the most difficult activities; and SC near 0.5 denote participants who preferred activities of intermediate difficulty. Supplementary analysis verified that the SC index is a more efficient measure of the tendency to choose challenging activities compared to simple contrasts between pairs of activities (Supplementary Fig.  4 ).

Plotting dwfPC versus SC reveals two important insights (Fig.  4 ). First, dwfPC has a strong inverted-U relationship with SC, suggesting that the best learning outcomes were associated with intermediate SC. An additive model of dwfPC that included both linear and quadratic SC-index terms (as well as control variables of initial performance and instruction) was superior to its counterpart with only a linear term, Δ AIC  = 11.775). The linear-quadratic model accounted for a significant fraction of variance ( \({R}_{{{{{{{{\rm{adjusted}}}}}}}}}^{2}=0.159,\,F(4,360)=18.238,\,p \, < \, 0.001\) ) and produced a significant negative coefficient for the quadratic term (−0.016, t (360) = −1.966, p  < 0.001). We replicated this finding when we repeated the analysis using unweighted final PC scores ( \({R}_{{{{{{{{\rm{adjusted}}}}}}}}}^{2}=0.191,\,F(4,360)=13.642,\,p \, < \, 0.001\) , with the coefficient for the quadratic term = −0.017, t (360) = −3.561, p  = 0.007) and when we replaced SC with pairwise contrast of activity choices (Supplementary Fig.  4 b) showing that the finding was not an artefact of the specific ways we measured PC or SC.

The scatter plot shows the difficulty-weighted final score (dwfPC; y -axis) as a function of the self-challenge index (SC; x -axis). Each point is one participant. Colors indicate the number of activities mastered: NAM1, N  = 99 ( N EG  = 42; N IG  = 57); NAM2, N  = 126 ( N EG  = 58; N IG  = 68); and NAM3, N  = 140 ( N EG  = 88; N IG  = 52); filled and unfilled circles indicate, respectively, EG ( N  = 188) and IG ( N  = 177) groups. The black curve shows the line of best fit from a linear-quadratic regression model, with 95% confidence intervals represented by the strip surrounded by black dashed lines. The marginal histograms on the top show the distributions of SC scores for each NAM (color) and group (solid and dashed traces). SC was higher for EG relative to IG groups in participants who mastered only 1 or 2 activities (NAM1 and NAM2), and was equivalent, with intermediate values, for participants who mastered all 3 activities (NAM3; top histogram). Source data are provided as a Source Data file.

Second, participants with different instructions and learning achievement fell on different portions of the inverted-U curve. Participants who did not master all 3 activities (NAM1 and NAM2) fell on the rising and falling arms of the inverted-U curve if they were in, respectively, the IG or the EG group (Fig.  4 ). These participants had equivalent dwfPC but higher SC in the EG relative to the IG group (multiplicative linear model; NAM1, t (359) = 2.856, p  = 0.005; NAM2 ( t (359) = 4.377, p  < 0.001; Tukey’s HSD; see the marginal histograms in Fig.  4 ). Thus, EG participants who failed to master all 3 tasks did so because they over-challenged themselves and those in the IG group did so because they under-challenged themselves. In contrast, participants who mastered all 3 activities were at the top of the inverted-U curve and had equivalent (intermediate) SC in the IG and EG groups (Fig.  3 c; no significant pairwise contrasts between EG and IG for NAM3, t (359) = 1.236, p  = 0.217; see the top marginal histogram). Thus, consistent with the activity preferences (Fig.  3 c): a subset of participants spontaneously adopted intermediate self-challenge strategies and maximized learning regardless of external instructions.

Computational modeling and sensitivity to LP

While empirical studies demonstrate preferences for activities of intermediate complexity, they have yet to report specific sensitivity to LP. One study 38 reports that people choose study words that are judged to have intermediate difficulty, but did not measure dynamic sensitivity to LP - the change in performance over time - either alone or in combination with PC.

To examine this question, we fit the participants’ activity choices by leveraging the formalism of intrinsically motivated reinforcement learning models 13 , 27 , 29 , 39 . Such models typically include three major components: (1) a space of learning activities, (2) an intrinsic utility function for each activity, associated with a decision-making mechanism, modeling how they are sampled, and (3) a model of learning mechanisms that improve skills after practicing an activity. Here, we already know the space of learning activities and we can observe the evolution of performance as learners engage in the activities. Thus, we can ask which intrinsic utility function could best explain the participants’ choices. To do so, we consider a standard softmax model (in a bandit setting 39 ), in which the utility of an activity is a linear combination of PC and LP:

PC and LP were dynamically evaluated for each activity i at each trial t based on the recent feedback history. PC was defined as the number of correct guesses over the last 15 trials of activity i , and LP was defined as the difference in PC between first versus second parts of the same interval (similar to models of PC and LP used in refs. 29 , 31 , 39 ). We fitted each participants’ data (excluding 8 EG and 9 IG participants who did not master even a single activity) as a probabilistic (softmax) choice over 4 discrete classes, using maximum likelihood estimation with 3 free parameters - the softmax temperature (capturing choice stochasticity) and weights w PC, w LP indicating the extent to which each participant was sensitive to, respectively, PC and LP (Methods, Computational modeling). Supplementary Fig.  5 illustrates the model fitting procedure for an example participant’s data.

