a literature review of reaction time

• Corpus ID: 17883851

A Literature Review on Reaction Time Kinds of Reaction Time Experiments

• R. Kosinski
• Published 2012

236 Citations

Brain reaction times. from individual to collective description, exploring the complexities of experimental design: using an on-line reaction time program as a teaching tool for diverse student populations, exploration of reaction time: ideas for an inquiry investigation in physics education, the effect of tactile imagery training on reaction time in healthy participants, simple visual reaction time in students of academy of c riminalistic, standardization measurement of reaction time in translation equivalence task of arabic english bilingual speakers, a comparative study of simple auditory reaction time in blind (congenitally) and sighted subjects, emotional theory of rationality, electrophysiological and psychophysical studies of the effects of the duration of presentation of textures on recognition thresholds, an investigation of the difference in reaction time to visual and auditory stimuli in two groups of patients with multiple sclerosis and healthy people, 131 references, effective analysis of reaction time data, reaction time and chronological age, motor processes in simple, go/no-go, and choice reaction time tasks: a psychophysiological analysis., reaction times and intelligence differences: a population-based cohort study, dissociating the effects of automatic activation and explicit expectancy on reaction times in a simple associative learning task., response times: their role in inferring elementary mental organization, sex differences in simple visual reaction time: a historical meta-analysis, alcohol impairs the cognitive component of reaction time to an omitted stimulus: a replication and an extension., gender differences in choice reaction time: evidence for differential strategies., neuroticism as mental noise: a relation between neuroticism and reaction time standard deviations., related papers.

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A Literature Review on Reaction Time by Robert J. Kosinski Clemson University

Reaction time has a been a favorite subject of experimental psychologists since the middle of the nineteenth century. However, most studies ask questions about the organization of the brain, so the authors spend a lot of time trying to determine if the results conform to some mathematical model of brain activity. This makes these papers hard to understand for the beginning student. In this review, I have ignored these brain organization questions and summarized the major literature conclusions that are applicable to undergraduate laboratories using my Reaction Time software.

I hope this review helps you write a good report on your reaction time experiment. I also apologize to reaction time researchers for omissions and oversimplifications.

Simple vs. Recognition vs. Choice Experiments

Number of possible valid stimuli

Stimulus Intensity

Other Factors Influencing Reaction Time  

Left or right hand

Direct vs. peripheral vision

Practice and errors

Distraction

Impairment by Alcohol

Order of presentation

Breathing cycle

Finger tremors

Personality type

Stimulant drugs

Intelligence

Brain injury

Illness  

Psychologists have named three basic kinds of reaction time experiments (Luce, 1986; Welford, 1980):

In simple reaction time experiments, there is only one stimulus and one response. 'X at a known location,' 'spot the dot,' and 'reaction to sound' all measure simple reaction time.

In recognition reaction time experiments, there are some stimuli that should be responded to (the 'memory set'), and others that should get no response (the 'distractor set'). There is still only one correct response. 'Symbol recognition' and 'tone recognition' are both recognition experiments.

In choice reaction time experiments, the user must give a response that corresponds to the stimulus, such as pressing a key corresponding to a letter if the letter appears on the screen. The Reaction Time program does not use this type of experiment because the response is always pressing the spacebar.

By the way, professional psychologists doing these experiments typically employ about 20 people doing 100-200 reaction times each...per treatment (Luce, 1986, Ch. 6)! Sanders (1998, p. 23) recommends an adequate period of practice, and then collection of 300 reaction times per person. Our experiments of 3 or 4 people doing 10 reaction times each are very small.

Mean Reaction Times

For about 120 years, the accepted figures for mean simple reaction times for college-age individuals have been about 190 ms (0.19 sec) for light stimuli and about 160 ms for sound stimuli (Galton, 1899; Fieandt et al. , 1956; Welford, 1980; Brebner and Welford, 1980).

Simple vs. Recognition vs. Choice Reaction Times

The pioneer reaction time study was that of Donders (1868). He showed that a simple reaction time is shorter than a recognition reaction time, and that the choice reaction time is longest of all. Laming (1968) concluded that simple reaction times averaged 220 msec but recognition reaction times averaged 384 msec. This is in line with many studies concluding that a complex stimulus (e.g., several letters in symbol recognition vs. one letter) elicits a slower reaction time (Brebner and Welford, 1980; Teichner and Krebs, 1974; Luce, 1986). An example very much like our experiment was reported by Surwillo (1973), in which reaction was faster when a single tone sounded than when either a high or a low tone sounded and the subject was supposed to react only when the high tone sounded.

Miller and Low (2001) determined that the time for motor preparation (e.g., tensing muscles) and motor response (in this case, pressing the spacebar) was the same in all three types of reaction time test, implying that the differences in reaction time are due to processing time.

Numer of possible valid stimuli. Several investigators have looked at the effect of increasing the number of possible stimuli in recognition and choice experiments. Hick (1952) found that in choice reaction time experiments, response was proportional to log(N), where N is the number of different possible stimuli. In other words, reaction time rises with N, but once N gets large, reaction time no longer increases so much as when N was small. This relationship is called "Hick's Law." Sternberg (1969) maintained that in recognition experiments, as the number of items in the memory set increases, the reaction time rises proportionately (that is, proportional to N, not to log N). Reaction times ranged from 420 msec for 1 valid stimulus (such as one letter in symbol recognition) to 630 msec for 6 valid stimuli, increasing by about 40 msec every time another item was added to the memory set. Nickerson (1972) reviewed several recognition studies and agreed with these results.

Type of Stimulus

Many researchers have confirmed that reaction to sound is faster than reaction to light, with mean auditory reaction times being 140-160 msec and visual reaction times being 180-200 msec (Galton, 1899; Woodworth and Schlosberg, 1954; Fieandt et al. , 1956; Welford, 1980; Brebner and Welford, 1980). Perhaps this is because an auditory stimulus only takes 8-10 msec to reach the brain (Kemp et al. , 1973), but a visual stimulus takes 20-40 msec (Marshall et al. , 1943). Reaction time to touch is intermediate, at 155 msec (Robinson, 1934). Differences in reaction time between these types of stimuli persist whether the subject is asked to make a simple response or a complex response (Sanders, 1998, p. 114).  

Froeberg (1907) found that visual stimuli that are longer in duration elicit faster reaction times, and Wells (1913) got the same result for auditory stimuli.

Piéron (1920) and Luce (1986) reported that the weaker the stimulus (such as a very faint light) is, the longer the reaction time is. However, after the stimulus gets to a certain strength, reaction time becomes constant. In other words, the relationship is:    

Kohfeld (1971) found that the difference between reaction time to light and sound could be eliminated if a sufficiently high stimulus intensity was used.  

If variation caused by the type of reaction time experiment, type of stimulus, and stimulus intensity are ignored, there are still many factors affecting reaction time.

Arousal. One of the most investigated factors affecting reaction time is 'arousal' or state of attention, including muscular tension. Reaction time is fastest with an intermediate level of arousal, and deteriorates when the subject is either too relaxed or too tense (Welford, 1980; Broadbent, 1971; Freeman, 1933). That is, reaction time responds to arousal as follows:    

Etnyre and Kinugasa (2002) found that subjects who had to react to an auditory stimulus by extending their leg had faster reaction times if they performed a 3 second isometric contraction of the leg muscles prior to the stimulus. You might expect that the muscle contraction itself would be faster (because the muscle was warmed up, etc.), but what was surprising was that the precontraction part of the reaction time was shorter too. It was as if the isometric contraction allowed the brain to work faster.  

