Category: Blog

The P3 (or P300) is an event related potential (ERP) component that has commonly been associated with attentional mechanisms and the updating of expectations. It can be evoked by stimuli in oddball paradigms, where a low-frequency stimulus (the “oddball”) is shown intermixed with high-frequency stimuli (the “standards”). For example, a subject in such a paradigm might be shown a series of words in white font (the standard stimuli), and much more rarely, be shown a word in red font (the oddball). This oddball stimulus commonly elicits a P3 at centro-occipital electrodes, one which is more positive in amplitude compared with the standard stimuli.

Oddball paradigms are also commonly cited in the time perception literature: canonically, the duration of oddball stimuli is overestimated, relative to the standard stimuli. Given this commonality, could the P3 be a neural correlate of this temporal distortion? A recent study by Ernst et al. investigated this question explicitly.

This study used an oddball paradigm as described above, where the standard and oddball stimuli varied in duration (from 600 – 1120 ms). After the presentation of an oddball stimulus, subjects were asked whether this was shorter or longer in duration relative to the preceding standards¹. The durations of the stimuli were chosen such that the P3 occurred well before the duration judgement was required (and indeed before the termination of the oddball). This way, it was possible to test whether the amplitude of the relatively early P3 predicted the subsequent duration judgement.

The behavioural data confirmed that time overestimations were observed for the oddball stimuli; the typical temporal oddball effect. Analysis also confirmed the primary hypothesis, larger P3 amplitude led to overestimation of the oddball stimuli. Specifically, the P3 was larger for overestimated, compared to correct trials. (The P3 was also larger for correctly classified “long” judgements compared to correctly classified “short” judgements.) Thus, in trials where the oddball was overestimated, the P3 closely resembled that in correctly judged trials where the oddball was actually longer. In sum, these findings suggest that the P3 tracked the perceived duration of the oddball stimuli.

The researchers also used a multivariate pattern recognition technique in a supplementary analysis. Here, a classifier was trained to discriminate between standard and oddball stimuli on the basis of the ERP data. Classification accuracy was assessed across the different time windows, and showed above-chance accuracy from 125 ms after oddball presentation, reflecting general ERP differences consistent with the scalp distribution of the P3. By assessing the output of the classifier on trials pooled into correct short, correct long, and overestimated durations, they were able to recapitulate the main findings of the ERP analysis (but for a generated, essentially synthetic, estimated ERP) over the time window of 375 – 600 ms. In essence, because the classifier selectively reproduced an ERP response to oddball stimuli, this analysis lessens the likelihood that some other experimental feature was responsible for the difference in duration perception. Similarly, given the scalp distribution and time window, it also provides some data-driven support that the P3 was responsible for the effect, without specifically pre-selecting for the P3².

Overall, this study provides clear support for the hypothesis that the P3 is a neural correlate of the temporal oddball effect. Given that the P3 has been implicated a rather wide variety of phenomena, these phenomena can now also be interrogated from the perspective of time perception. There is also an ample opportunity for future research to ascertain whether the P3 is either necessary or sufficient for temporal distortions in a wider range of paradigms. In general, this study has delineated the P3 as a valuable component of interest for ongoing time perception research that uses EEG.

---

Source paper:

Ernst, B., Reichard, S. M., Riepl, R. F., Steinhauser, R., Zimmermann, S. F., & Steinhauser, M. (2017). The P3 and the subjective experience of time. Neuropsychologia, 103, 12–19. http://doi.org/10.1016/j.neuropsychologia.2017.06.033

The following is a guest post from Sven Thönes at Johannes Gutenberg-Universität Mainz, Germany.

Over the last decades, numerous studies have reported that the perception of time and the basic processing of temporal information is distorted in patients suffering from schizophrenia. The investigation of timing in schizophrenia is of particular interest because the notion of mistimed information transfer by Andreasen, et al. (1999), which is one of the most popular theories on the cognitive impairments and clinical outcomes in schizophrenia, assumes that distorted temporal processing may underlie the patients’ symptoms.