The bivariate form of the model that included both LP and PC (Eq. ( 3.1 )) provided a superior fit to the data in both EG and IG groups. The bivariate model average AIC score (M = 491.992, SD = 200.389)) was lower than that of an alternative model based on random selection (M = 693.147; SD = 0; the baseline model yields the same likelihood regardless of participants’ choices; see Methods, Computational modeling, Eq. ( 5.5 )) and, importantly, also outperformed univariate models that included only LP or only PC terms (Fig.  5 a). A 2-way ANOVA of AIC scores showed a significant effect of model form ( F (2, 1089) = 43.992, p  < 0.001), a marginal effect of instruction ( p  = 0.054), but no interaction between model form and EG/IG groups ( p  = 0.716). The bivariate model had the lowest AIC scores in a large majority of participants in both groups (EG: 70.74%; IG: 74.01%). Finally, in each group, the bivariate model had a significantly lower AIC relative to each participant’s next-best model (Wilcoxon signed-rank test, EG: mean difference = 21.503, SD = 41.433; Z (188) = 55, p  < 0.001; IG: mean difference = 21.882, SD = 45.383; Z (177) = 46, p  < 0.001) and was at least 2 AIC points away from the next-best model in a majority of participants (EG: 58.51%; IG: 62.71%).

a The bivariate models had better AIC scores both across and within groups ( N EG  = 188; N IG  = 177), compared to random-choice and univariate baselines univariate models. Box boundaries represent the 1st and the 3rd quartiles, and the lines inside show median scores; whiskers represent the full sample range. The dotted red line shows the AIC of the random-choice model. b Fitted coefficients reproduce choice patterns across instruction and NAM groups. The panels show the average time allocation patterns obtained by simulating activity choices over 250 trials using N  = 500 randomly sampled coefficients from the pool of all fitted bivariate models. c Models of two distinct activity-selection strategies. The top row shows the joint distributions of normalized bivariate-utility coefficients. Subsets of these distributions whose data is presented below are highlighted with solid colors. These subsets were formed by first grouping all fitted models into three segments along \(\hat{w}{{{{{{{\rm{PC}}}}}}}}\) and \(\hat{w}{{{{{{{\rm{LP}}}}}}}}\) , and then selecting groups corresponding to PC-driven and LP-driven profiles. Sample sizes of each subset are shown their respective subpanels. The bottom row shows mean relative frequencies of selecting each activity in the corresponding subset of participants depicted immediately above. LP-driven participants sampled the unlearnable activity (A4) in relative moderation compared the PC-driven group. d LP-driven participants selected allocated time more efficiently for learning and had better learning outcomes. The top row shows fractions of participants in the two groups that reached an objective criterion of 13/15 trials on the hardest learnable activity (A3) at least once in the experiment. The middle row shows the relative preference for activity A4 over A3, defined as the difference between fractions of participants (that still have not mastered A3) who selected A4 minus the fraction selecting A3. The bottom row shows average SC scores in the two groups (shaded regions indicate the standard error). Source data are provided as a Source Data file.

The fact that the bivariate model fits free-choice data better than univariate models provides direct evidence that participants are sensitive to LP – a heuristic for the temporal derivative of PC – above and beyond overall error rates. Importantly, the lack of interaction between model form and instruction shows that participants do not need to be explicitly instructed to maximize learning to demonstrate sensitivity to LP. Additional analyses showed that the PC and LP coefficients remained important even after including a term representing task familiarity (the reciprocal of novelty) in the utility function (see Supplementary Fig.  6 ). As discussed for Fig. S6, we focus on models without the familiarity term because in our task, novelty/familiarity is defined only by past choices and is thus circular if used to model choices. Modeling familiarity accounts for choice autocorrelation, but does not explain it. We note, however, that in computational RL studies 24 , 40 ), measures of competence (like our PC) are used as a proxy for novelty preference that guides agents towards unfamiliar states.