Age. Reaction time shortens from infancy into the late 20s, then increases slowly until the 50s and 60s, and then lengthens faster as the person gets into his 70s and beyond (Welford, 1977; Jevas and Yan, 2001; Luchies et al. , 2002; Rose et al. , 2002). Luchies et al. (2002) also reported that this age effect was more marked for complex reaction time tasks. Reaction time also becomes more variable with age (Hultsch et al. , 2002). Welford (1980) speculates on the reason for slowing reaction time with age. It is not just simple mechanical factors like the speed of nervous conduction. It may be the tendency of older people to be more careful and monitor their responses more thoroughly (Botwinick, 1966). When troubled by a distraction, older people also tend to devote their exclusive attention to one stimulus, and ignore another stimulus, more completely than younger people (Redfern et al. , 2002). Lajoie and Gallagher (2004) found that old people who tend to fall in nursing homes had a significantly slower reaction time than those that did not tend to fall. An early study (Galton, 1899) reported that for teenagers (15-19) mean reaction times were 187 msec for light stimuli and 158 ms for sound stimuli. Reaction times may be getting slower, because we hardly ever see a Clemson freshman (or professor) who is that fast.

Gender. At the risk of being politically incorrect, in almost every age group, males have faster reaction times than females, and female disadvantage is not reduced by practice (Noble et al. , 1964; Welford, 1980; Adam et al. , 1999; Dane and Erzurumlugoglu, 2003). Bellis (1933) reported that mean time to press a key in response to a light was 220 msec for males and 260 msec for females; for sound the difference was 190 msec (males) to 200 msec (females). In comparison, Engel (1972) reported a reaction time to sound of 227 msec (male) to 242 msec (female). Botwinick and Thompson (1966) found that almost all of the male-female difference was accounted for by the lag between the presentation of the stimulus and the beginning of muscle contraction. Muscle contraction times were the same for males and females. In a surprising finding, Szinnai et al. (2005) found that gradual dehydration (loss of 2.6% of body weight over a 7-day period) caused females to have lengthened choice reaction time, but males to have shortened choice reaction times. Adam et al. ( 1999) reported that males use a more complex strategy than females. Barral and Debu (2004) found that while men were faster than women at aiming at a target, the women were more accurate. Jevas and Yan (2001) reported that age-related deterioration in reaction time was the same in men and women.

Left vs. right hand. The hemispheres of the cerebrum are specialized for different tasks. The left hemisphere is regarded as the verbal and logical brain, and the right hemisphere is thought to govern creativity and spatial relations, among other things. Also, the right hemisphere controls the left hand, and the left hemisphere controls the right hand. This has made researchers think that the left hand should be faster at reaction times involving spatial relationships (such as pointing at a target). The results of Boulinquez and Bartélémy (2000) and Bartélémy and Boulinquez (2001 and 2002) all supported this idea. Dane and Erzurumluoglu (2003) found that in handball players, the left-handed people were faster than right-handed people when the test involved the left hand, but there was no difference between the reaction times of the right and left handers when using the right hand. Finally, although right-handed male handball players had faster reaction times than right-handed women, there was no such sexual difference between left-handed men and women. The authors concluded that left-handed people have an inherent reaction time advantage. In an experiment using a computer mouse, Peters and Ivanoff (1999) found that right-handed people were faster with their right hand (as expected), but left-handed people were equally fast with both hands. The preferred hand was generally faster. However, the reaction time advantage of the preferred over the non-preferred hands was so small that they recommended alternating hands when using a mouse. Bryden (2002), using right-handed people only, found that task difficulty did not affect the reaction time difference between the left and right hands.

Direct vs. Peripheral Vision. Brebner and Welford (1980) cite literature that shows that visual stimuli perceived by different portions of the eye produce different reaction times. The fastest reaction time comes when a stimulus is seen by the cones (when the person is looking right at the stimulus). If the stimulus is picked up by rods (around the edge of the eye), the reaction is slower. Ando et al. , 2002 found that practice on a visual stimulus in central vision shortened the reaction time to a stimulus in peripheral vision, and vice versa.

Practice and Errors. Sanders (1998, p. 21) cited studies showing that when subjects are new to a reaction time task, their reaction times are less consistent than when they've had an adequate amount of practice. Also, if a subject makes an error (like pressing the spacebar before the stimulus is presented), subsequent reaction times are slower, as if the subject is being more cautious. Ando et al. (2002)  found that reaction time to a visual stimulus decreased with three weeks of practice, and the same research team (2004) reported that the effects of practice last for at least three weeks. Rogers et al. (2003) found that training older people to resist falls by stepping out to stabilize themselves did improve their reaction time.

Fatigue. Welford (1968, 1980) found that reaction time gets slower when the subject is fatigued. Singleton (1953) observed that this deterioration due to fatigue is more marked when the reaction time task is complicated than when it is simple. Mental fatigue, especially sleepiness, has the greatest effect. Kroll (1973) found no effect of purely muscular fatigue on reaction time. Philip et al. (2004) found that 24 hours of sleep deprivation lengthened the reaction times of 20-25 year old subjects, but had no effect on the reaction times of 52-63 year old subjects. Takahashi et al. (2004) studied workers who were allowed to take a short nap on the job, and found that although the workers thought the nap had improved their alertness, there was no effect on choice reaction time.  

Fasting. Three days without food does not decrease reaction time, although it does impair capacity to do work (Gutierrez et al. , 2001).

Distraction. Welford (1980) and Broadbent (1971) reviewed studies showing that distractions increase reaction time. Richard et al. (2002) and Lee et al. (2001) found that college students given a simulated driving task had longer reaction times when given a simultaneous auditory task. They drew conclusions about the safety effects of driving while using a cellular phone or voice-based e-mail. Redfern et al. (2002) found that subjects strapped to a platform that periodically changed orientation had slowed reaction time before and during platform movement. The reaction time to auditory stiimuli was more affected than response to visual stimuli. 

Warnings of Impending Stimuli. Brebner and Welford (1980) report that reaction times are faster when the subject has been warned that a stimulus will arrive soon. In the Reaction Time program, the delay is never more than about 3 sec, but these authors report that even giving 5 minutes of warning helps. Bertelson (1967) found that as long as the warning was longer than about 0.2 sec., the shorter the warning was, the faster reaction time was. This effect probably occurs because attention and muscular tension cannot be maintained at a high level for more than a few seconds (Gottsdanker, 1975). 

Warnings about Impairment by Alcohol. Fillmore and Blackburn (2002) found that subjects who had drunk an impairing dose of alcohol reacted faster when they were warned that this was enough alcohol to slow their reaction time. Unwarned subjects who drank suffered more decreased reaction times. However, the warned subjects were also less inhibited and careful in their responses. Even subjects who drank some nonalcoholic beverage and then were warned (falsely) about impairment by alcohol reacted faster than unwarned subjects who drank the same beverage. 

Order of Presentation. Welford (1980), Laming (1968) and Sanders (1998) observed that when there are several types of stimuli, reaction time will be faster where there is a 'run' of several identical stimuli than when the different types of stimuli appear in mixed order. This is called the "sequential effect." Hsieh (2002) found that the shifting of attention between two different types of tasks caused an increase in reaction time to both tasks. 

Breathing Cycle. Buchsbaum and Calloway (1965) found that reaction time was faster when the stimulus occurred during expiration than during inspiration.

Finger Tremors. Brebner and Welford (1980) report that fingers tremble up and down at the rate of 8-10 cycles/sec, and reaction times are faster if the reaction occurs when the finger is already on the 'downswing' part of the tremor. 

Personality Type. Brebner (1980) found that extroverted personality types had faster reaction times, and Welford (1980) and Nettelbeck (1973) said that anxious personality types had faster reaction times. Lenzenweger (2001) found that the reaction times of schizophrenics was slower than those of normal people, but their error rates were the same. Robinson and Tamir (2005) found that neurotic college students had more variable reaction times than their more stable peers. 

Exercise. Exercise can affect reaction time. Welford (1980) found that physically fit subjects had faster reaction times, and both Levitt and Gutin (1971) and Sjoberg (1975) showed that subjects had the fastest reaction times when they were exercising sufficiently to produce a heartrate of 115 beats per minute. Kashihara and Nakahara (2005) found that vigorous exercise did improve choice reaction time, but only for the first 8 minutes after exercise. Exercise had no effect on the percent of correct choices the subjects made. On the other hand, McMorris et al. (2000) found no effect of exercise on reaction time in a test of soccer skill, and Lemmink and Visscher (2005) found that choice reaction time and error rate in soccer players were not affected by exercise on a stationary bicycle. Collardeau  et al. (2001) found no post-exercise effect in runners, but did find that exercise improved reaction time during the exercise. They attributed this to increased arousal during the exercise.

