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Non-auditory effect of communitynoise on interval timing in humans: anexplorationBabita Pande a , Gajanan Rathod b , Nishtha Vaidya a , ChaynikaNag a , Arti Parganiha a & Atanu Kumar Pati aa School of Life Sciences, Pt. Ravishankar Shukla University,Raipur, 492010, Indiab Information Technology Cell, Chhattisgarh Public ServiceCommission, Raipur, IndiaAccepted author version posted online: 12 Oct 2011.Version ofrecord first published: 31 Oct 2011.

To cite this article: Babita Pande , Gajanan Rathod , Nishtha Vaidya , Chaynika Nag , Arti Parganiha& Atanu Kumar Pati (2012): Non-auditory effect of community noise on interval timing in humans:an exploration, Biological Rhythm Research, 43:6, 585-601

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Non-auditory effect of community noise on interval timing in humans:

an exploration

Babita Pandea, Gajanan Rathodb, Nishtha Vaidyaa, Chaynika Naga,Arti Parganihaa and Atanu Kumar Patia*

aSchool of Life Sciences, Pt. Ravishankar Shukla University, Raipur 492010, India;bInformation Technology Cell, Chhattisgarh Public Service Commission, Raipur, India

(Received 17 April 2011; final version accepted 9 September 2011)

Several physiological disorders and impairment of cognition have been ascribed tothe effects of noise. In the present study, we assessed the consequence of noise oninterval timing – one of the crucial cognitive components in humans. The studywas carried out during the five-day State carnival from afternoon to late evening(14:30–21:30). The sound level of the environment was in the range of 70–110 dBA(‘‘loud’’ to ‘‘very loud’’; Noise guidelines manual of city of Carlsbad, 1995,California, USA: Nolte and Associates, Inc.). On each day during the carnivalrandomly selected apparently healthy subjects, irrespective of gender, verballyestimated retrospectively or prospectively the duration of the display of the imageson the computer screen that was programmed for 60-s interval. A similar studywas carried out on randomly selected apparently healthy subjects in a very quiet toquiet environment (20–50 dBA) and was considered as the control group.The estimates were then converted to a duration judgment ratio, ‘‘theta (y).’’The Chi-square results established a significant relationship among condition,paradigm and accuracy in 60-s estimates. The distribution spectra of the estimatesexhibited multimodal pattern, irrespective of condition and paradigm. The majorpeak was located at 60 s in all sets of study. The subjects underestimated the 60-sinterval, irrespective of the noise condition and paradigm. The results of two-wayANOVA revealed a statistically significant effect of the factor ‘‘condition’’ on 60-sestimation, when evaluated separately for each paradigm. The gender andparadigm significantly modulated 60-s estimates independently only in the quietconditions. The higher retrospective judgment of one-minute duration might beattributed to difficulty in memory-related neurological processing of the short-interval durations in a noisy environment. The acute exposure to noise that wasprevailing during the carnival seems to improve prospective estimations of 60 s.However, in quiet conditions, humans pay more attention to prospectively passingevents, resulting in larger underestimations. In general, although both males andfemales underestimated 60-s intervals; a significant effect of the factor ‘‘gender’’was discerned only in quiet conditions. From these results, it can be concluded thathumans mostly underestimate a short duration (60 s), when estimated verbally. Along-term study is recommended to investigate: (1) the effects of chronic and acuteexposure of noise on perception of short-time intervals in humans, and (2) theeffects of interaction of noise with other factors, such as gender, age and time of theday involving a larger population.