As a final validation of our models, we conducted model simulations of time-allocation using the coefficients fitted by the bivariate models. We simulated activity choices over 250 trials in each NAM and EG/IG group using the observed success rates in conjunction with the fitted coefficients (randomly sampled with replacement over 500 iterations). As shown in Fig.  5 b, the simulations reproduced the main patterns of time allocation, including the preference for activity A4 in the EG and IG NAM3 groups, and the preference for activity A1 in the NAM1 and NAM2 IG groups (see Fig.  3 c for comparison), confirming that the bivariate models captured the main features of the empirical data.

Computational theories suggest that sensitivities to PC and LP will have distinct contributions to activity choices and learning. While a sensitivity to PC can motivate people to learn by steering them away from overly easy activities, a sensitivity to LP may protect them from focusing on overly difficult or impossible activities. Several aspects of the w PC and w LP coefficients in our task support these hypotheses.

First, the w PC and w LP were uncorrelated and showed different effects of instructions, suggesting that they capture different influences on choice strategies. We found no correlation between the w PC and w LP coefficients in the IG group (Pearson correlation of normalized coefficients, IG group: r (186) = −0.077, p  = 0.298); EG group: r (175) = 0.062, p  = 0.399; the normalization procedure is described in Methods, Computational modeling). Moreover, the PC coefficients were on average positive in the IG group and negative in the EG group (consistent with the groups’ relative preferences for easier versus harder activities) while the LP coefficients showed no effects of instructions (mean normalized PC coefficient in IG: M norm  = 0.255, SD = 0.724; in EG: M norm  = −0.232, SD = 0.741; 1-way ANOVA, F (1, 363) = 40.240, p  < 0.001; mean normalized LP coefficient in IG: M norm  = 0.079, SD = 0.640; in EG: M norm  = 0.062, SD = 0.631; 1-way ANOVA, F (1, 363) = 0.065, p  = 0.799).

Additional analyses supported the view that while both PC and LP coefficients correlate with higher self-challenge (Supplementary Fig.  4 c), a sensitivity to LP can steer people away from unlearnable activities. We first conducted a group-level analysis of the correlation between the coefficients and two model-free measures of task choices: the difference between the time devoted to A3 versus easier activities (indexing the tendency to choose more challenging learnable activities) and the difference between the time devoted to activity A4 relative to the other activities (indexing the tendency to choose the unlearnable activity). Across all participants, lower PC coefficients coincided with a preference for choosing both A3 and A4, but \(\hat{w}{{{{{{{\rm{LP}}}}}}}}\) coefficients correlated only with a preference for the learnable, A3 activity (Supplementary Fig.  7 ).

To more closely examine the specific contribution of LP sensitivity we focused on two subsets of participants whose choices were driven predominantly by, respectively, PC or LP. As shown in Fig.  5 (c, top), PC-driven participants had negative PC coefficients but near-zero LP coefficients and LP-driven participants had positive LP coefficients but near-zero PC coefficients (see Methods, Computational modeling for details of the grouping procedure). While both groups preferred more difficult activities (cf. Supplementary Fig.  4 c) the preference for A4 was lower in LP-driven relative to PC-driven participants. Linear regression models of time allocation as a function of activity (A3 or A4) and type of drive showed that PC-driven people engaged in activity A4 more often relative to A3 in both the EG and IG groups (EG: slope = 76.485, t (104) = 7.019, p  < 0.001; IG: slope = 83.941, t (72) = 5.199, p  < 0.001) but this preference was lower or absent in LP-driven participants as shown by its negative interaction with the type of drive (EG: interactionslope = −47.628, t (104) = −2.726, p  < 0.001; IG: interactionslope = −125.179, t (72) = − 5.764, p  < 0.001).

Importantly, the lower preference for A4 enhanced learning outcomes in the LP-driven relative to the PC-driven group. As shown in Fig.  5 d, after approximately trial 80, PC-driven participants showed a prominent increase in choices of A4 in favor of A3 but this was not seen in the LP-driven participants (Fig.  5 d, middle row, captured as a decline in SC in the latter group (Fig.  5 d, bottom row). At around the same time, the fraction of participants mastering A3 in the LP-driven group exceeded that in the PC-driven group (Fig.  5 d, top row). By the end of the free-play stage, the probability of mastering at least 2 activities was 90.48% in the LP-driven group versus 70.59% the PC-driven group, and the probability of mastering all 3 tasks was, respectively, 64.29% versus 34.98%. Thus, consistent with theoretical predictions, LP-driven choices increase the efficiency of active learning by steering participants away from unlearnable activities.

While the ability to self-organize study time is critical for learning success, finding an efficient organization poses a daunting computational challenge. Prominent theories such as the free energy principle postulate that animals are intrinsically motivated to optimize their explanatory models of the environment 10 , 41 . However, the strategies for optimal exploration that are proposed by these theories are limited to highly simplified laboratory conditions while being typically too complex to be computed in real-world situations 42 . Similarly, mathematical models prescribing how students should allocate study time across competing activities show that optimal allocation is strongly sensitive to the precise shape of the learning trajectory, but this shape is typically unknown to the learner in advance 12 .

