The effects of color on time perception – Blue stimuli are temporally overestimated

In a paper recently published in Scientific Reports, Sven Thönes, Christoph von Castell, Julia Iflinger, and Daniel Oberfeld investigated whether duration judgments depend on the color (hue) of stimuli to be judged.

As color represents a basic feature of visual stimuli in lab experiments as well as in every-day environments, potential effects of hue on our perception of time are important to be considered. In particular, the well-known effects of arousal on time perception suggest that arousing hues, such as red, induce an overestimation of duration.

In a two-interval duration-discrimination task, the authors investigated whether participants indeed overestimate the duration of red stimuli in comparison to blue stimuli, while controlling for differences in brightness (individual adjustments by means of flicker photometry) and saturation (colorimetric adjustment in terms of the CIELAB color space). The mean duration of the stimuli was 500 ms. Moreover, the participants’ affective reaction (arousal, valence, dominance) towards the color stimuli were measured by means of the Self Assessment Manikin Scales.

Interestingly, the results showed a significant overestimation of the duration of blue compared to red stimuli, even though the red stimuli were rated as being more arousing. The estimated point of subjective equality showed that blue and red stimuli were perceived to be of equal duration when the blue stimulus was in fact 60 ms (12%) shorter than the red stimulus.

These surprising results (high arousal related to temporal underestimation) question arousal to be the main driving factor in the context of color and time perception. Moreover, the precision (variability) of duration judgments, i.e., the duration difference limen, did not differ between red and blue stimuli, questioning also an explanation in terms of attentional processes. The authors propose that specific neurophysiological mechanisms of color processing might be the basis of the effect, which need to be investigated in more detail in future studies.

Importantly, in timing-related visual experiments, it needs to be considered that the hue of the stimuli can affect time perception.

Source article:

Thönes, S., von Castell, C., Iflinger, J., & Oberfeld, D. (2018). Color and time perception: Evidence for temporal overestimation of blue stimuli. Scientific Reports, 8(1688) doi: 10.1038/s41598-018-19892-z

–Dr. Sven Thones (

Linking sense of agency to perceived duration

Sense of agency (SoA) is an important feeling associated with voluntary actions, enabling one to experience that he/she is controlling the actions and through them the events in the external environment. Until now, only the distortion of time interval between the action and its consequence (i.e. intentional binding effect) was associated with SoA, but a recent study by Shu Imaizumi and Tomohisa Asai, published in Consciousness and Cognition showed that even perceived duration of consequence is linked with SoA.

To investigate this association, they measured the perceived duration (measure of subjective time) of visual display and the rating for amount of control (explicit measure of agency) as a function of temporal contiguity (between action and visual display) and identity of visual display (being participants own hand vs. someone else’s hand). In each trial, participant performed a complex hand gesture as depicted by the image on the screen. This hand gesture was recorded by the overhead camera and projected on the screen after variable delay. While participants performed this task, their hands were covered so the only visual feedback of their action was the one that they saw on the screen. Participants reported whether they perceived the duration of the displayed video feedback (3000ms) as “short” or “long”. They also reported whether they felt that they controlled the displayed hand, by providing a binary response as “totally agree” or “totally disagree”.

The agency was manipulated in two ways, one by changing the visual display (self vs. others) and second, by manipulating the action consequence delay (50ms, 250ms, 500ms, 1000ms or 1500ms). In half trials, participants saw recording of their own hand (self-condition) and in other half trials, they saw the prerecorded clips of other person hand movements performing similar action (other-condition). Orthogonally, the temporal contiguity between action and visual feedback (50ms, 250ms, 500ms, 1000ms or 1500ms) was also manipulated. Based on prior studies on SoA, it was expected that seeing visual feedback of one’s own hand should elicit stronger SoA compared to seeing someone else hand. Similarly, one should experience a stronger SoA for visual feedback displayed with short delay (50ms, 250ms, or 500ms) compared to longer delay (1000ms or 1500ms). They hypothesized that if SoA influences perceived duration then participants should report “long” judgment more often for conditions that are known to boost SoA.

Results revealed that when the visual feedback display consisted of participant’s own hand, they reported stronger SoA and perceived the duration as longer, for short action outcome delay (50ms, 250ms, or 500ms) and this effect become weaker as the delay become longer (1000ms and 1500ms). Furthermore, the above effect was not observed when display consisted of someone else hand, suggesting the possibility that SoA and perceived outcome duration might be linked. Another similar experiment, investigated the effect of participants own hand projected from first person perspective (upright) vs. second person perspective (inverted). Authors expected that inverted perspective would be treated as non-self and will not influence perceived duration, but surprisingly both inverted as well as upright perspective showed similar effect on perceived duration and agency, suggesting that independent of orientation the visual information regarding one’s own hand is processed in a similar manner.