Keywords: noise; interval timing; retrospective paradigm; prospective paradigm

*Corresponding author. Email: akpati19@gmail.com

Biological Rhythm Research

Vol. 43, No. 6, December 2012, 585–601

ISSN 0929-1016 print/ISSN 1744-4179 online

� 2012 Taylor & Francis

http://dx.doi.org/10.1080/09291016.2011.629829

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1. Introduction

Humans possess inbuilt timing machineries, the circadian timing system being thebest example. Its purpose is to help living organisms to adapt to their immediate andever-changing environment. The supraschiasmatic nuclei (SCN) – the centralpacemaker or circadian clock – coordinate many behavioral, physiological andmolecular variables to recur harmoniously with the ambient environment every 24 h(Pati 2001). The second timing machinery is known as the ‘‘interval timer’’. Itmeasures short-intervals, like a stopwatch, and is popularly regarded as a ‘‘biologicalstopwatch’’ (Verginia 1996; Aschoff 1998). It deals principally with the cognitivefunction of processing and perceiving short-lasting events. Functional magneticresonance imaging (fMRI) of brain areas has revealed that regions, like the basalganglia and cerebellum are the primary structures involved in tracking short timeintervals (Harrington et al. 1998). The basal ganglia and cerebellum have neuronalconnections with the cerebral cortex; therefore the latter has a participatory role inshort-time perception (Gibbon et al. 1984; Meck 1984). The thalamus and dopamineare also believed to be involved in the process (Matell and Meck 2000).

Humans either judge short-lasting events with prior knowledge of the task(s)involved, or estimate the duration of elapsed events without any prior knowledge.The former is known as a prospective paradigm and the latter as a retrospectiveparadigm for estimating the short intervals. Many researchers have advocateddifferent underlying informational processing pathways operating during retro-spective and prospective time estimation (Hicks et al. 1976; Block 1992; Block andZakay 2000). It is said that, in the prospective paradigm, subjects pay more attentionto ongoing events whereas, in retrospective judgment, they focus on memorizing orremembering the interval of elapsed duration (Zakay 1990). Brown (1985) stated thatsimilarities exist in estimations made via these paradigms, and recommended thatcomparing prospective and retrospective time estimation within the same experimentwould elucidate the role of attention in both of them. Cerebral hemispheres, besidesbeing engaged in interval timing mechanism(s), also supervise attention andmemory-related processes. The cerebral cortex takes part in subjective perceptionof short intervals, either directly or indirectly through attention or memory-relatedinformation processing (Posner and Dehaene 1994; Rao et al. 1997). Subjectiveestimation made through a prospective paradigm is assumed to be based oncomparison with duration in reference memory (Church 1989; Zakay and Block1997). Researchers rely mainly on four methods – verbal estimation, production,reproduction and comparison – to assess objectively short-time estimations (Blockand Zakay 1997; Zakay 1990). Estimating short intervals through verbal andproduction methods, the subjects are believed to memorize and compare the targetintervals with the related information on time measurement stored in the memory(Block et al. 2000). Thus, on the basis of the above reports, it can be argued thatprospective and retrospective paradigms are linked in some way.

Some researchers represent the short-time estimates as a duration judgment ratio,which is the ratio of subjective to objective duration (Block et al. 1999, 2000);others use theta estimates, ‘‘y’’, and define three levels of theta estimates: y¼ 1,perfect estimation; y4 1, underestimation; y5 1, overestimation (based on Kleinet al. 2003; Pande and Pati 2010).

The interval timer is considered to be a flexible timing system that can be trainedto increase its accuracy. It is also regarded as very sensitive, as many internal andexternal factors modulate its functioning (Wright 2002). A prominent and widely

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studied biological variable in humans is body temperature, and this modulates short-duration estimates. It has been documented that at higher body temperaturesestimations become shorter (Hoagland 1933; Kuriyama et al. 2003, 2005). Pati andGupta (1994) observed that people were able to estimate accurately short timeintervals when measured at their peak body temperature. Other variables thatmodulate human time perception are: mood (Angrilli et al. 1997), hormones such asadrenaline (Wright 2002), diseases like Parkinsonism (Pastor et al. 1992; Malapaniet al. 1993) and attention-deficit/hyperactivity disorder (Baldwin et al. 2004). Anindividual’s chronotype (Esposito et al. 2007) and the time of day also influence thecognitive ability to judge short time intervals (Pati and Gupta 1994; Kuriyama et al.2003, 2005; Pande et al. 2011).