LP-based algorithms solve this conundrum by generating intrinsic rewards for activities in which learning recently occurred in practice, and thus provide a uniquely powerful means to optimize choices of study activity using a biologically plausible mechanism. And yet, it is unknown whether or how such choice strategies influence human behavior. Here we use a free-choice paradigm in which participants allocate study time based on dynamic feedback history and provide direct empirical evidence that humans are sensitive to LP.

Converging evidence suggests that humans tend to choose activities of intermediate complexity in a range of disparate settings - e.g., when spontaneously allocating visual attention in infancy ( 19 , solving complex cognitive tasks) or declaring esthetic preference 20 , 21 , 43 . Our present results show that the preference for intermediate complexity extends to choices of learning activities (see also ref. 18 ) and, most importantly, that it may be a manifestation of an underlying LP-based mechanism. Thus, the ubiquitous preference for intermediate complexity reported in different settings may reflect an underlying mechanism that steers organisms toward activities that provide learning maximization.

Two major ideas in the literature postulate that exploration is structured based on the learner’s competence (prediction errors or error rates) or, alternatively, based on changes in competence over time (learning progress). However, whereas these strategies are typically framed as mutually exclusive alternatives 25 , 44 , 45 , 46 our findings suggest that these two factors are uncorrelated and can jointly shape activity choices and contribute to different aspects of an investigative policy. A sensitivity to PC – with a preference for higher error rates – motivates people to explore more difficult unfamiliar activities, while a sensitivity to LP - the temporal derivative of PC - allows people to avoid unlearnable activities.

The properties of PC- and LP-based control mechanisms in our data suggests that the relative influence of each type of control may depend on the set of available learning activities. Here we used a relatively simple setting in which the available activities can be quickly mastered, and found that a PC-based strategy strongly contributed to the drive to choose challenging activities rather than stick with already-mastered tasks. However, if the environment is replete with challenging and unlearnable tasks, e.g., during realistic scientific investigation, an LP-based strategy may be more critical for steering learners toward tasks where progress is made as proposed in artificial curiosity 25 , 26 , 29 .

Our results also pertain to the relation between extrinsic and intrinsic motivation - and specifically the debate whether extrinsic rewards bolster 3 or suppress 47 the intrinsic motivation to learn. Our findings suggest that the answer is more complex, as external objectives both enhanced and impaired different aspects of our learners’ study strategy. On one hand, external objectives motivated participants to greater self-challenge, as people who were told to study for a test showed a greater tolerance for errors and better learning outcomes than those who did not. On the other hand, external instructions dampened learning achievement by inducing some participants to labor in vain on a random activity rather than learnable activity.

It is important to note that, while previous studies pitted intrinsic motivation against extrinsic monetary incentives (e.g. 47 ), the extrinsic motivation for the EG group in our task came from the specification of a learning objective. In addition, rather than rewarding participants for individual correct answers, our external instruction specified the end-goal but not the local strategy for achieving the goal; this allowed people to choose their activities and commit errors in the short term, in the interest of maximizing learning in the long term. This greater autonomy, we believe, contributed to the synergism we observed, whereby externally imposed goals enhanced the eventual learning outcomes, rather than hindering them. Our findings support two key postulates of self-determination theory stating that intrinsic and extrinsic motivations are not dichotomous but fall on a continuum, and that a sense of agency is a strong factor that motivates people to internalize and meet externally imposed goals 48 . Thus, the most critical question may not be whether external objectives have beneficial or detrimental effects - but how to balance these objectives to support the investigative strategy that is most efficient in a particular context.

Last but not least, by examining investigations on longer time-scales, our results bear on the increasingly recognized distinction between momentary curiosity and sustained learning and interest 9 , 49 . Beyond the brief satisfaction offered by fleeting (diversive) curiosity, long-term sustained interest, and the willingness to exert sustained effort in pursuit of such interests, can have profound influence on the lifelong acquisition of competence and skills 9 , 50 . Hidi and Renninger 9 , 50 proposed a four-stage model of interest development, whereby situational interests is initially triggered and sustained (or dampened) by the environment but with time gives way to well-developed interest in which people spontaneously generate new questions and initiate investigations 38 . The fact that many people in our IG group mastered two or more tasks and reported subjective interest proportional to their time allocation (Supplementary Fig.  2 ), suggests that the activities we provided may have triggered their situational interest regardless of explicit instructions. The fact that higher achievements were more common in the EG group suggests that external instructions help support that fledgling interest. Thus, important questions for future research concern the relation between the mechanisms by which people self-organize their activities, their subjective feelings of interest and the impact of both factors on the development of lifelong interests and skills.