In a recent meta-analytical review published in Clinical Psychology Review, Sven Thoenes (Leibniz Research Centre for Working Environment and Human Factors) and Daniel Oberfeld (Johannes Gutenberg-University Mainz) reevaluated the data from 957 patients with schizophrenia and 1060 healthy control participants, provided by 68 studies on timing in schizophrenia from the past 65 years. The original studies applied a large variety of different temporal tasks, such as time estimation, production, reproduction, and duration discrimination, as well as the detection of temporal gaps and judgments of temporal order or simultaneity. Importantly, the reported behavioral measures represented different aspects of temporal performance, indexing between-group differences either in accuracy (i.e., signed deviation of the duration judgments from the veridical value) or precision (i.e., variability of the judgments or sensitivity in a discrimination task).

In their analyses, Thoenes and Oberfeld differentiated between different duration ranges and different temporal tasks representing either time perception (judgments of time intervals) or basic temporal processing (e.g., judgments of temporal order) as well as between effects of schizophrenia on accuracy and precision.

Interestingly, independent of the specific temporal tasks and interval durations used, the results clearly demonstrate that both time perception and basic temporal processing are less precise (more variable) in patients with schizophrenia (Hedges’ g > 1.00), whereas effects of schizophrenia on the accuracy of time perception are rather small and task-dependent. These results are in accordance with the theoretical assumption of mistimed information transfer and the notion of a more variable internal clock in patients with schizophrenia, but not with a strong effect of schizophrenia on clock speed.

The review suggests that future research needs to investigate to what degree the impairment of temporal precision may be due to clock-unspecific processes, such as general cognitive deficiencies in schizophrenia. Moreover, research should aim at combining established experimental and phenomenological approaches in order to gain a broader understanding of the specific temporal distortions in schizophrenia.

While research on time perception and temporal processing may be interesting as such, the recent meta-analysis shows that it may also be supportive for the generation and testing of theories in adjacent (cognitive) domains, such as clinical psychology and neuropsychology.

---

Source article:

Thönes, S. & Oberfeld, D. (2017). Meta-analysis of time perception and temporal processing in schizophrenia: Differential effects on precision and accuracy. Clinical Psychology Review, 54, 44-64. http://dx.doi.org/10.1016/j.cpr.2017.03.007

References

Andreasen, N. C., Nopoulos, P., O’Leary, D. S., Miller, D. D., Wassink, T., & Flaum, L. (1999). Defining the phenotype of schizophrenia: Cognitive dysmetria and its neural mechanisms. Biological Psychiatry, 46, 908-20.

A paper recently published in PNAS reports a fascinating example of task-specific perceptual reorganisation in deaf participants that raises interesting questions regarding the involvement of high-level auditory cortex in temporal processing.

 

The study found that a rhythmic sequence task involving visual stimuli (a flashing disc) evoked activity in a high-level auditory region in deaf participants. The region – called area Te3 – showed stronger responses to temporally patterned sequences of visual flashes compared to visual sequences comprised of isochronous stimulation. In participants with intact hearing however, area Te3 showed rhythm selective responses only to auditory sequences, confirming that this region is typically involved in auditory processing. The authors concluded that auditory sensory depravation led to a reorganisation of the pathways servicing high level auditory cortex, a suggestion supported by connectivity analysis showing increased connectivity between area Te3 and visual area MT/V5 in deaf participants.

 

Although a striking example of perceptual reorganization, what is interesting about this conclusion is that the authors interpret the results as evidence of task-specific reorganization of high-level cortex. The implication here being that area Te3 is specialized for rhythmic processing in a modality independent manner. To support their argument, the authors note similar evidence of modality independent functional specialization in blind participants who show activation in visual cortex to auditory stimuli.

 

How could it be that an auditory selective region could come to be visually selective in deaf participants? One answer may lie in the residual hearing reported by the deaf participants. A table in the supplementary materials indicates that all the participants used hearing aids (outside the study) and that most reported their speech perception to be poor-moderate. This is interesting since listeners with low hearing rely more on visual temporal cues from the face to facilitate speech intelligibility. The increased utilization of visual timing cues to improve auditory processing may have led to a strengthening of the structural pathways between higher auditory cortex and visual cortex.