In conclusion, this study provides evidence that SoA also affects perceived duration and participants perceives the outcome duration to be longer when they feel stronger SoA. However, this study is unclear about the exact mechanism that would explain the observed temporal expansion associated with SoA. Moreover, only single duration was used to evaluate changes in temporal perception. Another recent study published in Scientific Reports by Makwana and Srinivasan, also demonstrated similar temporal expansion associated with intentional action, which was sensitive to temporal contiguity and source of action (intention-based vs. stimulus-based). They demonstrated the intention induced temporal expansion, using multiple durations and paradigms (temporal bisection and magnitude estimation), In addition, they also investigated its underlying mechanism in terms of internal clock (most influential model of time perception), suggesting the role of switch dynamics and not the pacemaker speed, to be involved in such temporal expansion. Thus, these studies overall suggest that intention and intentional action, not only influence the time between the action and the outcome but may also influence other aspects of the outcome events such as its duration, and more studies are required to fully understand in what all ways our perception is distorted  by intentional action.



  1. Moore, J. W., & Obhi, S. S. (2012). Intentional binding and the sense of agency: a review. Consciousness and cognition21(1), 546-561.
  2. Makwana, M., & Srinivasan, N. (2017). Intended outcome expands in time. Scientific Reports, 7(6305) doi: 1038/s41598-017-05803-1


Source article:  Imaizumi, S., & Asai, T. (2017). My action lasts longer: Potential link between subjective time and agency during voluntary action. Consciousness and cognition, 51, 243-257.


—Mukesh Makwana (,

Doctoral student,

Centre of Behavioural and Cognitive Sciences (CBCS), India.

Expectation, information processing, and subjective duration

A paper recently published in Attention, Perception, and Psychophysics tested an implementation of the temporal oddball illusion (according to which standard stimuli seem shorter than oddball stimuli of the same duration) in a novel context using a novel methodology (musical imagery reproduction). This paper is, to the authors knowledge, the first to test whether the temporal oddball illusion translates from single events to multiple-event sequences, and whether information processing influences this potential translation.

In two experiments, musical chord sequences of varying durations (3.5 s; 7 s; 11.9 s) did or did not contain auditory oddballs (sliding tones), and people listened to the sequences while engaged in either direct temporal or indirect temporal processing. We manipulated information processing by independently varying the task (Experiment 1), the sequence event structure (Experiments 1 and 2), and the sequence familiarity (Experiment 2). The task was either to complete a verbal estimation (“What is the duration of this excerpt?”) or a musical imagery reproduction (“Imagine that excerpt playing back in your head. Re-play it through your head the exact way you heard it play through the headphones, from start to finish. Press the green button to mark the start of the excerpt you’re imagining. Press the red button to mark the finish of the excerpt you’re imagining.”). The sequence event structure was either repeated (the mere repetition of a single chord), coherent (chords progressions that follow the rules of Western tonal harmony), or incoherent (the coherent sequences scrambled such that the chords progressions violated the rules of Western tonal harmony). The sequence familiarity was either familiar (presented during an exposure phase) or unfamiliar (not presented during the exposure phase). Completing a verbal estimation task, and listening to coherent, repeated, and familiar sequences induces direct temporal processing. Completing a musical imagery reproduction task, and listening to incoherent and unfamiliar sequences induces indirect temporal processing.

The main findings were that the sequences containing oddballs seemed shorter and longer than those not containing oddballs when people were engaged in direct and indirect temporal processing, respectively. These results support the dual-process contingency model of short interval time estimation, and can be explained using the notion of an information processing continuum (Zakay, 1993): as attention shifted from counting seconds (direct temporal processing) to listening to music (indirect temporal processing), for example, the effect of oddballs shifted from decreasing the number of seconds counted to increasing the amount of music remembered.


Zakay, D. (1993). Relative and absolute duration judgments under prospective and retrospective paradigms. Attention, Perception, & Psychophysics, 54, 656–664.

Source paper:

Simchy-Gross, R., & Margulis, E. H. (2017). Expectation, information processing, and subjective duration. Attention, Perception, & Psychophysics, 1-17.


Reprints are available at

– Rhimmon Simchy-Gross

PhD student

Music Cognition Lab @ University of Arkansas

Intended outcome appears longer in time

We live in a complex and dynamic world where sometimes our action yields the intended (desired) outcomes and sometimes the unintended outcomes, but does our subjective time changes as a function of outcome being intended or unintended. To find the answer, read the recent article by Mukesh Makwana and Prof. Narayanan Srinivasan, published in Scientific Reports.

In a series of five experiments involving temporal bisection task (Exp1-4) and magnitude estimation task (Exp5), they investigated whether participants perceive the duration of intended outcome differently compared to unintended outcome, and if yes then what are its underlying mechanisms.

They reasoned that when a participant intends an outcome, its representation gets activated and this prior self-activated representation would lead to earlier awareness of the intended outcome compared to unintended outcome  extending the temporal experience. Recently, pre-activation account has been used to explain temporal expansion (Press et al., 2014).

To manipulate intentional nature of the outcome they used a simple color choice question. In each trial, amongst the choice of two colors, they asked participants to indicate what color circle they want to see, by pressing the allocated key for that color. After 250ms (Exp1) of the intentional key press, they were randomly presented with circle of either intended color (50% times) or unintended color (50% times) whose duration was randomly manipulated amongst nine levels (300ms to 700ms in steps of 50ms). This was done to reduce or eliminate the sensory-motor prediction between the key press and the color of the outcome circle, so that the effect of intention on the perceived duration of the outcome is not confounded with probability-based prediction. Irrespective of the intentional nature of the outcome, participants were supposed to report whether they perceived the duration of the outcome as closer to short (300ms) or long (700ms) anchor duration that they learnt in training phase before the main experiment. Each individual data was sorted into two conditions i.e. when they get the intended outcome (i.e. Intended condition) and when they did not get the intended outcome ( i.e. unintended condition). Psychometric (Weibull) functions were fitted for this two conditions and bisection points were calculated. Bisection point or point of subjective equality is the measure of shift in temporal perception, where lower values of bisection point in a condition indicate temporal expansion relative to condition with higher bisection point. Results of Exp1 showed that participants perceived the duration of intended outcome as longer compared to unintended outcome.