Finally, the experimental setup implemented in our study allows researchers to fit and evaluate a larger scope of models. In this study we focused exclusively on modeling activity choices while eschewing assumptions about the learning process itself and the potentially complex factors that modulate it (including, e.g., forgetting, switching costs, effort, and preferences for uncertainty). However, follow up work can easily extend the task design to allow for proper examination of these factors, for example by collecting subjective probability ratings to track participants’ evolving inferences regarding each task. Moreover, while our task takes a step towards a more naturalistic lab setting by giving people the freedom to choose their own learning activities it supplies a very limited set of learning activities. Future studies can benefit from the straightforward parametrization of the learning environment (e.g., number of learning activities, difficulty levels, number of response categories, time horizon, etc.) to study how different drives self-organized learning according to context.

Four-hundred participants (including 208 female, 187 male, and 5 participants of undisclosed gender) were recruited for the study on the online platform Amazon Mechanical Turk. Participants were between 19 and 71 years of age, with an average age of 36.15 years, SD = 10.54). All participants provided informed consent. All the procedures were approved by the Institutional Review Board of the University of Rochester.

All participants were told that the experiment will last 45 min to 1 h and, upon completion, they will be compensated $1 regardless of performance. This scheme was consistent with prevailing rates on Amazon MTurk and with our goals of minimizing the role of monetary incentives and avoiding biasing participants toward activities with consistently high performance. All participants were asked to complete the task on their own in a quiet environment and eliminate external distractions (e.g., turn off cell phones, TV sets, music players, etc). After receiving detailed written instructions, each participant completed 4 task modules in sequence: (1) 15 forced-choice familiarization trials with each activity; (2) rating of prospective learnability for each task (see below); (3) a free-play stage with 250 trials of free-choice of activity; (4) 6 additional subjective ratings (see below).

Before delivery of the instruction, participants were randomly assigned to the EG and IG groups, who received identical treatments except for the initial instruction. The IG participants received a task description that did not communicate any expectations or objectives on the part of the experimenters: “In each family there are several individuals, and the appearance of an individual might predict what food they like to eat. When you interact with a monster family, different individuals will be presented to you. For each individual, two food items will be displayed, and you can click on the one you think it prefers. You will receive feedback whether your guess was correct or not”, which was followed by brief descriptions of familiarization, free-choice, and questionnaire stages. The EG participants’ instructions were identical, except for two additional sentences that included an explicit prescription of a learning goal: “In the main section of the task, we ask you to play for 250 trials and try to maximize your learning about all the 4 families ” followed by information on the post-session testing module re-emphasizing their objective: “We will briefly test how well you learned to predict the food preferences within each family”. After the free-play stage, participants in the EG group received the announced test (between steps 3 and 4) consisting of 15 forced-choice trials on each activity. (However, in our analyses we used the last 15 trials on the free-play stage rather than the test data, as the latter were not available for the IG group). Participants in both groups also provided several ratings of the activities, described in detail in Supplementary Fig.  2 .

Data analysis

Statistical analyses were performed in using the R 3.5.0 (relevant libraries include contrast 0.22 , emmeans 1.4 , MASS 7.3.51.5 , tydyverse 1.3.0 and rstatix 0.6.0 ). Data wrangling, data visualizations, and computational modeling were done in Python 3.6 (relevant libraries include matplotlib 3.2.2 and seaborn 0.11.0 for data visualizations; numpy 1.19.0 , scipy 1.5.1 , and pandas 0.24.1 for data wrangling, visualizations, and modeling). Complete lists of Python and R libraries and sub-dependencies is provided in the code repository (see Code availability). All the t -tests reported throughout this article and supplementary information are two-tailed. We excluded a total of 18 participants – 5 in the EG and 13 in the IG group – who did not appear to be sufficiently engaged in the task based on a response bias criterion (see Supplementary Fig.  1 for more details). This criterion measured the participants’ tendency to choose a single response category in each activity (i.e., always guessing the same food item, regardless of the stimulus).

Difficulty-weighted final performance

Difficulty weighted final PC (dwfPC) is a weighted average of each participant’s finalPC(fPC) on the learnable activities over the last 15 trials played on the activity. The weights are equal to the activity rank (1, 2 and 3) divided by the sum of the ranks (6). Thus, dwfPC for participant i is \({{{{{{{{\rm{dwfPC}}}}}}}}}_{i}=\frac{1}{6}{{{{{{{{\rm{fPC}}}}}}}}}_{i,A1}+\frac{1}{3}{{{{{{{{\rm{fPC}}}}}}}}}_{i,A2}+\frac{1}{2}{{{{{{{{\rm{fPC}}}}}}}}}_{i,A3}\) . (Here and in all subsequent analyses we chose a 15-trial time window that was equal to the number of familiarization trials each participant played).