Punishment . Shocking a subject when he reacts slowly does shorten reaction time (Johanson, 1922; Weiss, 1965). Simply making the subject feel anxious about his performance has the same effect, at least on simple reaction time tasks (Panayiotou, 2004). 

Stimulant Drugs. Caffeine has often been studied in connection with reaction time. Lorist and Snel (1997) found that moderate doses of caffeine decreased the time it took subjects to find a target stimulus and to prepare a response for a complex reaction time task. Durlach et al. (2002) found that the amount of caffeine in one cup of coffee did reduce reaction time and increase ability to resist distraction, and did so within minutes after consumption. McLellan et al. (2005) found that soldiers in simulated urban combat maintained their marksmanship skills and their reaction times through a prolonged period without sleep better when given caffeine. Liguori et al. (2001) found that caffeine can reduce the slowing effect of alcohol on reaction time, but can't prevent other effects such as body sway. On the other hand, Linder (2001), using our software and a "Spot-the-Dot" test, found that drinking one can of either a caffeinated or a caffeine-free cola had no detectable effect on reaction time. Kleemeier et al. (1956) found that administering an amphetamine-like drug to a group of elderly men did not make their reaction times faster, although it did make their physical responses more vigorous. 

Intelligence. The tenuous link between intelligence and reaction time is reviewed in Deary et al. (2001). Serious mental retardation produces slower and more variable reaction times. Among people of normal intelligence, there is a slight tendency for more intelligent people to have faster reaction times, but there is much variation between people of similar intelligence (Nettelbeck, 1980). The speed advantage of more intelligent people is greatest on tests requiring complex responses (Schweitzer, 2001).

Brain Injury. As might be expected, brain injury slows reaction time, but different types of responses are slowed to different degrees (reviewed in Bashore and Ridderinkhof, 2002). Collins et al. (2003) found that high school athletes with concussions and headache a week after injury had worse performance on reaction time and memory tests than athletes with concussions but no headache a week after injury.

Illness . Minor upper respiratory tract infections slow reaction time, make mood more negative, and cause disturbance of sleep (Smith et al. , 2004).  

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REACTION TIME A Literature Review on Reaction Time By Robert J. Kosinski. Published by Clemson University, Sept. 2006

(http://biae.clemson.edu/bpc/bp/Lab/110/reaction.htm).

Loss control professionals are confronted with thinking about reaction time and how it affects that last fraction of a second before the proximate cause of a damaging energy exchange. This literature review was written for university students preparing to conduct their first reaction time experiment. Just because it was specifically written for someone else is no reason for loss control professionals not to take advantage of it. Since it was written for students, it is refreshingly approachable.

The review gently whispers that we don't know everything yet. At the beginning of a learning project an open mind is more important than a preconceived notion. Since we all lack knowledge, just about different things, shared curiosity supersedes authoritative ignorance. The review opens with the explanation that there is no need to start with the complexities and Imponderables of the mysteries of the mind. Work on something manageable to start. This is a guide to help a student start on a simple experiment so that s/he can see how science works. Naturally, an undergraduate's first experiment will not have a large enough sample for serious research results, but a well-directed first experiment points to the path for more involved future learning....

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• Published: 14 December 2020

How does cognitive function measured by the reaction time and critical flicker fusion frequency correlate with the academic performance of students?

• Archana Prabu Kumar 1 , 2 ,
• Abirami Omprakash 2 ,
• Maheshkumar Kuppusamy 3 ,
• Maruthy K.N. 4 ,
• Sathiyasekaran B.W.C. 5 ,
• Vijayaraghavan P.V. 6 &
• Padmavathi Ramaswamy 2  

BMC Medical Education volume  20 , Article number:  507 ( 2020 ) Cite this article

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The reaction time (RT) is “ the time taken for the appearance of rapid voluntary reaction by an individual following a stimulus, either auditory or visual ” and the Critical Flickering Fusion Frequency (CFFF) is “ the rate at which successively presented light stimuli appear to be steady and continuous ”. RT and CFFF are commonly used for the assessment of cognitive functions that are known to influence academic performance. However, data about the exact correlation between these are scarce, particularly in India. This research aimed to study the association between visual RT (VRT), auditory RT (ART) and CFFF and their impact on the academic performance of undergraduate students.

This cross-sectional study was conducted on 700 students of Faculty of Medicine and Dentistry at a private medical university in South India, during the period from 2015 to 2017. The VRT, ART and CFFF were evaluated, and the best out of three subsequent attempts was recorded. The mean score (in percentage) of the three best marks out of the five internal assessments for the course during each academic year was considered for analysis. The association between the different cognitive tests and the average academic performance was analysed.

Female students had faster VRT ( n  = 345, mean = 243.97, SD = 83.87) than male students ( n  = 273, mean = 274.86, SD = 96.97) ( p  = 0.001). VRT and ART had a moderate negative correlation with academic performance (for ART, r =  − 0.42, p  < 0.001; for VRT; r =  − 0.40, p <  0.001). CFFF had a very weak positive correlation with academic performance ( r =  0.19, p  = 0.01). The only independent predictors of academic performance were RT and gender (Adjusted R 2  = 0.11).

Although there is a correlation between CFFF and cognitive function, our study showed only a weak correlation between CFFF and academic performance. Female students had faster RTs, and gender was an independent predictor of academic performance. Rather, students with faster RTs appear to have an advantage in academic performance.

Peer Review reports

Several factors can influence the academic performance of students, and cognitive ability is among the most important of these factors [ 1 , 2 ]. Cognitive ability is determined by cognitive functions, which are in turn influenced by the speed of information processing, attention span, language skills, visual-spatial orientation and so on [ 3 ]. Many factors that influence cognitive functions have been identified. Some of the important factors include age, gender, body mass index (BMI), educational background, socio-economic status, hormonal disorders and other comorbid conditions [ 4 ]. In addition, it has been established that the aforementioned factors also impact academic performance [ 5 ].

The assessment of cognitive functions may help in the evaluation of students’ functional capacity [ 6 ]. There are many tools for the assessment of cognitive functions, out of which Reaction Time (RT) and Critical Flicker Fusion Frequency (CFFF) are two of the commonly used tests for the assessment of certain cognitive functions that are involved in learning and performance [ 7 , 8 ]. RT and CFFF have also been documented as markers of a higher cognitive function [ 7 , 8 , 9 , 10 , 11 ]. They are commonly used in clinical settings because they are simple, reliable, valid and cheap [ 8 , 12 ].

RT is the time elapsed between the presentation of a particular sensory stimulus to an individual and their consequent behavioural response to that stimulus [ 7 ]. This stimulus may be visual, auditory, tactile or that of any sensory modality [ 7 ]. The human body responds to different stimuli at various speeds [ 12 ]. For instance, it responds faster to auditory stimuli than to visual stimuli [ 12 ]. RT is a fundamental contributor to the processing of information, and it is considered to be an index for information processing speed and the speed of response programming [ 13 ]. Several types of RTs exist, such as the simple RT (time between the stimulus presentation and an individual’s response), recognition RT (response to a particular stimulus and not to others) and choice RT (different stimuli requiring different responses) [ 14 ].

The CFFF test is another tool for the assessment of cognitive domain. It evaluates the cortical arousal state and the activity of the central nervous system [ 8 ]. The visual cortex processes the sensory information it receives in two manners: temporal and spatial [ 15 ]. Spatial processing refers to the ability of the cortex to discriminate between different presenting stimuli with regard to their location in space, whereas temporal processing relates to the discrimination between the stimuli with respect to the time elapsed between them [ 15 ]. The CFFF test evaluates the visual cortex’s temporal processing of the stimuli. It measures the frequency of presentation of successive visual stimuli at which they are perceived as a continuous and stable stimulation rather than discrete events. Several studies show that CFFF is positively correlated with concentration, alertness, enhanced attention and executive functions [ 11 , 16 , 17 ]. Thus, it is used as an adjunct test for the evaluation of various cognitive domains during psychometric tests [ 11 ]. The CFFF test also has the advantages of being simple, easy to perform and language-independent [ 8 ].