They also studied whether increase in delay between the intentional action and its outcome affects the intention induced temporal expansion observed in Exp1. So further two experiments were performed with increased delay between action and outcome i.e. 500ms in Exp2 and 1000ms in Exp3. Rest stimuli, apparatus and procedure were identical to Exp1 except that in Exp2 instead red and green, yellow and blue color circles were used. Results showed that the intention induced temporal expansion was observed till 500ms delay but as the delay increased to 1000ms the temporal expansion effect vanished, suggesting that the self-activated representation fades away around 1000ms of the intentional action.

To establish that for the above-observed temporal expansion effect, intentional activation of the representation is necessary and not just priming or instruction-based action is not sufficient Exp4 was performed. In Exp4, instead of intending and selecting what color circle they wanted to see, in each trial participants  were shown color word i.e. RED or GREEN on the screen and they just pressed the corresponding key. Rest procedure, stimuli and analysis was similar to Exp1. Results showed no difference in duration perception between word congruent condition and word incongruent condition, suggesting the importance of intention in the above effect.

Lastly, Exp5 was performed using magnitude estimation paradigm to investigate whether intention affects the time perception by increasing the pacemaker speed or affecting the switch or gating component of the “internal clock model”.  The internal clock model is the most influential classical model used to explain human timing behaviour. If any factor influences the pacemaker speed then as the magnitude of the actual duration increases the difference between two conditions should also increase given a typical “slope effect”. On the other hand, if the switch or gating component is affected then no slope effect is observed. Results showed no slope effect, indicating that intention might influence the switch or gating mechanism.

In conclusion, a series of experiments in this study provides convincing evidence that intention affects temporal perception and participants perceives the intended outcome to be longer in duration compared to unintended outcome. Moreover, this intention induced temporal expansion effect depends on the temporal contiguity between the action and the outcome and it vanishes at 1000ms action-outcome delay. Furthermore, in terms of internal clock, this effect is most probably not due to increase in pacemaker speed, rather opening or closing of the switch seems more probable mechanism. As humans are intentional agents and intentions forms a critical part of daily life, more studies investigating the effects of intention on perception in general should be pursued.




  1. Press, C., Berlot, E., Bird, G., Ivry, R., & Cook, R. (2014). Moving time: The influence of action on duration perception. Journal of Experimental Psychology: General, 143(5), 1787.


Source article:  Makwana, M., & Srinivasan, N. (2017). Intended outcome expands in time. Scientific Reports, 7(6305) doi:  10.1038/s41598-017-05803-1


—Mukesh Makwana (,

Doctoral student,

Centre of Behavioural and Cognitive Sciences (CBCS), India.


Olfactory-Visual Sensory Integration Twists Time Perception

During everyday interactions, our senses are bombarded with different kinds of sensory information, which are processed by dedicated sensory systems operating at different temporal sampling scales to form a coherent percept. The question is whether information from one modality (say olfactory) influences the temporal perception of stimulus from other modality (say vision). Although, previous studies have investigated the effect of auditory stimulus on temporal perception of visual stimuli [1, 2], the evidence for the effect of olfactory stimulus on temporal perception of visual stimuli was lacking. A recent study published in Cerebral Cortex, by Prof. Wen Zhou and her lab members (Dr. Bin Zhou, Guo Feng, and Wei Chen), fills this gap and addresses whether odor influences visual temporal sampling and duration perception.

To study the effect of odor on visual temporal sampling, they used a two alternative forced choice chromatic critical flicker fusion (CFF) task with two isoluminant complimentary color images of banana or apple alternating at different frequencies (15, 20, 22.5 & 25 Hz in different blocks) for duration of 400ms (see figure 1, in original paper). In each trial, there were two 400ms flickering interval each flanked by 200ms mask, and separated by 600ms blank between the two intervals. Out of two, only one interval contained the flickering fruit image (either banana or apple) and participants reported the interval that contained fruit image. Along with visual stimuli, in Exp1 (N=16), participants were also exposed to two different odor stimuli (banana-like, amyl acetate 0.02%v/v in propylene glycol; and apple-like, apple flavor Givaudan, in separate blocks). The idea was to check whether the odor congruency influence the temporal sampling (CFF threshold) for the flickering banana or apple images. Results revealed that participants object detection increased significantly when the odor and the object image content matched, even when the task did not demanded any explicit object discrimination or identification, suggesting that sensory congruency between olfactory and visual inputs boosted the corresponding object visibility around CFF. Another analysis by fitting the psychometric function for the two odor conditions, with frequencies on x-axis and accuracy on y-axis, suggested that olfactory-visual congruency also facilitated the visual temporal sampling.