 

If so this raises questions regarding the degree to which area Te3 should be considered a task-specific region (i.e., modality independent, selective for timing tasks), or an auditory region typically involved in the temporal organisation of speech. Posterior STS is a multi-sensory region and shows many areas that are strongly selective for audio-visual speech perception. To identify the properties of area Te3, a more careful analysis of stimulus specific and task specific responses would need to be carried out within individual participants before any definitive claim can be made regarding the functional properties of this region.

 

Bronson Harry

The MARCS Institute, Western Sydney University

Twitter | ResearchGate | Web

Picking up on statistical regularities over time is an important prerequisite for language acquisition. For example, learning the transitional probabilities between syllables provides important scaffolding for segmenting the ongoing speech stream into component words – something that is not possible based on auditory information alone. A recent study by Kikuchi and colleagues examined the electrophysiological neural responses to confirmations and violations of an artificial grammar’s rules, but did so in an especially ambitious way – by comparing invasive recordings from human and monkey auditory cortex.

Both species were exposed to an artificial grammar (sequences of CVC nonsense words concatenated in rule-based ways) for 30 minutes, and then neural recordings were made during listening to sequences in which the context led to a specific nonsense word being consistent with or violating the grammatical structure. In response to all nonsense words, both species showed phase consistency in the theta frequency band (~4–8 Hz) as well as power modulations in the gamma band (>~50 Hz). In addition, significant phase–amplitude coupling was found between the theta and gamma bands in response to nonsense words. The more interesting question then, is what happens in response to confirmations versus violations of the artificial grammar rules?

In both species, phase–amplitude coupling was modulated by both confirmations and violations of the artificial grammar rules. Some neurons liked confirmations, some liked violations, and some liked both. In a classical statistical testing world, averaging over recording sites, this would very much be a null effect. Of course, that’s not how neural population coding goes, so we can imagine that looking at the activity pattern over the population of neurons may have provided more information about whether grammar was being respected, but this type of analysis was not performed. Instead, an analysis is presented which suggests that the latencies of the different neural effects in monkeys at least were different, such that phase–amplitude coupling effects and changes in single-unit activity occurred earlier in time than gamma-power modulations. Keep in mind that these are the latencies of the statistical effects, and not necessarily when the real action starts happening (just when the action crosses a significance threshold). There were no attempts to relate the effects to each other in a more fine-grained way, to learn for example whether single-trial phase–amplitude-coupling modulations might predict subsequent power modulations on the same trial.

So, I’ll ask, as the authors ask, what does it all mean? There were no species differences whatsoever, at least in as far as what the current techniques and measures could tell. What does that imply for the relationship between neural “oscillations” (here, theta–gamma coupling specifically) and speech segmentation / perception? That is, can a neural response that is conserved across species do something special for humans that it doesn’t do for other species that don’t use language in the way we typically think of language being used? I’d say, “sure”. For one, the study tested responses to learned statistical regularities in the transitions between complex sounds, something some species of non-human animals seem quite able to do (see also a recent demonstration that monkey auditory cortex neural activity synchronizes with the slow rhythms of speech). On top of that, to cite something Anne-Lise Giraud said at a “Neural Oscillations in Speech and Language Processing” workshop I just attended, one of the really appealing things about neural oscillations is exactly that they are evolutionarily conserved, but still DO seem to have been coopted to do something special for humans.

To sum up, despite my superficial grumpiness about the paper’s shortcomings, I do think the approach is 100% commendable, and one way forward for learning about speech and language processing. Species comparisons are hard, especially with invasive recordings even for humans(!). But having the opportunity to directly compare humans to other species and to use carefully matched stimuli, pipelines, and maybe even tasks has the potential to tell us a lot about the human capacity to learn and communicate via spoken language.

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”.

References:

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. http://dx.doi.org/10.1037/xge0000314

—Mukesh Makwana (mukesh@cbcs.ac.in),
Doctoral student,
Centre of Behavioural and Cognitive Sciences (CBCS), India.