NAM designation

We divided participants into discrete groups based on the number of activities on which they reached a mastery criterion. The data presented in this article are based on a criterion of 13/15 correct trials (86.7% correct), which, in a binomial distribution with discrete outcomes, corresponds to p  = 0.0037 of arising by chance. Additional analyses verified that the conclusions are robust over a range of criteria (see Supplementary Fig.  3 ). Ten participants (5/154 in the IG group and 5/176 in the EG group) did not master any activity and were excluded from NAM-related analyses and computational modeling.

Self-challenge index

For each participant, we defined a self-challenge (SC) index for each trial t and activity i as:

where PC t , i is the recent PC of the selected activity the participant selected on trial t (measured over the last 15 trials on that activity, including familiarization trials) and where \({\min }_{\forall k\in K}{{{{{{{{\rm{PC}}}}}}}}}_{:t,k}\) and \({\max }_{\forall k\in K}{{{{{{{{\rm{PC}}}}}}}}}_{:t,k}\) are the minimum and maximum PC experienced by the participant over the entire set of trials (including both free- and forced choice) prior to trial t and over the entire set of activities K . Thus, SC values close to 1 indicate a tendency to select activities that yield the minimum PC (“over-challenging”) and values closer to 0 indicate a tendency to select activities with the highest PC (“under-challenging”). To get a single SC index for each participant, we averaged each participants’ the trial-wise SC scores across the entire free-play stage. Supplementary analyses verified that the SC index was a better, more concise measure of the preference for challenging tasks relative to the pairwise preferences between different combinations of activities (see Supplementary Fig.  4 ).

Computational modeling

To understand which intrinsic utility function could best explain the task sampling behavior, we consider a model in the bandit setting ( 39 ), where an intrinsic utility function for each task, measuring its value, is used to decide which task to sample probabilistically. The sampling mechanism used here is the softmax function, following prior models of human decision making in RL and bandit settings 51 . This softmax function simultaneously translates the underlying choice utilities into selection probabilities and scales the correspondence between utility and probability:

U i is the subjective value of choice i , and k indexes the utilities of all items in the set of available activities K (including i ); the parameter τ , known as temperature, controls how strongly the item values determine the probability of their selection. U was defined for each trial as described in the Results section (Computational modeling and sensitivity to LP), as a linear combination of two quantities that represent two aspects of learning: competence and change in competence. Both signals were defined for a retrospective time window of the last 15 trials played on the activity chosen at trial i (including familiarization trials early in the free-play epoch):

where \({y}_{t^{\prime} }\) equals 1 or 0 if the participant guessed, respectively, correctly or in error at time \(t^{\prime}\) . Hence, PC was defined as the proportion of correct guesses over the last 15 trials, while LP was defined as the absolute value of the difference in PC over the first 10 and the last 9 of the same stretch of 15 trials. This implementation of PC and LP signals is similar to machine learning models in refs. 29 , 31 , 39 . In particular, one follows these computational approaches in using the absolute value of LP, which was shown to enable learners to detect tasks where performances decrease, e.g., due to forgetting, and re-gain interest to re-focus on them 29 .

An individual set of parameters was estimated for each participant by minimizing the negative sum of log likelihood values over the free play trials (see ref. 52 ). Assuming that choice probabilities on a trial come from a categorical probability distribution, the likelihood of a model equals the probability (provided by the model) of the observed choice. The categorical distribution is a special case of the multinomial probability distribution, which provides the probabilities of K discrete outcomes in a single sample. Thus, the likelihood of a model that predicts choices with probabilities p t is:

where p t is a vector of probabilities at time t associated with K items indexed by j , and the term [ j  =  i ] evaluates to 1 when i is the activity that was chosen and to 0 otherwise. Thus, at the level of a single trial, higher likelihood is attributed to the model that assigns higher utility to the option chosen on the subsequent trial. For two and more trials, the likelihood of a model increases with the utility of the observed choices across trials. Therefore, in a maximum-likelihood model, a highly positive coefficient for a given learning signal reflects a tendency to choose options with higher values along that signal. Conversely, a highly negative coefficient for a feature indicates a tendency to choose options that have lower values along that feature, while coefficients close to zero reflect the indifference to the feature. The total likelihood of observing all choices from a participant is given by the product of likelihoods from individual trials, \(\mathop{\prod }\nolimits_{t}^{T}L({{{{{{{{\bf{p}}}}}}}}}_{t}| {{{{{{{\rm{choice}}}}}}}})\) . We take a logarithm of each individual trial’s likelihood value in order to compute the overall model likelihood per individual as the sum of single-trial log likelihoods, \(\mathop{\sum }\nolimits_{t}^{T}{{{{{{\mathrm{log}}}}}}}\,L({{{{{{{{\bf{p}}}}}}}}}_{t}| {{{{{{{\rm{choice}}}}}}}})\) , rather than their product. Finally, we maximized this summed likelihood by minimizing its negative value using the L-BFGS-B nonlinear numerical optimization method 53 .