Learning is a complex process that depends on several factors, namely concentration, arousal of the cerebral cortex, attentiveness and rapidity of information processing [ 18 ]. The sequence of events contributing to the measurement of RT includes the perception of the sensory stimulus, processing of the information through central and peripheral mechanisms and the passage of the motor impulse through the neuronal pathways, followed by motor activity (end-organ activation) [ 19 ]. RT evaluates the pace and quality of information processing [ 20 ], whereas CFFF measures the “cortical arousal” [ 21 , 22 , 23 ]. All of these play a vital role in effective learning, thereby facilitating better academic achievement.

Given the fact that the RT testing and the CFFF evaluation can measure information processing speed, attention, concentration and alertness [ 11 , 13 , 16 , 17 ], all of which are important factors presumed to be associated with higher academic achievement [ 24 , 25 , 26 ], it can be expected that better cognitive functions may also be linked with better academic performance [ 27 ]. There are very few studies that have explored this relationship, and although they have shown that there is a statistically significant relationship between faster RT and better academic performance, the correlation appears to be weak at best ( r =  0.07 to r =  0.29) [ 20 , 28 ]. To the best of our knowledge, no studies have included CFFF. Therefore, this research aimed to study the association between the academic performance of undergraduate students in India and their visual RT (VRT), auditory RT (ART) as well as CFFF.

This was a cross-sectional study conducted on undergraduate students who attended the Physiology and Applied Physiology course at the Faculty of Medicine and the Faculty of Dentistry at a private medical university in South India during the period from 2015 to 2017. Students with any visual problems, hearing deficiency, hormonal disorders or any neurological disease were excluded from the study. Ethical approval to carry out this research was obtained from the ethical committee of the host institution. After explaining the study details, written informed consent was taken from all the students who agreed to participate in the study.

Demographic data, such as age, gender and BMI, were collected from the students. The average academic score of the three best formal tests conducted during the academic year was calculated as an indication of academic performance. Auditory RT, visual RT and CFFF were then measured for each student.

• Academic performance

Five internal assessment exams relating to the discipline of Physiology and Applied Physiology were conducted at an interval of 8 to 10 weeks during each academic year. Each internal assessment included 15 multiple-choice questions (one mark each and a maximum total score of 15), four short notes (five marks each, maximum score of 20) and one essay (a maximum score of 15 marks). The maximum total score per internal assessment was 50. All the assessments were held for 1 h and 30 min each. All question papers were designed based on a standard blueprint, and the difficulty index was comparable for all the assessments. For this study, the mean score (in percentage) of the three best marks out of the five internal assessments was considered for analysis. No internal assessments were conducted on the same day as the cognitive tests. The tests were corrected by faculty members who were not aware of the results of the cognitive tests. The faculty members who corrected the answer scripts of the students were masked about the student’s RT and CFFF.

Cognitive testing

The VRT and ART were assessed by using the PC 1000 Hz reaction timer, which is an in-house built device that comprises a 1000 Hz square wave oscillator [ 19 ]. The device is composed of a small light-emitting diode for visual stimulation, a headphone (1000 Hz) for auditory stimulation and two connected components (A and B), all of which are connected to a computer device. Component A is the part of the device that is controlled by the examiner via a start button, whereas component B is the part of the device that the subject faces. The RT was recorded via the Audacity software (version 1.2.2) in a 0.001 s accuracy wave format [ 29 ]. Our previous validation study on healthy volunteers using the PC 1000 Hz reaction timer showed a strong concurrent validity [ 19 ].

Prior to the recording, all subjects were instructed to get adequate sleep the night before the testing and have a light breakfast on the day of the test because sleep deprivation and the type of breakfast can affect cognition [ 30 , 31 , 32 ]. The students were also instructed not to consume any stimulants (including caffeinated foods and drinks) on the day of testing [ 33 ]. All recordings were done between 9 a.m. and 11 a.m. at the Physiology Department of the host institution, and all the students were educated about the tests prior to the testing.

Visual reaction time (VRT) testing

The VRT was measured in milliseconds (msec) by getting the subject to sit and look at component B of the PC 1000 Hz timer device and having the examiner sit in front of and control component A. The examiner used the start button on component A to start the stimulation procedure, and the subject was instructed to press the stop button with their dominant hand as soon as they saw the red light.

Auditory reaction time (ART) measurement

Similar to the case of the VRT measurement, the examiner started the stimulation by pressing the start button on component A of the device, and the subject pressed the stop button with their dominant hand once they heard the sound through their headphones (refer to Fig.  1 ). For each subject, three trials were allowed with an interval of one minute for both VRT and ART, and the minimum time recorded was the one used for analysis. The time elapsed between the presentation of the stimulus and the subject’s response was calculated in msec by using the Audacity software installed on the connected computer (refer to Fig.  2 ).

The procedure of testing the auditory reaction time. PC 1000 Hz reaction timer with component A (with examiner) and component B (with subject)

The audacity software used during ART and VRT measurement. Audacity software (version 1.2.2) storing the recordings of ART and VRT in a 0.001 s accuracy wave format

CFFF testing

The CFFF test was carried out by using a standard electronic module and standard protocols as documented in other studies [ 21 , 22 , 23 ]. The system of this module presented a series of red-light stimuli at different frequencies ranging from 12 Hz to 120 Hz. The examination was conducted in a dimly lit room with the subject sitting 80 cm away from the module and a 40 W bulb fixed behind the subject. The red light was presented against a white background, and the frequency of the flicker was gradually increased from 12 Hz until the subject reported that the presented light was perceived as “steady”, “constant” or “fused” light (refer to Fig.  3 ). The mean value of three descending measures from high to low frequency when the subject reported that the light started to flicker and the mean value of three ascending measures from low to high frequency when the subject reported that the light stopped to flicker were collected for analysis.

The procedure of CFFF measuring. CFFT test with red light against a white background with the subject sitting 80 cm away from the module

Statistical analysis

All the data were fed into the computer and analysed by using the Statistical Package for Social Science (SPSS) software, version 25.0 (IBM, Armonk, NY, USA). Data cleaning with the removal of outliers (RTs shorter than 95 msec or longer than 650 msec, academic scores of 0%) was performed prior to the analysis. Based on their average academic scores (“academic performance”), students were categorized as low achievers (i.e., they scored 35% or less), mid achievers (i.e., they scored between 36 and 74%) and high achievers (i.e., they scored higher than 75%). Based on their BMI, they were classified as underweight (BMI below 18.5), normal weight (BMI between 18.5 and 24.9) and overweight (BMI of 25 and above). The categorical variables (gender, academic performance group and BMI range) were presented as frequencies and percentages. The continuous variables (age, BMI, academic performance, ART, VRT and CFFF) were presented as means and standard deviations (SD) of the sample or medians and interquartile ranges (IQR). The academic performance was specified as the dependent variable. All other variables were considered as independent variables or factors.

Student’s t-tests were conducted for comparing the cognitive test results (ART, VRT and CFF) and the academic performance between the female and male students. One-way ANOVAs, followed by Tukey’s honestly significant difference (HSD) post hoc tests, were conducted to compare the ART and VRT of low, mid and high achievers as well as that of underweight, normal weight and overweight students. Pearson’s r correlation analysis was used to check for correlation between the academic results and ART, VRT and CFFF. The following convention was adopted to describe the strength of the correlation according to r values: 0.00 to 0.19 signifying “very weak”; 0.20 to 0.39 signifying “weak”; 0.40 to 0.59 signifying “moderate”; 0.60 to 0.79 signifying “strong”; 0.80 to 1.0 signifying “very strong”. Multiple regression analysis was conducted to examine the relative effects of CFFF, VRT, ART, BMI, and gender on academic performance. The statistical analysis was performed at a 0.05 level of significance. Complete case analysis was performed while dealing with missing data.