Many findings suggest that males and females estimate short time intervalsdifferently (Rammsayer and Lustnauer 1989; Hancock et al. 1992), even thoughMarmaras et al. (1995) noted a similarity in short-interval estimation in males andfemales. On the contrary, females have been reported to be inconsistent as comparedwith males (Block et al. 2000). The females underestimate short intervals morefrequently than males (Kellaries and Mantel 1994; Block et al. 2000). With increasingage, the difference in estimation increases (Block et al. 1998; Craik and Hay 1999);this has been ascribed to slowing of information processing related to time or toslowing of the internal clock ( Fraisse 1984; Craik and Hay 1999).

Ambient environment is one of the most important factors that might influencecognition considerably. With industrialization and developments in science andtechnology, humans in modern society are often faced with the problem ofundesirable, unwanted and unpleasant sound levels in their environment – noise.Noise is sound pollution that may be emitted from several sources with differentintensities and might have negative health consequences. The quantification of theamplitude or loudness of a sound is measured in decibels, dBA (Noise GuidelinesManual 1995). Modern electronic and mechanical gadgets are often primary sourcesof noise. Other sources, which are secondary in nature, come from people’s activities,for example: traffic, crowds and carnivals.

Noise has dual effects on the human biological system; these are the auditory andnon-auditory consequences. The auditory effect is hearing impairment; the non-auditory effect is variously apparent in individuals’ physiology, behavior, psychologyand cognition. In India, the limit of noise intensity level is in the range of 75–70 dBAand 55–45 dBA for industrial and residential areas, respectively (Noise PollutionRegulation and Control Rules 2000, 2006). The WHO has considered 65 dBA as thesafe threshold limit (Berglund et al. 1999).

Noise affects the hypothalamus–pituitary–adrenocortical axis and results in theobvious symptoms of annoyance, stress and altered behavior (Alario et al. 1987;Ising and Braun 2000). Continuous exposure to background noise with intensityover 95 dBA hampers normal performance (Broadbent 1979). There are manyexamples of harmful effects of acute or chronic noise exposure on a variety ofcognitive functions, such as decreased performance in attention-demanding tasks,reading skills and comprehension in children (Cohen et al. 1980; Hygge et al. 2003;Sheild and Dockrell 2003; Ljung et al. 2009). Smith and Stansfeld (1986)documented a greater number of failures in day-to-day attention, memory andactions in subjects exposed to high aircraft noise compared with a quiet area.

Blake (1971) reported better mental performance under noisy conditions in themorning hours, whereas Smith and Miles (1985) did not find any diurnal effect of

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noise. Loeb et al. (1982) noted interaction effects among noise, time of day andgender in an arithmetic task. In contrast, Smith and Miles (1987) did not find anysignificant interaction effects of these factors. However, it has been acknowledgedthat the interaction effect could depend on the type of cognitive task underconsideration and the noise intensity level (Baker et al. 1984; Kryter 1985).

Thus, even though literature is available on the non-auditory effects of noise ondifferent cognitive attributes, information on the impact of ambient noise on short-time perception is meager (Delay and Mathey 1985). The interval 60 s or ‘‘oneminute’’ is the duration that people mostly use in daily life to encode an event. In oneof our studies we documented that young subjects judged 60-s interval with a highdegree of accuracy (Pati and Pande 2011). Therefore, studying the effects ofcondition, paradigm, gender and their interactions on 60-s estimate is important.

The present study is an exploratory study and its main focus is to investigate thenon-auditory effect of noise on perception of time intervals of 60 s. The primaryobjective is to test the hypothesis that noise (condition) does not affect the accuracywith which this interval is judged. Our second hypothesis is that there will be nosignificant difference in judgment of this interval with regard to either paradigm orgender vis-a-vis condition.