Values of the estimated parameters vary not only due to different choice data between participants, but also as a function of initialization of starting values in the parameter space. Because of this variability, we estimated a model multiple times for each participant using different parameter initializations for every fit, until a convergence criterion was reached. The utility parameters were initialized from a random uniform distribution between −1 and 1, and softmax temperature was randomly sampled from [0, 100]. Convergence was reached by repeatedly fitting a model with different random initializations until 50 maximum likelihood models were found. Concretely, the algorithm updated the current "best model" each time a model better the current best was found, and stopped when it found a model just as good as the current best 50 times.

For analyses of the relation between the coefficients, instructions and choices, we normalized each coefficient pair [ w PC, w LP] by their Euclidean norm, allowing us to interpret the coefficients as relative preferences for PC and LP, respectively.

To select participants driven predominantly by PC or LP (Fig.  5 c, d), we categorized all participants into equally-spaced bins (bin 1  = [−1.00, −0.33); bin 2  = [−0.33, 0.33); bin 3  = [0.33, 1.00]) along each (normalized) coefficient. The PC-driven group (Fig.  5 c, left) had negative PC coefficients but near-zero influence of LP (intersection of bin 1 along PC and bin 2 along LP i.e., \({\hat{w}}_{{{{{{{{\rm{LP}}}}}}}}}\approx 0,\,{\hat{w}}_{{{{{{{{\rm{PC}}}}}}}}}\approx -1\) ), while the LP-driven group (Fig.  5 c, right) had a high preference for LP but little preference to PC ( \({\hat{w}}_{{{{{{{{\rm{LP}}}}}}}}}\approx 1,\,{\hat{w}}_{{{{{{{{\rm{PC}}}}}}}}}\approx 0\) ).

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated and/or analyzed during the current study are available in the Open Science Framework public repository, https://osf.io/k2yur/ , which includes datasets derived from raw data as well as the raw data themselves. All source data used for visualizations and analyses are also provided with this paper as a Source Data file.  Source data are provided with this paper.

Code availability

We share all the code used in quantitative analyses (including visualizations) and computation modeling on GitHub, https://github.com/flowersteam/Humans-monitor-LP . A permanent version of the code repository for the figures and analyses is archived on Zenodo https://doi.org/10.5281/zenodo.5179939 . All source code usef for visualizations and analyses is also provided with this paper as a Source Code file.

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Acknowledgements

The work was supported by a Human Frontiers Team Grant to J.G. and PY.O. We acknowledge the generous support of Celeste Kidd and Amanda Yung in implementing the experiments on Amazon Mechanical Turk.

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J.G. and PY.O. designed the experiment and supervised data collection. A.T., P.K. and J.G. analyzed the data with assistance from PY.O. A.T. and PY.O. designed the computer model with assistance from J.G. A.T., PY.O. and J.G. wrote the manuscript.

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Ten, A., Kaushik, P., Oudeyer, PY. et al. Humans monitor learning progress in curiosity-driven exploration. Nat Commun 12 , 5972 (2021). https://doi.org/10.1038/s41467-021-26196-w

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In the early portion of the twentieth century, nuclear power became the most fascinating technological breakthrough since the invention of the horseless carriage. With untapped potential, nuclear power could have become the leading source of energy for mankind, but once it was developed into a weapon, people became afraid. From then this point on, people started looking closer into how advances in technology can affect the lives of millions of people. There is a vocal minority of people who decide against their children being vaccinated because they think the medicine will give their child autism or other adverse effects on their lives. However, when it comes down to it, humans are astounding animals that adapt to all situations they

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As the world continues to grow each and every day new discoveries are made. Discoveries are made regarding new illnesses and infectious diseases that can infect humans and also be life taking. Along with the discoveries of illness and infectious diseases there are new medicine discoveries. These new medicine discoveries are helping the human race stay healthier longer. The reason people are able to live healthier longer lives is because the new medicine discoveries are making people immune to illnesses and infectious diseases. The new medicine discoveries are for everyone but mainly for the newest generations brought into today. Theses new medicine discoveries are turned into immunizations or vaccines, which are given to humans who want them.