Demographic characteristics and cognitive results

Seven hundred undergraduate students were recruited for this study. After data cleaning, 618 records were available for analysis. Female students constituted most of the recruited subjects (345 / 618, i.e., 55.8%). All students were 18-year-old (618 / 618), and hence, age was not considered further in any statistical test. Table  1 shows the ART, VRT, CFFF, BMI and academic performance of the study participants.

Female students ( n  = 345) had a faster VRT (mean = 243.97, SD = 83.87) than male students ( n  = 273, mean = 274.86, SD = 96.97) ( p = 0.001 ), and they demonstrated better academic performance (mean = 56.16, SD = 19.66) when compared to male students (mean = 48.02, SD = 13.13) ( p < 0.001 ) (refer to Fig.  4 ). Although female students exhibited lower ART and CFFF measurements than male students, these did not reach statistical significance (refer to Table  2 ).

Boxplot of academic scores for female and male students. Female students had higher scores than male students, though the spread was more pronounced

A one-way ANOVA was performed to identify the effect of RT on exam performance for low, mid and high achievers (refer to Table 3 ). There was a significant effect of VRT [F (2, 615) = 6.40, p =  0.001] and ART [F (2, 615) = 24.47, p =  0.001] on exam performance. Tukey’s post hoc assessments showed that the RT of low achievers was significantly ( p  < 0.01) higher than the mid and high achievers. No such difference was found between the mid and high achievers ( p  > 0.05). There was also a statistically significant difference in the RTs between female and male students for each performance group (refer to Table  4 ).

There was a statistically significant effect of VRT [F (2, 615) = 4.39, p  = 0.01] on BMI, whereas no such effects were found in the case of ART [F (2, 615) = 0.02, p  = 0.97]. Tukey’s post hoc assessments showed that students with normal weight have faster VRT, compared to underweight students ( p  < 0.05) but not compared to obese students (refer to Table  5 ).

Correlations

Pearson’s coefficient (r) was utilized to examine the correlation between academic performance and cognitive measurements (VRT, ART, CFFF). VRT and ART had a moderate negative correlation with academic performance (for ART, r =  − 0.42, p  < 0.001; for VRT; r =  − 0.40, p <  0.001) (refer to Figs.  5 and 6 ). CFFF had a very weak positive correlation with academic performance ( r =  0.19, p =  0.01; refer to Fig.  7 ). The correlations were quite similar while examining each gender separately (refer to Table  6 ).

Scatter plot for ART and academic performance. Auditory Reaction Time (ART) had a moderate negative correlation with academic performance

Scatter plot for VRT and academic performance. Visual Reaction Time (VRT) had a moderate negative correlation with academic performance

Scatter plot for CFFF and Academic performance. CFFF had a week positive correlation with academic performance

In the process of assessing the multicollinearity, the variance inflation factor (VIF) was estimated to find the correlation between the predictor variables and the strength of that correlation. The findings indicated that multicollinearity was not a concern (CFFF, VIF = 1.0; ART, VIF = 1.01, VRT, VIF = 1.07, Gender, VIF = 1.04, BMI, VIF = 1.001). Multiple regression analysis showed that only RTs (for ART, β = − 0.05, t = − 6.75, p <  0.001; for VRT, β = − 0.01, t = − 2.3, p <  0.02). and gender (β = − 6.84, t = − 5.12, p <  0.001) were the significant predictors for academic performance among the students. In contrast, CFFF (β = 0.13, t = 0.11, p  = 0.27) and BMI (β = − 0.15, t = − 1.09, p =  0.27) were not significant predictors in the model for the academic performance (refer to Table  7 ). The adjusted R 2 value was 0.11; therefore, 11% of the variation in academic performance would be explained by the model containing RTs and gender.

The academic performance of students is affected by diverse factors, and the identification of these is essential for improving the outcome of scholastic achievement and future occupational outcomes. This study aimed to assess whether certain cognitive functions have a role in the academic performance of university students at the Faculty of Medicine and the Faculty of Dentistry. We evaluated the cognitive functions of students by using the ART, VRT and CFFF tests. The results from our study indicate that faster RTs were one of the predictors of better academic performance among the recruited participants. It was observed that female students had faster RTs and that gender was an independent predictor of academic performance.

Faster RTs are indicative of better cognitive functions, including memory and verbal fluency [ 6 ], processing speed [ 34 ], and intelligence [ 35 , 36 , 37 ]. Therefore, students with faster RTs are more likely to have better academic performance, as revealed by other studies. Prabhavathi et al. studied the impact of RT on academic performance among undergraduate medical students [ 20 ]. They found that students with faster RTs had higher academic scores, and they attributed it to better attention, concentration, cortical arousal and processing speed. Sharma et al. [ 28 ] found similar results in the case of another cohort of medical students, although the correlation was small and not statistically significant. Studies on adolescents also indicate a correlation between cognitive tests and academic performance [ 18 , 38 ]. However, academic performance may be affected by other factors, and RT is only a single contributor to the various cognitive functions [ 39 , 40 ]. Academic stress is shown to influence academic scores to a greater degree in the case of female students than in the case of male students [ 41 , 42 ]. Moreover, other non-cognitive factors, such as gender, age, BMI, attendance percentage, social factors and general health play a role in academic performance [ 5 , 43 ].

Similar to other studies, VRT was slower in the case of underweight and overweight students, and ARTs were similar [ 44 , 45 ]. The reasons for this are still unclear, and other confounding factors may be relevant. The arm to height ratio can negatively affect RTs but not in a linear fashion [ 46 ]. The BMI has a positive correlation with fat percentage [ 47 , 48 ] and a complex correlation with muscle mass, handgrip strength and endurance [ 49 ]. All these factors can affect RTs individually, with a generally positive correlation between fat percentage and VRT and a negative correlation between muscle function indices and RTs [ 44 , 50 , 51 , 52 ].

The female students included in our study had a significant RT when compared to the male students, contrary to the findings of the wider literature [ 18 , 20 , 52 , 53 , 54 , 55 , 56 , 57 ]. The different conduction velocities of central neurons, analytical pathways complexity, acetylcholine synthesis and hormonal effects on neural transmission [ 53 ] are thought to result in females having faster decision times [ 58 ] and faster auditory latencies [ 59 ] but slower RTs. In contrast, although the muscle contraction times are similar between genders [ 60 ], female students have weaker motor responses [ 53 ], and this may further explain the differences in the RTs.

More recent reports show there are no differences in the RTs between genders [ 34 , 61 , 62 ], and this could represent the effect of factors such as the increasing trend of exercise and training among female students, as it is related to faster RTs [ 52 ]. RTs in females are also subject to timing with respect to the menstruation cycle [ 63 ]. RT can be influenced by the arm span to height ratios, which are different between males and females [ 46 ]. Our study did not collect data on exercise or menstruation, and this could be the focus of future research to explain the contrasting results.

Female students had higher academic scores, similar to other studies that have shown an advantage of female students over the male in terms of academic achievement, especially in the health sciences [ 64 , 65 ], although sometimes, the difference is not statistically significant [ 20 ]. A recent meta-analysis confirmed that the wider literature reports similar findings [ 66 ], and yet, our regression analysis failed to show gender as a predictor of academic scores. As other authors argue, genders are more alike than different [ 67 ], and socioeconomic status, stereotype manipulation and school-related factors may explain most of the academic differences [ 68 ].

The data obtained from the literature on the correlation between CFFF and academic performance are scarce and indirect. Several studies have reported that the frequency of CFFF affects several visual processing skills, such as reading, visual attention and alertness [ 69 ]. The visual processing speed is essential for scholastic achievement and academic performance because it is directly correlated to reading ability, decision making and cerebral arousability [ 16 , 17 , 70 ]. Corr et al. reported that CFFF was positively correlated with procedural learning [ 71 ], and Mewborn et al. also reported that CFFF correlated significantly with executive functions in young adults [ 11 ]. Executive functions include working memory, impulse control, cognitive flexibility in generating different solutions to a problem and planning towards achieving an objective that are considered to predict academic performance, at least in the case of primary school children [ 72 ]. Caultela and Barlow reported in their study on 40 Boston College undergraduates that there was a significant correlation between CFFF and intelligence measured by the Otis Quick Scoring Intelligence test and the College Board tests for Verbal and Mathematical ability [ 73 ], results that have been suggested by other studies [ 74 ]. Intelligence is a strong predictor of academic achievement [ 72 ], with prior academic achievement also playing a significant role in the pathway between intelligence and final academic achievement [ 75 ]. However, CFFF performance is not a predictor of global cognition [ 11 ], and our study only revealed a very weak correlation between CFFF and academic scores. The correlation between CFFF and the factors known to affect academic achievement may not be as strong as predicted; more contemporary studies are needed to retest these assertions.