To establish that the above congruency effect is specific to odor and not just semantic information (or context) provided by the odor, they performed two control experiments. In the first control experiment (Exp2A, N=16), participants performed exactly the same task as Exp1 but instead of actual odors, odorless purified water was used and was suggested to participants as diluted banana or apple odor. In the second control experiment (Exp2B, N=16), semantic textual labels, “banana odor” or “apple odor”, were displayed at the center of the screen. In both the control experiments, they did not observe the odor-visual congruency effect, suggesting that presence of odor is important for such sensory integration.

The next question was to find the neural correlates of the odor-visual congruency effect, emphasizing at what level of visual processing the odor starts modulating it. For this, they performed an EEG experiment (Exp3, N=18) using the same stimuli as in Exp1, but modifying the task a bit. In the modified task, only one flickering interval of 400ms was presented flanked by red-green noise mask of 100ms, and participants reported whether the object was present or absent in that trial. All objects (apple or banana image) were presented to participants’ at subliminal frequency. For nine participants flicker frequency of 22.5Hz was used whereas for other nine participants 25Hz was used. Results from time-frequency analysis, revealed that maximum congruency-induced enhancement (i.e. greater normalized power difference) was observed in electrodes over right temporal regions around 150-300ms post stimulus onset. The difference around this time window suggests that during odor-visual congruency, odor starts influencing vision at the stage of object-level processing. Even source-localization analysis indicated the activation of right temporal region which is again known to be involved in object level representations. Thus, these evidences strongly suggest that odor influences the corresponding visual object at the stage of object-level processing.

From the above experiments, it was evident that the odor-visual congruency modulates visual temporal sampling, so the next logical question was whether it also influences the perceived duration of the visual stimuli. To answer this question, in Exp5 (N=24), they used a 2-Alternative Force Choice (AFC) comparison task, in which one image (either apple or banana) was a standard image (500ms) and the other image (either banana or apple) was test image (of varying durations 300, 350, 400, 450, 500, 550, 600, 650, 700ms). Participants reported which of the two images appeared longer in duration. For half participants (N=12) apple image was standard and banana image was comparison, whereas for other half (N=12) banana image served as standard and apple image served as comparison. Participants in both these groups were exposed to banana-like or apple-like odor in separate blocks. Point of subjective equality (PSE) and difference limen (DL) were measured for both the odor conditions. PSE is the measure of perceived duration whereas DL is the measure of temporal sensitivity. A two way mixed ANOVA on PSE values, with odor (banana-like, apple-like) as within-subjects factor and comparison image (banana image, apple image) as between-subjects factor, showed significant interaction. Further post hoc analysis after Bonferroni correction revealed that participants perceived the duration of the image to be longer when the image content and the odor were congruent compared to when they were incongruent. Similar analysis with DL, did not show any significant difference neither for main effects nor for interaction, suggesting that odor modulates only the perceived duration but not the temporal sensitivity.

Again to confirm that the above congruency effect on perceived duration is due to odor, not just because of semantic information (or context) provided by the odor, they performed two control experiments (Exp5A and Exp5B) similar to Exp2A and Exp2B. In Exp5A (N=24) instead of odor, odorless purified water suggested as diluted banana-like or apple-like odor were presented, whereas in Exp5B (N=24) instead of odor, textual labels (“banana odor” or “apple odor”) were presented on the screen. Neither the purified water nor the textual labels, showed the odor-visual congruency effect of perceived duration as seen in Exp4, suggesting the importance of odor in odor-visual sensory integration to modulate visual temporal perception.

In conclusion, this study provides a convincing evidence for the effect of odor on visual time perception, including temporal sampling and perceived duration. In future, it would be interesting to investigate this effect with other time perception paradigms such as magnitude estimation and measure the slope effect, which might help to know whether odor influences the pacemaker speed or the switch/ gating mechanisms in context of “internal clock model”. Moreover, it would be further interesting to investigate whether such odor-visual congruency effect influence the neural correlate of time perception such as CNV (contingent negative variation) component.


1. Romei, V., De Haas, B., Mok, R. M., & Driver, J. (2011). Auditory stimulus timing influences perceived duration of co-occurring visual stimuli. Frontiers in psychology, 2.

2. Yuasa, K., & Yotsumoto, Y. (2015). Opposite distortions in interval timing perception for visual and auditory stimuli with temporal modulations. PloS one, 10(8), e0135646.

Source article: Zhou, B., Feng, G., Chen, W., & Zhou, W. (2017). Olfaction Warps Visual Time Perception. Cerebral Cortex, 1-11.

—Mukesh Makwana (,
Doctoral student,
Centre of Behavioural and Cognitive Sciences (CBCS), India.

What Language You Speak Shapes Your Subjective Time

If the popular 2016 science fiction movie “Arrival”, wherein linguist Dr. Louise Banks learns an alien language that enables her to understand and perceive the concept of time in a very different way (i.e. past, present and future exists simultaneously), fails to amaze you then probably the real experimental evidence in similar vein might astonish you. Yes, the recent article by Prof. Emanuel Bylund and Prof. Panos Athanasopoulos published in the Journal of Experimental Psychology: General demonstrates the effect of language on time perception.