2. Material and method

2.1. Protocol

The short time interval estimations were carried out in noisy and quiet conditions.The noisy condition was on the eve of a five-day State carnival. The environmentalsound level in the carnival was about ‘‘loud’’ to ‘‘very loud’’, with a range of around70–110 dBA (with reference to Noise Guidelines Manual 1995). The time-estimationtask (60-s interval) was scheduled in the afternoon and late evening hours (14:30–21:30). The 60-s interval was chosen because it has been observed to be judged withgreat accuracy in young adults (Pati and Pande 2011). It is the interval that is usuallyused by people to denote time of any task: for example, ‘‘just a minute’’ or ‘‘wait aminute’’. We developed time-estimation software for assessment of the subjectiveestimation of the interval (Rathod’s Software). In this software, images thatcomprise a single or series of photographs appeared on the computer screen(11006 8.500), the total duration of display being 60 s. The study was also carriedout on randomly selected, apparently healthy, human subjects in a ‘‘very quiet’’ to‘‘quiet’’ environment (around 20–50 dBA), this population being considered to bethe control group.

2.2. Procedure

On each day during the carnival, randomly selected, apparently healthy, subjectsverbally estimated the duration of the displayed images retrospectively. For theseshort-time estimations (with the retrospective paradigm), the subjects were unawarethat they would be required to estimate the duration of the display. The subjectswere instructed to look at the images displayed on the computer screen (11006 8.500)and, after disappearance of the images, they were then asked to estimate the totaltime for which the image/images had been displayed. The subjects were asked fortheir time estimate immediately after the disappearance of the images, to avoid anydelay between the target duration and their judgment, as this is considered to be

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an important factor when using the retrospective paradigm (Espinosa-Fernandezet al. 2003). The images were also estimated with a prospective paradigm. In thisparadigm, the randomly selected subjects were instructed to estimate the total timeof the displayed images and this instruction was given immediately before showingthe images.

The responses to both paradigms were presented at three levels of accuracy:perfect estimation, overestimation and underestimation. An identical protocol wasfollowed under the quiet conditions for the control subjects. All subjects chosen wereindependent. Informed consent for participation was obtained from each subject.

2.3. Subjects

In the present study, 602 subjects participated voluntarily in the noisy conditions.Of these, 426 subjects (age range¼ 16–65 years, median age¼ 27 years) estimatedthe duration of displayed images with the retrospective paradigm. There were332 males (median age¼ 28 years) and 94 females (median age¼ 22 years). Theremaining 176 subjects (age range¼ 16–67 years, with median age¼ 23 years; males:N¼ 122, median age¼ 25 years; females: N¼ 54, median age¼ 22 years)prospectively estimated the interval for which the images were displayed on thescreen.

In quiet conditions, 130 subjects (age range¼ 19–61 years, median age¼ 24 years;males: N¼ 69, median age¼ 28 years; females: N¼ 61, median age¼ 22 years)retrospectively estimated the duration of the displayed images and 135 subjects (agerange¼ 18–52 years, median age¼ 23 years; males: N¼ 70, median age¼ 23 years;females: N¼ 65; median age¼ 23 years) estimated the duration prospectively.

The subjects were classified into three age groups, namely young (16–33 years),middle-aged (34–51 years) and old (52–69 years) (Figure 1).

Figure 1. Frequency distribution of subjects as function of age, condition (noisy vs. quiet),and paradigm (retrospective vs. prospective). Each volunteer estimated a 60-s intervalverbally.

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2.4. Statistical analyses

A database was created both in MS-Access and Excel. The estimates were convertedto duration judgment ratio, ‘‘theta (y)’’ (subjective estimates/objective interval),with y¼ 1 (perfect estimation), y4 1 (underestimation) and y5 1 (overestimation)(Block et al. 1999, 2000; Klein et al. 2003; Pande and Pati 2010). Chi-square test(three-dimensional contingency table) was performed to find out relationship amongattributes, such as condition (noise vs. quiet), paradigm (retrospective vs.prospective) and accuracy (y5 1, y¼ 1, y4 1). A 2-way ANOVA was employedto see the effects of condition and gender on 60-s estimation with retrospectiveand prospective paradigm. A similar test was performed to find out the effects ofparadigm and gender on 60-s estimations in both conditions, noisy and quiet(CoStat, CoHort Software; Version: 4.02). A 3-way ANOVA was also carried out tofind out the effects of condition, paradigm and gender on 60-s intervals in the youngage group only, using SPSS (ver. 10.0). To investigate significant differences withinfactors, the post-hoc Duncan’s multiple-range test was used. Student’s t-testsassuming unequal variances were also carried out using SPSS, to compare the meansbetween groups.