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 (mukesh@cbcs.ac.in),

Doctoral student,

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

 

Our perception of and memory for the passage of time depend on a lot of factors that are unrelated to the actual physical passage of time, as measured by a clock. The adages “time flies when you’re having fun” and “a watched pot never boils” summarize these effects: fill an interval with a lot of interesting stimuli, and time [prospectively] flies, but an empty interval occupied only by waiting will seem to last an eternity. As a generalization, explanations for these effects tend to focus on how much attention was on time itself. When exciting things are happening, you don’t pay much attention to time passing, and so time seems to fly, whereas when nothing’s happening, where else could you put your attention but on the passing of time?

Meditation can take many forms, but (here comes another generalization) one commonality among various practices is that they often promote awareness – whether of one’s surroundings, one’s own mental state, or one’s responses to external stimuli – and self-regulation. With respect to time perception, one consequence of meditation that is particularly interesting is mindfulness, that is, bringing one’s attention to the experiences occurring in the present moment. Intuitively, it makes sense that mindfulness might improve time perception, or in some cases, might lengthen perceived duration, as time won’t fly like it would if you were distracted from each present moment.

Transcendental meditation is a specific practice in which a calm, peaceful, and aware mental state is achieved via repetition of a mantra. Making an assumption that transcendental meditation practitioners would be more mindful than matched controls, Schötz, Otten, Wittmann, Schmidt, Kohls, and Meissner tested for a relation between mindfulness and time perception. Practitioners and controls were tested on mindfulness, impulsiveness, attention, time perspective, subjective experience of time, as well as time estimation, reproduction, and discrimination tasks. I’ve taken the liberty of plotting the important results, since the original manuscript didn’t contain any figures.

Meditators were significantly more mindful (present and accepting), scored higher on the “present fatalistic” dimension of the time perspective questionnaire (related to mindfulness, can be summarized by the statement “Because things always change, one cannot foresee the future”), and reported significantly less time pressure than matched controls. The groups were matched on other important things though, like attentional capacities, stress levels, and mental and physical activity.

There were also differences in terms of time perception, but some of the results were admittedly very confusing. Meditators were better at estimating an 80-s interval during which they were reading numbers, but weren’t better at producing a minute while reading numbers, or estimating a 40-s interval while not doing anything else. (The dashed lines in the figure are the target duration – if the bars hit those dashed lines, participants would be, on average, perfectly accurate.)

Meditators were also more precise at reproducing intervals in the milliseconds-to-seconds range (600 ms – 1400 ms and 8 s – 20 s), thought the metric used to determine precision completely escaped me – it’s also very strange based on the psychophysics of time perception that precision would be on the order of 13% for intervals that were hundreds-of-milliseconds long, but on the order of 2% for longer intervals (it’s the latter part that’s more weird). But details aside, it seems practitioners were more accurate at interval reproductions.

Finally, auditory temporal discrimination thresholds were smaller for meditators.

So, what can we conclude from these data? Should you practice transcendental meditation to improve your time perception abilities? Maybe just your ability to estimate 80 s while reading numbers? That’s unclear. There certainly does seem to be something to the idea that practicing transcendental meditation might come with a somewhat more accurate time sense. Does this come from using a mantra as a metronome as the authors suggest? Seems unlikely that would be a useful strategy for reproducing e.g., 600 ms. Does it result from being more mindful, and aware of the present and the passage of time? That seems realistic to me. But, importantly, moving forward, and as scientific as well as personal interest in meditative practices seems to increase, it’s critical to use well-motivated and well-controlled designs to test the potential benefits as well as detriments of meditation.

– Source article: Schötz, Otten, Wittmann, Schmidt, Kohls, Meissner. Time perception, mindfulness and attentional capacities in transcendental meditators and matched controls. Personality and Individual Differences.

 

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 (mukesh@cbcs.ac.in)
Doctoral student,
Centre of Behavioural and Cognitive Sciences (CBCS), India.