The message from this study is that having data on basal the cognition levels of students is always beneficial, and based on it, cognitive skills can be trained and enhanced [ 76 , 77 ]. Teachers are recommended to employ cognitive learning strategies that might enhance a learner’s capability to process knowledge more deeply and help them to eventually transfer the knowledge gained and apply it to newer circumstances [ 78 ].

Some of the techniques include the following:

1. Spaced practice: “ Creating a study schedule that spreads study activities repeated over a period of time ” [ 79 ].

2. Interleaving: “Switching between topics while studying” [ 79 ].

3. Elaboration: “Asking and explaining why and how things work” [ 79 ].

4. Retrieval practice: “Bringing learned information to mind from long-term memory” [ 79 ].

5. Reflection training [ 80 ] and Reflection [ 81 , 82 ].

6. Mindfulness learning [ 83 ].

Globally, all medical schools are marching towards a competency-based curriculum where ‘reflection’, ‘case-based discussions’, ‘journal clubs’ and ‘self-directed learning’ are becoming an essential aspect of the effort to improve cognition [ 81 ].

One of the main strengths of our study is that it evaluated more than one cognitive domain in correlation with academic performance. Other strong points of this study include large sample size and homogeneity of the study participants in terms of a similar age group, comparable socioeconomic background, school curriculum and qualification through a standard eligibility examination conducted by the Government of India. Other confounders such as gender and BMI were addressed through statistical analysis. The main limitation of the study is that the cognitive tests were not conducted on the same day as the internal assessment exams. However, cognitive tests (including the RT tests) have been reported to have high test re-test reliability [ 84 , 85 ]. Additionally, the narrow standard deviation values for academic performance indicate that the scores lie within a narrow area, and therefore, it is more difficult to identify the correlations. This was a cross-sectional study, and therefore, we could not establish a causal relationship. The study participants belonged to a homogenous group; hence, other confounding variables (e.g., age, socioeconomic status and educational background) could not be studied. There was no longitudinal follow-up of the study participants.

Faster VRTs and ARTs are correlated with better academic performance among undergraduate students, and the correlation is independent of other variables such as gender or BMI. The CFFF was practically not correlated. This indicates that attention, concentration, cortical arousal and processing speed may be more important for learning. This study highlights the importance of RT in academic performance. RT can be promoted by following a healthy lifestyle.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Auditory reaction time

Body Mass Index

Critical Flicker Fusion Frequency

Interquartile range

Reaction time

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We would like to sincerely thank all the participants included in this study for actively volunteering to undergo the cognitive tests.

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Archana Prabu Kumar

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Archana Prabu Kumar, Abirami Omprakash & Padmavathi Ramaswamy

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APK and RP conceptualized the study. AO and KM played a key role in data collection, data entry and data analysis. KNM provided scientific and technical support throughout the process of data collection. BWC and PVV provided intellectual input throughout the study. APK and RP closely supervised the data collection, data entry and data analysis, and they contributed immensely to the writing and editing of the manuscript. All authors read and approved the final manuscript.

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Additional file 1: supplementary figure 1.

- Block diagram of the Auditory and Visual reaction time measuring device using Audacity® software. Supplementary Fig. 2 - PC 1000 HZ Reaction timer device. Supplementary Fig. 3: Graphical flow chart for Reaction time estimation in Audacity® software with PC 1000 Hz reaction timer. Supplementary figure: 4 - CFFF measuring portable device. Supplementary figure: 5 – NETHRA- CFFF device Control software for execution of CFFF test.

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Prabu Kumar, A., Omprakash, A., Kuppusamy, M. et al. How does cognitive function measured by the reaction time and critical flicker fusion frequency correlate with the academic performance of students?. BMC Med Educ 20 , 507 (2020). https://doi.org/10.1186/s12909-020-02416-7

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Introduction

Choice reaction time (CRT) is one favorite dependent variable of cognitive psychologists, and historic changes in the use of reaction time illustrate the rise of the information-processing paradigm.

Preview: A Brief History of Reaction-Time Research

Interest in human reaction time predates scientific psychology. During the nineteenth century, Bonders used subtraction of reaction times to decouple mental processes from one another. In the heyday of behaviorism, reaction-time research declined. During the 1960s, mental processes again became the focus of reaction-time research. Today, many kinds of cognitive research use CRT methods.

Information Theory

Interest in efficient transmission of messages over limited communication channels led to experiments in which the human was the limited channel.

Information Theory and Choice Reaction Time Shannon (1948) defined information mathematically in terms of the number of alternatives and the probability of each. Merkel (1885) found that CRT increased linearly with the number of alternatives. Hick (1952) and Hyman (1953) related the increase to the amount of information transmitted.

Binary Decisions Information theory is built on a logarithmic metric, which implies underlying binary decisions. Leonard (1958) tried to see whether people used binary mental decisions to solve CRT tasks, but his data indicated that they do not.

Cognitive Inference and Information Fitts and Switzer (1962) found that CRT'depends on the number of stimuli people infer to be present more than it does on the actual number employed by the experimenter.

131 Engineering Psychology

Man / Machine Compatibility The need for cognitive concepts was highlighted by research on CRT and man/machine compatibility. The question was how to best tailor machines to fit people's capabilities, and trying to answer it showed that some of these capabilities are cognitive. Fitts and Seeger (1953) showed that the response arrangement producing the fastest reaction time depended on the stimulus arrangements used.

Psychological Refractory Period One limit on how people perform is revealed when they are required to make two responses in succession; the second response is typically slower.

Decomposing Mental Processes

By the mid-60s, CRT research included information theory experiments, engineering studies with utilitarian purposes, functionalist investigations of stimulus and response variations, studies of skills that demanded sophisticated cognition, and high-powered mathematical theorizing about CRT results. Smith (1968) moved psychology importantly toward the information-processing approach by integrating all of these sorts of research into a single stage-analytic framework, which reflected the influence of a computer analogy. He argued that four underlying processing stages are all reflected in CRT: stimulus preprocessing; stimulus categorization; response selection; and response execution.

Sternberg's Serial Exhaustive Model Elegant reaction-time research by Sternberg (1966) stimulated much psychological interest in mental processes.

Sternberg's Task Sternberg's item recognition task requires people to memorize a short list of digits and to then respond Yes or No as to whether a test digit is one of the digits in the memorized set.

Exhaustive Scanning Yes reaction times increase linearly with the number of items in the memory set. Reaction times for No responses parallel those for Yeses: Memory scanning is exhaustive.

High-Speed Scanning People scan their memories at the rate of about 38 milliseconds per item, which is about 25 items per second.

Additive Factor Method Sternberg (1969, b) improved upon Donders' subtraction methods by developing the method of additive factors. It attempts to isolate distinct information-processing stages of cognition. Sternberg's task and his additive factor logic have been used in hundreds of experiments.

Criticisms of Sternberg's Model Because they have been scrutinized by so many experimenters, Sternberg's task and model have been criticized frequently.

A Iternate Models Theories have been developed to explain Sternberg's data with mental mechanisms that are basically different from those specified by Sternberg, and there is no compelling reason to reject any of them.

Unpredicted Results Modifying Sternberg's procedure in any of several ways produces results that his theory cannot accommodate. This shows that his model is an elegant theory of a particular sort of performance, but it is not a general theory of human performance.

Sternberg's Contribution The elegance of Sternberg's research inspired many psychologists to study cognition from an information-processing point of view. The additive factor method provided a powerful tool for convergent validation of cognitive processes.