3. Results

3.1. Frequency distribution of 60-s estimates

Frequency distributions of 60-s estimates as function of condition and paradigmwere plotted (Figure 2A–D). The pattern of distribution is multimodal. However, themajor peak was located at 60 s in each case.

The Chi-square results established that condition (noise and quiet), paradigm(retrospective and prospective) and accuracy (y5 1, y¼ 1 and y4 1) in 60-sestimates (p5 0.001; Figure 3) are mutually dependent on each other. The frequencyof under-estimates of 60 s was higher with the prospective paradigm compared withthe retrospective in quiet conditions. Overall, relative frequency of under-estimation

Figure 2. Frequency distribution of 60-s estimate in noisy and quiet conditions measuredusing retrospective and prospective paradigms. (A) noisy – retrospective, (B) noisy –prospective, (C) quiet – retrospective and (D) quiet – prospective

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of the 60-s intervals was higher in noisy conditions with retrospective paradigm(Figure 3).

3.2. Main effects

3.2.1. Condition

A statistically significant effect of the factor condition (p5 0.05) on 60-s estimationswas validated independently for each paradigm (Tables 1 and 2). The mean theta

Table 1. Effects of condition (noise vs. quiet) and gender (female vs. male) in verbalestimation of 60-s interval with retrospective paradigm. All subjects.

ANOVA summary

Factor F1,552 p

Condition 4.02 50.05Gender 1.37 0.24Condition6 gender 1.49 0.22

Table 2. Effects of condition (noise vs. quiet) and gender (female vs. male) in verbalestimation of 60-s interval with prospective paradigm. All subjects.

ANOVA summary

Factor F1,307 p

Condition 5.83 50.05Gender 3.07 0.08Condition6 gender 0.09 0.76

Figure 3. Frequency of 60-s estimate as function of conditions, paradigms and accuracy.Relative frequencies in brackets calculated with reference to total frequency (N¼ 867).

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score was higher in the noisy than the quiet conditions using the retrospectiveparadigm, whereas the opposite was observed with the prospective paradigm(Figure 4). In the former paradigm underestimation was significantly higher in malesunder noisy conditions than their counterparts under quiet conditions (Figure 5).In contrast, in the latter paradigm underestimation was significantly higher infemales under quiet conditions (Figure 5). The main effect of condition became notsignificant when data on all subjects were pooled, irrespective of paradigm, and

Figure 4. Effects of conditions (noisy vs. quiet) on verbal estimation of 60-s interval, in allsubjects, measured using the retrospective and prospective paradigms. Means bearing thesame letter are not significantly different from each other at p 4 0.05 (based on Duncan’smultiple-range test).

Figure 5. Verbal estimation of 60-s interval measured using retrospective and prospectiveparadigms in all female and male subjects in noisy and quiet conditions.

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subjected to two-way ANOVA (Table 3). Results of the 3-way ANOVA (involvingcondition, paradigm and gender) also did not reveal significant effect of condition on60-s estimations in young subjects (Table 4). Results from the young subjects supportthe findings depicted in Figure 5 (Figure 6).

Table 3. Effects of condition (noise vs. quiet), paradigm (retrospective vs. prospective) andtheir interaction on verbal estimation of 60-s intervals. All subjects.

ANOVA summary

Factor F1,863 p

Condition (C) 0.01 0.91Paradigm (P) 0.11 0.74C6P 9.70 50.01

Table 4. Effects of condition (noisy vs. quiet), paradigm and gender on estimates of 60-sintervals. Young subjects.