The linguistic relativity hypothesis or more popularly known as “Sapir-Whorf hypothesis[1]” suggest that language affects thought process and cognition (although see McWhorter[2], 2014 for opposing view). Previous studies[3-5] by Prof. Lera Boroditsky and colleagues have shown how the concept of time, is represented differently in different languages, but a strong experimental study to demonstrate that language affects time perception was lacking.

Prof. Bylund and Prof. Athanasopoulos used temporal reproduction task, involving three groups, i) only Spanish speakers, ii) only Swedish speakers and iii) Spanish-Swedish bilinguals to investigate the effect of language on time perception. They selected Spanish and Swedish speakers, as in both these languages time is represented and expressed differently. While Spanish speakers represents time in terms of volume and use metaphors like “much time”, Swedish speakers on the other hand represents time in terms of distance and use metaphors like “long time”.

For 40 Spanish and 40 Swedish speakers, they measured the performance in temporal reproduction task as a function of changes in the non-temporal stimulus dimensions such as growing line (representing distance metaphor) or filling of container (representing volume metaphor). The duration of the stimulus and the irrelevant stimulus dimensions (i.e. length of line and filling of container) were manipulated orthogonally. The stimulus duration for reproduction task ranged from 1000ms to 5000ms in steps of 500ms, whereas the length of growing line or the filling of container ranged from 100 to 500 pixels in steps of 50 pixels.

Half of the Spanish and Swedish speakers performed the temporal reproduction task with growing line stimulus while other half performed the temporal reproduction task with filling container stimulus. At the beginning of every trial, the instruction to perform either the temporal reproduction task or the non-temporal (line or container) task was prompted with a word label and a symbol (e.g. hourglass for temporal task, and cross for non-temporal task). For Spanish group the following word labels were used ‘duracion’ for temporal task, ‘distancia’ for line task or ‘cantidad’ for container task, whereas for Swedish group the following word labels were used ‘tid’ for temporal task, ‘avstand’ for line task or ‘mangd’ for container task.

When they categorized the data into extreme (1000ms, 1500ms, 4500ms, 5000ms) and medium category (2000ms to 4000ms), they found that for medium category Spanish speakers performance in temporal reproduction task was influenced when observing the filling container but not when observing the growing line. On the contrary, Swedish speakers performance in temporal reproduction task was influenced when observing the growing line but not when observing the filling container. As the Spanish speakers use amount or volume based metaphor to represent time, having a volume based stimulus interfered with their temporal reproduction whereas the Swedish speakers use distance based metaphor to represent time, having a distance based stimulus interfered with their temporal reproduction.

Interestingly when the same experiment was performed with different 40 Spanish and 40 Swedish speakers, without the word prompt (only symbols were used to indicate which task to perform), no such effect was observed, suggesting that linguistic cue or prompt is necessary for such effect to be tapped in the temporal reproduction task.

To establish that the above effect is mostly language related and not cultural bias, they performed the above experiment with 74 Spanish-Swedish bilinguals wherein half participants were given prompt in Spanish language and other half were given prompt in Swedish language. As predicted and observed in experiment 1, when Spanish word prompt was used participants temporal reproduction was influenced by filling container stimulus, whereas when Swedish word prompt was used participants temporal reproduction was influenced by growing line stimulus. Thus establishing that language context influences time perception.

In conclusion, this study provides a convincing evidence for the effect of language context on time perception and opens a range of possibilities and questions, to be explored and answered, resulting in better understanding the relationship between language and time perception. In future, it would be nice to investigate this effect with other languages and temporal paradigms such as temporal bisection and generalization. In addition, it would be interesting to investigate whether such linguistic cues really influence time perception or only induce response bias; such questions can be addressed by performing the ERP version of similar experiment and measuring the CNV (contingent negative variation) component.

Although to experience such a drastic change in time perception as depicted in the movie “Arrival” may not be feasible at the moment, but some milder progress has been made in this direction with the introduction of “The Whorfian Time Warp”.


1. Whorf, B. L. (1956). Language, thought, and reality: Selected writings (J. B. Carroll, Ed.). Cambridge, MA: MIT Press.

2. McWhorter, J. (2014). The Language Hoax. Why the World Looks the Same in Any Language. New York: Oxford University Press.

3. Boroditsky, L. (2001). Does language shape thought? Mandarin and English speakers’ conception of time. Cognitive Psychology, 43, 1-22.

4. Boroditsky, L., Fuhrman, O., & McKormick, K. (2010). Do English and Mandarin speakers think about time differently? Cognition, 118, 123-129.

5. Casasanto, D., Boroditsky, L., Phillps, W., Greene, J., Goswami, S., Bocanegra-Thiel, S. & Gil, D. (2004). How deep are effects of language on thought? Time estimation in speakers of English, Indonesian, Greek, and Spanish. In K. Forbus, D. Gentner, & T. Regier (Eds.). Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 186–191). Mahwah, NJ: Lawrence Erlbaum Associates.

Source article: Bylund, E., & Athanasopoulos, P. (2017, April 27). The Whorfian Time Warp: Representing Duration Through the Language Hourglass. Journal of Experimental Psychology: General. Advance online publication.

—Mukesh Makwana (,
Doctoral student,
Centre of Behavioural and Cognitive Sciences (CBCS), India.