132Speed Accuracy Trade-Off

The logic of many reaction-time experiments requires that their subjects make no errors, but they make some nevertheless. Fitts (1966) showed that errors and reaction time can be changed by paying subjects for one or the other. People will trade speed for accuracy or vice versa.

Sternberg Again Speed-accuracy trade-off accounts for some data that are inconsistent with Sternberg's model.

Signal Detection Theory

Situations where a person must detect a faint stimulus in noise were treated as problems in sensory psychophysics—defining the energy level that produces a response. However, powerful decision processes and motivational components were discovered. Signal detectability theory was developed to separate true sensory factors from decision processes. It soon proved to have wide applicability in cognitive research beyond the original task.

Hits and False Alarms When someone is trying to indicate whether or not a signal is present, he scores a Hit when he says Yes and the signal is there. He has made a False Alarm when he says Yes but the signal is absent.

D- Prime (d') and Beta (β) Whether a person scores a Hit or a False Alarm depends both on his sensitivity, called d', and his biases, called /3.

ROC Curves By varying payoffs (or certain other factors), one can construct a Receiver-Operating-Characteristic (ROC) curve. From the ROC curve, one can estimate d' free of bias (β). ROC curves have helped to solve complex problems in several areas of cognitive psychology.

CRT and Signal Detection Theory Atkinson and Juola (1974) applied signal detection theory to account for both Sternberg's CRT data and many findings that his theory does not encompass.

Caveats We have presented only the main concepts and general outlines of signal detection theory. The theory highlighted internal decision processes: Active cognitive mechanisms came to be used in the analyses of human perceptions and reaction, replacing the passive information theoretic analogy.

Serial or Parallel Processing?

Neisser's Work Although Sternberg typically found support for serial processing, Neisser (1964) used a visual search task that suggested parallel processing. This apparent conflict stimulated much research.

Egeth's Approach Elegant designs by Egeth (1966) to resolve the conflict using multidimensional stimuli did not provide unequivocal support for either serial or parallel processing.

The Current Views of Serial and Parallel Processing It now appears that the early stages of pattern recognition are parallel and that the serial processing observed by Sternberg may be due to later stages of processing present in his task but not in the tasks of other investigators. The current view is that very early pattern-recognition processes are mostly preattentive, automatic, nonstrategic, and parallel. Later processing stages are mostly attended, flexible, strategic, and serial.

The Status of CRT in 1979

Choice reaction time has become a ubiquitous dependent variable rather than a topic area. This is possible because of the well-developed theoretical framework that researchers have established for the CRT task. Reaction-time measures are now used in all areas of cognitive psychology.

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The importance of reaction time, cognition, and meta-cognition abilities for drivers with visual deficits

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Individuals who do not fulfill required visual field standards have their driving license withdrawn in Sweden. However, understanding of the ability to compensate for this loss is limited. This study aimed to determine if reaction time and cognitive performance are important for safe driving in visual field loss (VFL) individuals. Visually demanding reaction time tasks of different complexity, for example, can help one understand why some VFL individuals drive as safely as normally sighted individuals. Twenty VFL individuals and 83 normally sighted individuals participated in a driving simulator experiment and an additional test battery. The driving task categorized VFL participants into two subgroups: passed or failed. Three reaction time tasks, four cognitive tests, and two meta-cognitive scales were completed. The passed VFL subgroup was faster than the failed subgroup in the context-dependent reaction time task and slower in the context-independent reaction time task. The passed subgroup performed equally well, or less well, on the cognitive tasks compared to the failed subgroup. The VFL participants performed less well than the normally sighted individuals on most cognitive tasks. However, VFL participants did not reflect on their driving ability (in meta-cognitive scales) in the same way as normally sighted individuals. There appear to be VFL subgroups in terms of ability to drive safely. Reaction time is important, but context dependent. Cognitive context-independent tests appear unrelated to driving test outcome for VFL individuals. The problems with context-independent testing of perceptual, cognitive, and meta-cognitive abilities when predicting safe driving capabilities are discussed.

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Andersson J, Peters B (2016) Simulator-based test method: assessment of driving ability in individuals with visual field loss [in Swedish]. Swedish National Road and Transport Research Institute, Linköping

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Andersson, J., Peters, B. The importance of reaction time, cognition, and meta-cognition abilities for drivers with visual deficits. Cogn Tech Work 22 , 787–800 (2020). https://doi.org/10.1007/s10111-019-00619-7

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DOI : https://doi.org/10.1007/s10111-019-00619-7

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A Comparative Study of Simple Auditory Reaction Time in Blind (Congenitally) and Sighted Subjects

Pritesh hariprasad gandhi.

Department of Physiology, Government Medical College, Bhavnagar, Gujarat, India

Pradnya A. Gokhale

H. b. mehta, background:.

Reaction time is the time interval between the application of a stimulus and the appearance of appropriate voluntary response by a subject. It involves stimulus processing, decision making, and response programming. Reaction time study has been popular due to their implication in sports physiology. Reaction time has been widely studied as its practical implications may be of great consequence e.g., a slower than normal reaction time while driving can have grave results.

To study simple auditory reaction time in congenitally blind subjects and in age sex matched sighted subjects. To compare the simple auditory reaction time between congenitally blind subjects and healthy control subjects.

Materials and Methods:

Study had been carried out in two groups: The 1 st of 50 congenitally blind subjects and 2 nd group comprises of 50 healthy controls. It was carried out on Multiple Choice Reaction Time Apparatus, Inco Ambala Ltd. (Accuracy±0.001 s) in a sitting position at Government Medical College and Hospital, Bhavnagar and at a Blind School, PNR campus, Bhavnagar, Gujarat, India.

Observations/Results:

Simple auditory reaction time response with four different type of sound (horn, bell, ring, and whistle) was recorded in both groups. According to our study, there is no significant different in reaction time between congenital blind and normal healthy persons.

Conclusion:

Blind individuals commonly utilize tactual and auditory cues for information and orientation and they reliance on touch and audition, together with more practice in using these modalities to guide behavior, is often reflected in better performance of blind relative to sighted participants in tactile or auditory discrimination tasks, but there is not any difference in reaction time between congenitally blind and sighted people.

INTRODUCTION

Reaction time is the time interval between the application of a stimulus and the appearance of appropriate voluntary response by a subject as rapidly as possible.[ 1 ] It is a measure of function of sensorimotor association[ 2 ] and performance of an individual.[ 3 ] It involves stimulus processing, decision making, and response programming.

Reaction time has been widely studied as its practical implications may be of great consequence, e.g., a slower than normal reaction time while driving can have grave results. Many factors such as physiological, psychological, pharmacological etc., have been shown to affect reaction times. They are age,[ 4 ] sex,[ 4 , 5 ] gender,[ 4 , 6 ] handedness,[ 7 , 8 ] physical fitness,[ 9 , 10 ] sleep,[ 9 ] fatigue,[ 9 ] distraction,[ 9 , 11 ] alcohol,[ 12 ] caffeine,[ 13 ] diabetes,[ 14 ] personality type and whether the stimulus is auditory or visual. Reaction time study has been popular due to their implication in sports physiology.[ 6 ]

The model for information flow within an organism can be represented in this way.[ 15 , 16 , 17 ]

Stimulus → Receptors → Integrator → Effectors → Response

More specific in human, the information flow can be represented in this way.

Stimulus → Sensory neuron → Spinal Cord or Brain → Motor Neurone → Response.

Types of auditory reaction time

• Simple reaction time: One stimulus and one response (Shorter duration)
• Recognition reaction time: There are some stimuli that should be responded to (the ‘Memory set’), and others that should get no response (the ‘Distracter set’). There is still only one correct response (Longer)
• Choice reaction time: There are multiple stimuli and multiple responses. The reaction must correspond to the correct stimulus (Longest duration).

Simple auditory reaction time[ 15 , 16 , 17 , 18 ] is the time interval between the onset of the single stimulus and the initiation of the response under the condition that the subject has been instructed to respond as rapidly as possible.