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Wherever there is vaccination there is some number of people who oppose it. The first step to identifying a solution to a problem in understanding how it is caused. Public support for vaccinations had been at an all time high of 95%, before the anti-vaccine movement swept across the United States. The new anti-vaccine movement in the United States can be attributed to the wide use of technology to spread ideas,

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Measles. Polio. Smallpox. The flu. Imagine the world when vaccines were yet to be created. There was a time when people lived in fear of dreadful diseases. Thanks to the introduction of vaccines, many of those devastating diseases have been nearly or completely wiped out. Despite these results, for some people, the question remains: should we vaccinate? Today, I will be discussing the development of the first vaccine, global benefits, and the anti-vaccine movement.

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Imagine going to the doctor’s office and as you walk in, you see the doctor smoking a cigarette! The doctor continues to check you and gives you medicine that was made in the 1900s. Most people would agree that changes in scientific knowledge is for the best, but some people just won’t allow for change. For example, some people think that the Earth is flat, notwithstanding all the evidence put against them. As scientific knowledge changes over time, society has adapted to the new knowledge for the better. For instance, we have medical knowledge. If medical knowledge didn’t change, we wouldn’t know how to make new medicine. Some people like to keep to the older ways like smoking. Once in a while, there comes someone who won’t use any medicine

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During the 20th century, the infectious disease death rate decreased from 800/1000 deaths to less than 100/1000 deaths. This is mainly due to the introduction of immunisation. Vaccination has clearly prevented millions of deaths over the last century; nevertheless, the anti-vaccination movement has grown significantly in recent years. Some of the reasons why people join this movement include the belief that vaccines don’t actually work, the belief that vaccines are unnatural and therefore unhealthy and the belief that vaccines contain toxins that cause bodily damage and neuropsychiatric problems (eg. Autism). This essay will discredit the beliefs associated with the anti vaccination movement through infectious disease statistics,

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Essay on Science in Society

Within the last century scientific discovery has been growing at an exponential rate. Evolution, genetics, physics, and chemistry have all greatly affected the way people view the universe and human role in it. Furthermore, the application of scientific discoveries has physically changed society. For example, humans went from being flightless to eighty years later having transportation in super sonic jets available. Rapid scientific change has caused many issues surrounding morality and science to arise. The idea behind the skepticism is that just because something can be done doesnt mean it should be. Nuclear weapons, biological weapons, and cloning have all fallen under fire due to this concept. People worry that

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Home — Essay Samples — Life — Curiosity — A Personal Narrative About How Far Curiosity Can Take You in Life

A Personal Narrative About How Far Curiosity Can Take You in Life

• Categories: Curiosity

About this sample

Words: 474 |

Published: Nov 20, 2018

Words: 474 | Page: 1 | 3 min read

Works Cited

• Einstein, A. (1955). Ideas and Opinions. New York, NY: Crown Publishers.
• Bhanot, N., & Leos, M. (2018). Curiosity and Its Role in Learning. In J. J. Huet, S. D. Scherer, & M. K. Trundle (Eds.), Interdisciplinary Approaches to Curriculum: Themes for Teaching and Learning (pp. 55-65). Rotterdam, Netherlands: Sense Publishers.
• Deci, E. L., & Ryan, R. M. (2000). The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227-268. doi:10.1207/S15327965PLI1104_01
• Dweck, C. S. (2016). Mindset: The New Psychology of Success. New York, NY: Ballantine Books.
• Deci, E. L., & Ryan, R. M. (2017). Self-Determination Theory: A Macrotheory of Human Motivation, Development, and Health. Canadian Psychology/Psychologie Canadienne, 58(2), 182-195. doi:10.1037/cap0000087
• Csikszentmihalyi, M. (1997). Finding Flow: The Psychology of Engagement with Everyday Life. New York, NY: Basic Books.
• Pink, D. H. (2009). Drive: The Surprising Truth About What Motivates Us. New York, NY: Riverhead Books.
• Duckworth, A. L. (2016). Grit: The Power of Passion and Perseverance. New York, NY: Scribner.
• Kashdan, T. B., & Yuen, M. (2007). Whether Highly Curious Students Thrive Academically Depends on Perceived Autonomy Support and Classroom Engagement. Journal of Educational Psychology, 99(2), 597-610. doi:10.1037/0022-0663.99.2.597
• Dewey, J. (1916). Democracy and Education: An Introduction to the Philosophy of Education. New York, NY: Macmillan.

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• The Joy of Human Curiosity

The Joy of Human Curiosity - Essay Example

• Subject: Philosophy
• Type: Essay
• Level: College
• Pages: 8 (2000 words)
• Downloads: 1
• Author: shaunreinger

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