Temporal encoding in EEG derived brain states

How our brain encodes time is still a mystery. It is possible, that temporal information might be encoded in hippocampal time cells, or activity in the midbrain dopamine neurons or neural circuitry of basal ganglia or some other neural dynamics. Investigating these temporal encoders usually requires non-human invasive or in vitro experimental approaches. However, a recent study published in Scientific Reports by Fernanda Dantas Bueno, Vanessa C. Morita, RaphaelY. de Camargo, Marcelo B. Reyes, Marcelo S. Caetano & André M. Cravo showed that temporal information can also be extracted from the non-invasive human-EEG derived brain states.

They used a unique and interesting temporal generalization task. Participants saw a target circle at the extreme left–center of the screen. Beginning of each trial produced a beep (say B1, 1000Hz, 100ms) and simultaneously the target circle started moving in the horizontal direction from left to right side of the screen, at the speed of 90/sec. At the center of the screen, there was an aiming sight (white circle). The moving target circle took exactly 1.5sec to reach at the center of the aiming sight. Participants were instructed to press a key when the target circle aligns with the aiming sight, this produced another beep (say B2, 500hz, 100ms) and a green disc as an indication of the key pressed by the participants. These types of trials were called as regular trials, and they essentially helped in learning the standard 1.5sec interval between the two beeps (B1 and B2). Intermixed with the regular trials there were test trials, which differed from regular trials in two aspects. First, the trajectory of the target was occluded via a rectangular box, so participants only heard the B1 beep and did not see the moving target circle. Second, participants did not pressed the key to produce B2 beep, instead the B2 beep was produced automatically after varying intervals (0.8, 0.98, 1.22, 1.5, 1.85, 2.27 or 2.8 sec) from B1. Participants reported whether the interval between B1 and B2 took less time, equal time or more time than the standard 1.5sec learnt during regular trials. Overall each participants performed 350 regular trials and 350 test trials. While participants performed this task, their brain activity was recorded using 64-channel scalp EEG.

For behavioral analysis, they fitted the psychometric function (cumulative normal) to the p(short) and p(long) responses, and calculated the point of subjective equality (PSE), just noticeable difference (JND) and weber ratio (WR) for proportion of short and long responses, separately. They found that the sensitivity was better i.e. JND was small, for short responses compared to long responses, but when the sensitivity was normalized with the actual interval then there was no difference between the short and long response conditions. Thus, they demonstrated the scalar property of time perception.

In electrophysiological analysis, they showed the classical CNV (contingent negative variation) which peaked at the standard duration (1.5sec). To investigate whether time-resolved EEG signals carry temporal information, they cleverly used the multivariate pattern analysis (MVPA) and multidimensional scaling (MDS) approach. According to the state-dependent timing models, the temporal information is encoded in different brain states, if this is true, then distinct spatiotemporal patterns of activity might produce different patterns of activation across the EEG sensors.  To measure the pattern of activation across EEG sensors, they performed the following analysis. Using Mahalanobis distance, they performed MVPA on data for six intervals (0.8, 0.98, 1.22, 1.5, 1.85, 2.27 seconds) and used MDS to represent them in a two dimensional plot. From these analysis they showed that EEG-derived spatio-temporal dynamic pattern, predicts the response of the participants for the uncertain intervals (short – 1.22sec, long- 1.85sec). Moreover, they also showed that the rate of change in state space as a function of time was higher for the shortest interval, than for the last interval, once again demonstrating the scalar property of time for brain states.

In conclusion, this a very good study, demonstrating and encouraging the use of MVPA and MDS to human EEG derived brain states, and its implication in understanding temporal encoding.

Source article: Bueno, F. D., Morita, V. C., de Camargo, R. Y., Reyes, M. B., Caetano, M. S., & Cravo, A. M. (2017). Dynamic representation of time in brain states. Scientific Reports, 7.


—Mukesh Makwana (,

Doctoral student,

Centre of Behavioural and Cognitive Sciences (CBCS), India.


Let’s Dissociate Neural Network for Time perception and Working Memory

At fundamental level time perception involves, storing the temporal information of the present event and comparing it with the past temporal memories of similar or other events. It is impossible to imagine the process of time perception in the absence of working memory, and hence it has always been difficult to dissociate and study them in a single paradigm.

A recent study published in Frontiers in Human Neuroscience by Sertaç Üstün, Emre Kale and Metehan Çiçek, designed a novel paradigm to understand and dissociate the neural networks involved in time perception and working memory. Although all time perception tasks involves working memory, the main objective of this study was to understand and compare the brain activity when participants are performing only timing task, only numerical working memory task, or both.

In this study, participants (N=15) performed four types of experimental tasks (control task, only timing task, only working memory task, and both) while their brain activities were scanned using fMRI. Before each trial participants were cued about which task they were supposed to focus and report.

In control task, participants saw a box, horizontally moving from left side of the screen towards right. The middle path of this moving box was occluded using a black wide vertical bar. This black bar could be imagined as a tunnel and the box as a car, so initially you see the car (box) moving from left to right, in the middle of the screen it goes through a tunnel (black bar) so you cannot see it, and after some time it reappears from the other side of the tunnel (black bar). Participants pressed a key, when the box reappeared from other side of the vertical black bar. In only timing task, the authors very smartly changed the speed of the moving box when it was occluded, so sometimes the box reappeared on the other side after a short time (when speed was increased) or after a long time (when speed was decreased). Participants reported whether the speed increased or decreased. In only working memory task, they used a numerical task, so this box could contain either 1, 2, 3 or 4 dots in it. The number of these dots could increase or decrease when it was occluded. Participants reported whether the number of dots increased or decreased. Lastly, in the dual task condition, they asked participants to focus and report both the number of the dots and the speed of the box.