ANOVA summary

Factors F1,660 p

Condition (C) 0.061 0.805Paradigm (P) 0.178 0.673Gender (G) 4.206 50.05C6P 10.889 0.001C6G 0.197 0.658P6G 0.007 0.935C6P6G 0.697 0.404

Figure 6. Verbal estimation of 60-s interval measured using both retrospective andprospective paradigms under noisy and quiet conditions in young female and male subjects.See legends to Figure 4. Refer also to Table 4 for the relevant ANOVA summary.

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3.2.2. Paradigm

The factor paradigm did not produce significant effect on 60-s estimations in allsubjects (Table 3) and in young group (Table 4). However, it significantly affected60-s estimations only in the quiet conditions in all subjects (Table 5) and in younggroup (Table 6). In quiet conditions, the mean theta score was significantly higherin young females than their counterparts in noisy conditions under prospectiveparadigm (Figure 6). The paradigm, however, did not produce any significant effectunder noisy conditions, irrespective of gender (Figure 6).

3.2.3. Gender

The factor gender affected 60-s estimations significantly in young group based on3-way ANOVA involving condition, paradigm and gender (Table 4). It significantlyaffected 60-s estimations in all subjects (Table 5), but not in young group (Table 6),under quiet conditions only.

3.2.4. Age and time of day

The main effect of age was not evaluated as a majority of the subjects were youngones (77%). The effect of time of day was not significant under noisy conditions(retrospective: F2,423¼ 1.076, p¼ 0.342; prospective: F2,173¼ 0.912, p¼ 0.404).Insufficient data points in quiet conditions did not allow us to perform ANOVA.

3.3. Effects of interaction

The interaction effects of condition and paradigm was significant on 60-s estimationsin all subjects as well as in young group (Tables 3 and 4). The other interactions, such

Table 5. Effects of paradigm (retrospective vs. prospective), gender (female vs. male) andtheir interaction on verbal estimation of 60-s intervals in quiet conditions. All subjects.

ANOVA summary

Factor F1,261 p

Paradigm (P) 7.21 50.01Gender (G) 3.92 50.05P6G 0.10 0.75

Table 6. Effects of paradigm and gender on verbal estimation of 60-s intervals in quietconditions. Young subjects.

ANOVA summary

Factor F1,234 p

Paradigm (P) 7.79 50.001Gender (G) 2.17 0.14P6G 0.79 0.37

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as condition6 gender and paradigm6 gender did not produce any significant effect(Tables 1, 2, 4–6).

4. Discussion

4.1. Frequency distribution of 60-s estimates

In the present study, we measured verbal estimates of short-time intervals in noisyconditions and compared with those obtained in quiet conditions. We employedcomputerized visual stimuli for verbal estimations of 60 s, using both prospectiveand retrospective paradigms. The distribution spectra of the estimates exhibitedmultimodal pattern, irrespective of condition and paradigm. The major peak waslocated at 60 s in all sets of study. However, sum of the total frequencies of estimatesgreater than 60 s compared to the frequency of exactly 60 s is larger, implying thatmostly people underestimate the studied interval. The observed multi-modality ofthe frequency distribution of 60-s estimates appears to be novel, as there is completeabsence of peer studies for comparison. A normal distribution has been reported forresponses around the criterion duration (Rakitin et al. 1998). In the referred study,reproduction method was used for 8 s and 21 s and the response distribution obeyedthe scalar property. Rakitin et al. (1998), however, viewed the response distributionwithin a truncated window of relative test criterion between 0 and 2. In our study, weincluded all responses and used verbal estimation procedure.