• It evaluates the processing speed of central nervous system (CNS) and coordination between the sensory and motor systems. Reaction time measurement includes the latency in sensory neural code traversing peripheral and central pathways, perceptive and cognitive processing, and a motor signal traversing both central and peripheral neuronal structures and finally the latency in the end effectors activation (i.e., muscle activation)
• Due to its simplicity, it can be assessed in blind participants.[ 19 , 20 ] Bernard et al . pointed out that the most important sensory modalities in the activities of the blind are touch (proprioception) and hearing. For this reason, the possibility that the blind possess a particular sensitivity with reference to touch and hearing is often assumed; it is therefore implied that the blind might be superior to the sighted in tasks in which touch and hearing are the most important performance elements. Bernard et al . showing there is no significant difference in reaction time between normal sighted groups and congenitally blind sighted group,[ 19 ] whereas Kujala et al , Neimyese et al ., Collignon et al . and Naveen et al . studies showing significant alteration in the reaction time. Many theories of Cross Modeling Sensory Reorganization or Properties of Plasticity in CNS had been postulated regarding this superiority. Previous studies in the past on auditory reaction time in blind participants having contradictory findings.[ 21 ]

JUSTIFICATION OF STUDY

Blindness is the functional disorders of sense organs may intensify the remaining senses. It is presumed that blind persons do not only hear better and have an intensified tactile sense but also have a stronger sense of smell. Better hearing ability was demonstrated by auditory evoked potentials, but the auditory reaction time is an ideal tool for measuring the level of sensory motor association.[ 22 , 23 ]

OBJECTIVES OF STUDY

To study simple auditory reaction time in congenitally blind subjects. To compare the simple auditory reaction time between congenitally blind subjects and healthy control subjects.

MATERIALS AND METHODS

After obtaining ethical clearance certificate from Institutional Review Board, Government Medical College, Bhavnagar, Gujarat, India. We carried out this study in two groups: 1 st group comprises of 50 congenital blind and the 2 nd of 50 healthy controls. 1 st group was containing 42 congenital blind male and 8 congenital blind females. Mean age was 23.56±8.92 years. 2 nd group was containing 43 healthy male and seven healthy female volunteers. Mean age was 19.56±6.28. Study was carried out in a sitting position after taking anthropometric data. It was carried out on Multiple Choice Reaction Time Apparatus, Inco Ambala Ltd. (Accuracy±0.001 s) at Government Medical College, Sir T. General hospital and Blind school, PNR campus, Bhavnagar.

Procedures done before obtaining simple reaction time

The detailed information of study to participants and informed written consent was taken before staring the reaction time. Proper preparation of participants was carried out and knowledge on precautions was given to them. The testing procedures were quite simple, non-invasive and harmless from subject's point of view. Subjects were explained and demonstrated about the procedure to be performed. A blindfold was given to participants (both congenital blind and controls) made up from dark black cotton cloth. Index finger of the dominant hand of participant was used on the key to get a response. Same instruction was given to both groups to press the key as soon as they hear a sound. Practice period of three trials with an instrument at each key (horn, bell, ring, whistle) were given to all participants. They were allowed to do enough practice as reaction time depends on the subject making a maximal alertness. Three times simple auditory reaction time was taken, and out of them fastest response was used for this study. Full series of tests takes time of about 4-5 min. All tests were recorded in sitting comfortable and relaxed position in the chair on before lunch and with no any tight clothing which substantially restricts discomfort. Following precaution was taken during data collection:

• Temperature was maintain between 30˚C and 35˚C
• Keep kept complete silence and avoids unavoidable voice
• Never set the instrument near any kind of disturbances
• Keep kept proper comfort for study participants.

Statistical analysis

The data were put in Microsoft Excel sheets. The mean and SD were count with the help of Excel. The data between cases and controls were analyzed in graph pad software and by unpaired test with the demo version of Graph pad software.

The present study was undertaken on the sample size containing 50 blind subject and 50 healthy control subjects with applying necessary inclusion and exclusion criteria as mentioned earlier. The subjects of the study group (Congenital blind) were screened with proper taking of history with special reference to history blindness (questionnaire) and with the help of their class teachers at the blind school. They were subjected to clinical examination in detail. The control healthy participants were screen out by proper examination and history taking.

Simple auditory reaction time response with four different type of sound was recorded in both groups.

In this present study, the mean and SD of all four types of sound stimulus are assess. In congenital blind group, simple mean auditory reaction time are slower in horn sound stimulus and bell sound stimulus than control grouped whereas in ring sound stimulus and whistle sound stimulus, simple mean auditory reaction time are faster than the control group [ Table 1 ].

Comparison between case and control group

Table 2 shows of simple auditory reaction time response with different type of sound stimulus like horn, bell, ring and whistle by both congenitally blind and normal sighted participants.

Comaparision of simple auditory reaction time with 4 different types of stimulus

By using Graphpad Instat 3 software, unpaired t -test applied for analysis of the data. The P >0.05 for all four type of stimulus. These shows values are no statistically significantly difference between both groups.

Table 3 represents the relationship of body mass index (BMI) with simple auditory reaction time. These values come after statistical analysis. The P >0.05 in all except <20 BMI group in horn sound, (that is by chance) considered as there are not any significant relation between BMI, and simple auditory reaction time in both group (1 st group is <20 BMI and 2 nd group is >20 BMI).

Relation of simple auditory reaction time with BMI of both groups

Blind individuals commonly utilize tactual and auditory cues for information and orientation (e.g., auditory pedestrian signals, tactual walking stones or Braille reading). Increased reliance on touch and audition, together with more practice in using these modalities to guide behavior, is often reflected in better performance of blind relative to sighted participants in tactile or auditory discrimination tasks.[ 21 ]

As described by Röder and Neville that blinds participants have better auditory performance than sighted participants. Outcome of this present study stated that there is statistically significantly no difference in simple auditory reaction time between congenitally blind and healthy control group. This outcome is as related as a result come by study of Bernard, which was done in 10 blind and 10 normal showing there is no significant difference in reaction time in between group.[ 19 ]

These values are further as comparable as study done by Borker and Pendnekar's.[ 24 ] Their study in normal participants showed Simple Auditory Reaction Time was 188±36 ms which is as near to simple auditory reaction time carried out in our study participants of both congenital blind and normal sighted subjects. In this study, a blind fold is given to both group of participants for given same environment to all. No any study mention on the blind fold given to both the congenitally blind group and control group.

Namita et al ., a comparative study of auditory and visual reaction time in males and females staff during shift duty in hospital showed auditory reaction time was 215.15±47.52.[ 9 ] In our study, Auditory Reaction Time (ART) for horn sound is 210.24±90 ms in congenital blind and 186.92±73.017 ms in normal sighted participants.

Niruba and Murthy's study of auditory and visual reaction time in type 2 diabetes; A case control study, showed ART in control was 174.13±30.7 ms, which is near to this study.[ 25 ] Kujala et al , Neimyese et al , Collignon et al and Naveen et al . studies showing significant alteration in the reaction time in congenitally blind as compare to healthy participants.[ 22 , 26 , 27 , 28 , 29 ] All above study was done in small groups and controversy in method the use.

In an early study in 1899 carried out by Galton a study of sound stimuli in teenagers (15-19); the result was mean ART was 158 ms for sound stimuli, which is accordance accordance with this study.[ 30 ]

Our finding regarding on two group of BMI as 1 st group having <20 BMI and 2 nd group having >20 BMI, there are statistically significantly no any difference between them. This shows BMI has no any impact on the participant's response to auditory stimuli. A study done by Nikam and Gadkari shows that there was significant positive correlation between BMI and reaction times (Visual Reaction Time (VRT) and ART) in both males and females by Pearson correlation analysis, but other factors such as age, sex, habit have also effect in the ART.[ 4 ]

In this study, there is statistically no significant difference in reaction time between congenital blind and normal healthy persons with a different kind of sound such as horn, bell, ring, and whistle in their group. This reflected the perception and response toward external auditory stimulus among congenital blind and normal sighted individual are equal. Loss of one sense does not reflect on the overacting of other sense as it act normally as per its’ perception and growth.

Source of Support: Nil

Conflict of Interest: None.