Behaviourally, they only recorded the reaction time (RTs) and accuracy for the four experimental tasks. In general, they found that participants were more accurate and faster in control task compared to any other demanding tasks. Comparing the accuracy of only timing task with only numerical working memory task suggests that timing task was relatively difficult compared to numerical working memory task.

In terms of brain activation, they observed enhanced activity in right dorsolateral prefrontal and right intraparietal cortical networks, together with the anterior cingulate cortex (ACC), anterior insula and basal ganglia (BG) when timing task was contrasted with control. While a right hemisphere domination was observed in timing task, they observed a left hemisphere domination when numerical working memory task was contrasted with control, specifically, enhanced activation in left prefrontal cortex, ACC, left superior parietal cortex, BG and cerebellum were observed. Both time perception and working memory were related to a strong peristriate cortical activity. One more interesting observation, was that while timing deactivated intraparietal sulcus (IPS) and posterior cingulate cortex (PCC), conversely the control, numerical memory, and dual (time-memory) tasks activated these brain regions.

They conclude that their results support a distributed neural network based model for time perception and that the intraparietal and posterior cingulate areas might play a role in the interface of memory and timing.

Although this study provides a good paradigm to study timing and memory related questions, there are some points, which should be noted. First, they do not use any explicit psychophysical timing task, which would have further provided more insights into the neural networks involved in maintaining a temporal working memory vs. maintaining a non-temporal working memory. Second, they only use one direction of moving box i.e. left-to-right, they could have controlled this by including the right-to-left direction, as well. This would reflect more about the hemisphere lateralization observed for timing and numerical working memory task. In addition, even top-to-bottom vs. bottom-to-top could be conducted, with horizontal black bar as occluder.

Overall, this is a very interesting study, and cleverly designed to investigate brain networks involved in timing and working memory, and encourage the timing community to do more research addressing these questions, and focus on the role of intraparietal and posterior cingulate areas in these two processes.

Source article: Üstün, S., Kale, E. H., & Çiçek, M. (2017). Neural Networks for Time Perception and Working Memory. Frontiers in Human Neuroscience, 11 (83).

—-Mukesh Makwana (
Doctoral student,
Centre of Behavioural and Cognitive Sciences (CBCS), India.

Image Contrast influence Perceived Duration

Majority of studies in time perception uses visual objects like faces (emotional, non-emotional), geometrical figures, scenes, numbers, etc as stimuli. Although these complex images have been shown to influence time perception (addressing different questions), but the role of basic perceptual features (like contrast) which constitutes these complex images are rarely studied. A recent study by Christopher Benton and Annabelle Redfern published in Frontiers in Psychology, investigated the role of contrast on perceived duration.

They based their hypothesis on two lines of research, first are the adaptation studies which shows duration compression explained by adaptation-related stimulus-specific reduction in neural activity in early visual areas. And second are those studies which shows increase in neural activity in early visual areas with increase in contrast. So from these studies they hypothesized that, if adaptation related decrease in neural activity in early visual areas is linked to decrease in perceived duration then contrast based increase in neural activity in early visual areas should lead to increase in perceived duration.

To test this hypothesis they used dynamic spatial noise patterns as stimuli with three levels of contrast (0.1, 0.3, and 0.9). Each noise element in this pattern changed its luminance sinusoidally at temporal frequency of 4Hz. Two types of spatial filters i.e. circular Gaussian envelope or circular aperture, were used to generate stimuli patches with either gradient boundary or sharp boundary. The sharp boundary circular patch acted as size control stimuli, as in gradient boundary the perceived size might also change with contrast confounding the timing results.

To measure the effect of contrast on perceived duration they used adaptive match-to-standard procedure. In each trial participants saw a standard stimuli (with contrast 0.3) for 600 ms followed by a match (test) stimuli (with either 0.1 or 0.9 contrast) displayed for time randomly decided by the adaptive procedure between 125ms to 3000ms. Participants reported which of the two appeared for longer duration. Gradient boundary and sharp boundary stimuli were used in separate blocks. Results indicated that participants perceived the high contrast stimuli to be longer in duration compared to low contrast stimuli irrespective of its boundary type.

Based on previous study linking contrast with temporal frequency, one might argue that the above results could not be due to contrast influencing duration but rather due to contrast influencing temporal frequency and which in-turn influencing duration. To control this they performed another experiment in which they first found the temporal frequency threshold for low and high contrast for individual participants. And then used individual specific temporal frequency to test the effect of contrast on perceived duration. In this experiment, even after controlling for temporal frequency change, results showed that perceived duration increased with contrast.