We transformed all responses into theta score and grouped them broadly intothree categories, such as y5 1, y¼ 1, and y4 1. We arranged the responsefrequencies with respect to three attributes, namely condition, paradigm andaccuracy and found a significant relationship between paired attributes and amongall attributes, gauged from Chi-square (three-dimensional contingency table) results.This confirms a strong interdependency among condition, paradigm and accuracy inestimates of short time intervals. The frequency of underestimates with prospectiveparadigm was larger than the retrospective in quiet conditions. It can be suggestedthat people pay more attention to time in quiet conditions and therefore gave longerverbal estimates with the prospective paradigm. The response pattern reversed inretrospective judgment of 60 s implying that subjects take more time to rememberthe duration of elapsed events in noisy conditions.

In general, we observed a wider variability in the estimations of 60 s intervals; thelargest being detected in prospective estimations under quite conditions. The findingsof Droit-Volet et al. (2004) that the variability in judging visual rather than auditorystimuli could be greater, due to focusing more attention on visual sources, such asthe computer screen, to start the processing of short time intervals, support thepresent results.

4.2. Condition

In the present study, under noisy conditions, the prospective estimations wereshorter as compared with those under quiet conditions. It implies that the subjectstend to become more accurate in judging the target interval prospectively in noisyconditions. It could be explained by the supposition that the prevailing noise levelduring the carnival might have made the subjects more attentive and consciousresulting in shorter estimations that are nearer to accuracy. It has been shown inan earlier report that noise considerably improves tracking and detection efficiencies

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of centrally located signals in the monitoring task (Hockey 1970). The views ofBroadbent (1971) that ‘‘noise may increase arousal’’ also support the presentfindings. The auditory noise has been shown to improve visual signal detection(Manjarrez et al. 2007). Moderate noise, unlike too little or too much, has beenreported to be beneficial for performance (Moss et al. 2004; McDonnell et al. 2007;Soderlund et al. 2010). The noise level in the carnival was loud to very loud;therefore, in some sense the latter findings contradict our results obtainedprospectively.

Noise is known to enhance the arithmetic calculation and visual memory tasks(Usher and Feingold 2000; Wilkinson et al. 2008). This improvement of cognitionthrough external noise has been explained in terms of the Moderate Brain Arousalmodel (MBA) that is based on the stochastic resonance concept (Sikstrom andSoderlund 2007). According to this concept, noise improves signaling. In this model,dopamine plays a vital role in neurotransmission in the brain and its release dependsupon environmental events (Floresco et al. 2003; Goto et al. 2007). Dopamine hasalso been reported to be involved in short-interval estimations related to informationprocessing (Matell and Meck 2000; Wright 2002). It has been suggested that externalnoise activates diminished neural processing of information because of low levels ofdopamine in subjects with attention deficit hyperactivity disorder (Soderlund et al.2010). The same researchers also observed that a non-clinical group of inattentivechildren performed better with regard to episodic memory in conditions ofbackground noise than did attentive (control) children. In our study, the subjectswere apparently healthy and the sound intensity level in the noisy conditions duringthe carnival might have posed negative effects on memory-related mechanismresulting in more underestimations with retrospective paradigms.

4.3. Paradigm

The factor paradigm did not produce any significant effect of 60-s estimates undernoisy conditions. However, in quiet conditions the subjects produced shorterestimates retrospectively. This means the subjects estimated the target duration moreaccurately with retrospective paradigm in quiet conditions. Probably in quietconditions subjects take less time to remember the duration of elapsed events as inretrospective judgment memory plays an important role. Block and Zakay (1997)also suggested that the prospective judgment takes longer than does retrospective.It may be due to allocation of attention during the processing of assessment ofshort-time intervals. It has also been reported that with increasing complexity of thestimulus, retrospective judgment time increases (Block 1990; Block and Zakay 1997).However, in the present study, we have used same stimulus type and thus eliminatedany influence stimulus type might have.