Authors suspected that the above increase in perceived duration due to contrast may not reflect entirely due to sustained neural activity but can also be explained by assuming some fixed neural activity threshold for stimulus onset and offset detection. In such a scenario, the perceived duration changes due to contrast is a result of difference in onset and offset timing rather than contrast driven sustained activity in the early visual areas. To investigate this, they designed a third experiment using a method of constant stimuli and tested the effect of contrast (0.1 and 0.9) on onset and offset perception. They found that contrast does influence the onset and offset perception of stimulus but this could account for around 20ms of difference between high and low contrast stimuli, which still cannot fully explain the 60ms difference they got in both the previous experiments.

Overall, the above study demonstrated the influence of low level perceptual feature such as contrast on perceived duration, further studies with multiple standard duration and larger N is needed to fully understand the role of contrast in time perception .  As pointed by authors, despite 89% reduction in contrast it only led to just 10% reduction in perceived duration, raises further interesting questions about the mechanisms underlying such effects. 

In my opinion, more studies are needed with not only contrast but also with other low level perceptual features like spatial frequencies, luminance; and also with curvatures, and textures, leading to complex images like faces. So that we can understand the role of specific components in altering time perception, which in future would enable researchers to model and predict (only to certain extend as complex objects influences are based on associated meaning as well) the perceived time of a complex image just by analyzing the lower level components of an image.

Source article: Benton, C. P., & Redfern, A. S. (2016). Perceived Duration Increases with Contrast, but Only a Little. Frontiers in Psychology, 7.

—-Mukesh Makwana, Doctoral student, (
Centre of Behavioural and Cognitive Sciences (CBCS), University of Allahabad, India.

Meditation, Sense of Agency and Time Perception

Whenever we perform any action or have a thought in our mind, we rarely wonder whether those belong to us or somebody else. But this seemingly trivial sense becomes very evident in patients with disorders of volition e.g. schizophrenia, alien limb syndrome, etc. where they are sometimes unable to associate agency for their own actions or thoughts.

Generally, when an individual performs a voluntary action (e.g. key press) leading to an outcome (e.g. tone), the perceived time of action and its outcome are shifted towards each other. The amount of shift in perceived time of action towards the outcome is called as action binding, and the amount of shift in perceived time of outcome towards the action is called outcome binding. The overall compression of subjective time between voluntary action and its outcome is famously known as intentional binding and is mostly studied using a Libet’s clock paradigm. Many researchers believe that the compression of subjective time between the voluntary action and its outcome acts as a cue for brain to distinguish between self and non-self action-outcome, and is often used as an implicit measure of sense of agency.

Meditation practices have profound effect on both our physical and mental well-being. But whether meditation practices could also influence sense of agency is unclear. A recent study published in Mindfulness by Lush, Parkinson and Dienes, investigated the effect of mindfulness meditation on intentional binding. In meditator group they had Buddhist mindfulness meditators (N=8) having around 14.6 years of meditation experience and in non-meditator group they had age and gender matched controls (N=8) with no experience of mindfulness meditation.

To measure intentional binding they used the standard Libet’s clock paradigm. Participants performed key press at their will which produced an auditory tone (1000Hz, 100ms) after a delay of 250ms. While they were doing this task, they fixated at the center of the screen displaying a clock face and a dot (0.2o) revolving around it at the speed of 1 revolution per 2560ms. Participants reported the time of action (key press) or outcome (tone) by indicating the position of the dot on the clock face where they thought it was present when that particular event occurred. There were four blocks; 1) contingent action block, 2) contingent outcome block, 3) baseline action block and 4) baseline outcome block. In both, contingent action and contingent outcome block, participants action produced an auditory tone. In contingent action block they were asked to report the perceived time of action whereas in the contingent outcome block they were asked to report the perceived time of outcome. In baseline action block, participants performed only voluntary key press without outcome tone, and reported the time of action. On the other hand, in the baseline outcome block, participants heard a tone randomly between 2.5 to 7 sec without performing any action, and reported the time when they heard the tone.

Mean judgement errors were calculated for each participant and each condition. Action binding and outcome binding were calculated by subtracting the appropriate baseline condition from their respective contingent condition. Overall intentional binding was calculated by subtracting the action binding from outcome binding.

Data was analyzed using Bayesian statistics as it has some advantages over conventional null hypothesis significance testing and is based on accessing the strength of evidence in favor of a particular hypothesis. Bayes factor is used to access strength of evidence. Bayes factor of above 3 indicates substantial evidence in favor of the alternative hypothesis and below 1/3 substantial evidence in favor of the null hypothesis. Bayes factor between 3 and 1/3 indicate data is insensitive in distinguishing between the null and alternative hypothesis.

Results showed that meditators reported more intentional binding compared to non-meditators, specifically the outcome binding was greater in meditators compared to non-meditators. The results suggest that mindfulness meditation shows increase in sense of agency. They explain their results could be due to mindfulness practitioner having greater meta-cognitive access to their intentions, and hence greater intentional binding.

Further evidences with different types of meditation practices and their effect on sense of agency is needed to better understand the relationship between meditation, sense of agency and time perception. Nevertheless, the current study provides a good start in this area, with their results giving hope that meditation practices could also be considered in treating the disorders of volition.


—–Mukesh Makwana, Doctoral student,

Centre of Behavioural and Cognitive Sciences, India.


Source article: Lush, P., Parkinson, J., & Dienes, Z. (2016). Illusory temporal binding in meditators. Mindfulness, 7(6), 1416-1422.