4.4. Gender

Findings in relation to gender differences in studies of short-time estimations areinconsistent. Some researchers have documented that males are better than females(Block et al. 2000) in terms of accuracy and consistency. Espinosa-Fernandez et al.(2003) observed that females overestimated more than did males with regard toprospective estimations. However, none of these experiments were carried out innoisy conditions. The present results indicate that females underestimated more,

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both prospectively and retrospectively and in both noisy and quiet conditions. Thegender difference was the maximum in quiet conditions with prospective estimation.This result corroborates the findings of Kellaris and Mantel (1994), who also foundthat females underestimated any time interval more than did males. The reasongiven for longer verbal estimation in females was that they gave more attention totime than did males (Block et al. 2000). It has been emphasized that femalesunderproduced 60-s and 5-min intervals more than did males, the underlying reasonfor which has been ascribed to elevated reaction times in females (Jurado et al. 1989;Espinosa-Fernandez et al. 2003). According to Hancock (1999), the gender differencein time estimation might be attributed to different spatio-temporal perceptualcapabilities. Our results support the findings of many other researchers who havefound that gender differences exist in short-time perception (Delay and Richardson1981; Rammsayer and Lustnauer 1989; Eisler and Eisler 1992; Hancock et al. 1992).

4.5. Age and time of day

Age is regarded as one of the important modulating factors for short-interval timeestimation. Block et al. (1998) witnessed that with the verbal estimation methodolder adults gave longer estimates as compared to their younger counterparts.Espinosa-Fernandez et al. (2003) documented that age had an effect on theestimation of longer time intervals, such as of 5 min rather than of 60 s. Craik andHay (1999) found age difference in estimation of short time intervals. In our study,the effects of age on time estimations remain inconclusive because of smaller samplesizes in the middle and older groups; the majority of the subjects belonged to younggroup only.

Best cognitive performance was observed to be in the afternoon hours due to thehighest body temperature at this time (Kleitman 1933). It is also known since longthat reaction time is inversely related to body temperature (Kleitman et al. 1938).Some authors have argued that the effect of noise on performance is influenced bythe factors ‘‘time of day’’ and ‘‘gender’’ (Kryter 1985). Noise could act as anactivator when cognitive performance is near its circadian nadir in an individual(Broadbent 1971; Kryter 1985). Baker et al. (1984) found that males did acomputerized addition task more rapidly but less accurately in the morning thanafternoon, but that the opposite result was found in females. Males performed betterin an arithmetic task in the late afternoon hours under noisy than quiet conditionsbut males’ presentation in an arithmetic task was poorer in the morning under noisyconditions (Frankenhaeuser and Lundberg 1977; Loeb et al. 1982). We have notreally investigated time-of-day effects on time estimations, as the window of ourstudy is narrow varying between 14:30 and 21:30.

4.6. Effects of interaction

It has been reported that the nature of interaction of the factors ‘‘noise’’, ‘‘time ofday’’ and ‘‘gender’’ varies with the type of task (Smith and Broadbent 1992). Of thethree factors that we have included in our study only the interaction effect ofthe factors ‘‘condition’’ and ‘‘paradigm’’ on 60-s estimations was significant. Westudied a singular task. There is a complete lack of information on the effects ofnature of interactions of noise with other factors on short-interval estimates,especially on 60 s.

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4.7. Implications and future work

The findings of the present study might be applicable in the field of sport, like cricket,where background noise from spectators sometime acts as a motivator and enhancethe player’s ability to pay attention in short-term cognitive tasks, such as catchingor hitting the ball. Future studies should investigate circadian variation in short-interval estimations under noisy conditions that would reveal interactions betweennoise exposure, gender, age and time of day. It is suggested that such study be carriedout on a larger sample.

Acknowledgements

This work was supported by the University Grants Commission, New Delhi, through itsDRS-SAP Scheme sanctioned to the School of Life Sciences, Pt. Ravishankar ShuklaUniversity, Raipur, in the thrust area, Chronobiology and the Department of Science andTechnology (DST), New Delhi, through the sanction of a Major Research Project under thescheme Cognitive Science Research Initiative (CSI). We thank the Head of the Department,School of Life Sciences, Pt. Ravishankar Shukla University, Raipur, India, for givingencouragement and support. We are grateful to the subjects, who voluntarily participated inthis study. We are also obliged to the esteemed referee who offered valuable suggestions for theimprovement of the manuscript.

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