Neural encoding of time: the striatum vs prefrontal cortex

The neural mechanisms of encoding timing are still controversial. According to one prominent hypothesis, time is encoded in local network dynamics – see a previous blog post dedicated to this issue. However, similar mechanisms (“population clocks”) have been linked to multiple areas across the brain, including the striatum, prefrontal and parietal cortices, and the hippocampus. Does this variety of brain regions reflect a specialisation of each area to track time at e.g. a different scale, or is time encoded in parallel in several regions?

To answer this question, Bakhurin et al. quantified and compared the degree of time encoding in two areas: the striatum and the orbitofrontal cortex (OFC). They acquired electrophysiological recordings in mice conditioned to receive a food reward (condensed milk) after a specific interval (2.5s) following an olfactory cue. Activity in both regions was measured simultaneously in 6 animals; in 5 further animals, only activity in the striatum was recorded. Spike sorting was used to isolate activity in single neurons (pyramidal cells in OFC and medium spiny neurons in the striatum). To quantify the degree to which each region tracks time, the authors used multivariate decoding – a multi-class support vector machine classifier, based on firing rates of multiple units – to estimate the elapsed time from neural activity. Ideally, feeding the data acquired e.g. 1s after the olfactory cue into the decoder would result in a correct estimation that 1s has elapsed since the cue. Using this technique, one can quantify whether neural activity in a given area is a better predictor of the actually elapsed time than neural activity in another area.

The results of this and several control analyses show that time can be decoded with higher fidelity from striatal activity than from prefrontal activity. This pattern of results – the striatum outperforming the OFC as a neural basis for decoding time – was robust and did not qualitatively change when using more or less neurons in each area; selecting units in the dorsal or ventral striatum, or in the medial or lateral OFC; or controlling for motor activity (animals licking in anticipation of the reward). These findings are interpreted by the authors in terms of the striatum providing a refined readout of upstream cortical activity. Thus, the striatum might outperform the OFC in encoding time per se. However, as the authors also note, neural activity in the OFC has a higher dimensionality than in the striatum (i.e., more principal components are needed to explain its variability). This might be due to the OFC encoding more task variables than the striatum, as suggested by the authors; however, it can also be explained by a higher anatomical or physiological variability, or a lower signal-to-noise ratio, in the OFC. Thus, it would have been beneficial for the study to include a task variable – perhaps reward accumulation over several trials – for which prefrontal activity would plausibly yield better decoding than striatal activity.

While the study shows differences in decoding performance between the two regions, it rarely addresses the question whether time encoding mechanisms are qualitatively similar or distinct between the two regions. The one finding that does suggest differences in how time is encoded by the two regions shows that motor responses distort time encoding more in the striatum than in the OFC. Specifically, training the decoder on trials in which animals displayed licking behaviour early on (first tercile) or relatively late (third tercile) induced systematic biases when the decoder was tested on the remaining trials (second tercile). Thus, in the striatum, motor responses seem to warp time encoding in the opposite directions: early motor response speed up in estimated time, while late motor responses induced delays in estimated time*. These effects were less pronounced in the OFC. In fact, early prefrontal activity seemed to be especially robust to any interference from motor responses.

Taken together, the paper shows that decoding elapsed time is overall more accurate based on striatal activity than on prefrontal activity – however, why this is the case remains an open question. On the other hand, striatal time-encoding activity might to some extent covary with motor-encoding activity. This co-dependency of time and motor encoding is weaker in the prefrontal cortex, suggesting intriguing qualitative dissociations between the neural mechanisms of time encoding in different regions. Previously, decoding based on different data modalities (MEG and fMRI) was used to find correlations and dissociations between decoding-enabling data features (e.g., early response latencies in the MEG and sensory regions in the fMRI). Perhaps future studies could use a similar approach to find whether time representation in one brain region can generalise to another region, suggesting shared mechanisms, or whether time encoding is subserved by neural mechanisms unique to each region.

Ryszard Auksztulewicz, Oxford Centre for Human Brain Activity 

Source article: Bakhurin KI, Goudar V, Shobe JL, Claar LD, Buonomano DV, Masmanidis SC (2016) Differential encoding of time by prefrontal and striatal network dynamics. J Neurosci, December 15, 1789-16. doi: 10.1523/JNEUROSCI.1789-16.2016

* In my original post, based on the published article, the sentence stated the opposite: “early motor response induce delays in estimated time, while late motor responses speed up estimated time”. However the Authors have asked me to correct this sentence according to their original intention, and have requested a correction in the journal article.

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

1st Timing Research Forum Conference – Call for Abstracts

The call for abstracts and symposium proposals for the 1st Conference of the Timing Research Forum (TRF1) is now open. TRF1 is the first international conference dedicated to multidisciplinary research on timing and time perception and will be held in Strasbourg, France from October 23-25, 2017.
The deadline to submit both abstracts and symposium proposals is May 1, 2017. During the submission process, please indicate if you wish to present an oral or poster presentation, and express your interest for student travel awards. All selected abstracts will be published in the journal, Timing and Time Perception Reviews.
We are pleased to announce three impressive Keynote Speakers –
Warren Meck:          Professor, Duke University
Lera Boroditsky:     Associate Professor, University of California San Diego
Sofia Soares:           PhD Candidate, Champalimaud Centre for the Unknown
Sofia will give the Prize Keynote Lecture based on her paper – Soares, S., Atallah, B. V. & Paton, J. J. (2016) Midbrain dopamine neurons control judgment of time. Science 354, 1273–1277, that was selected by the TRF Committee as the ‘Best Timing Paper of 2016’.
For further details about submission guidelines, conference program, registration, accommodation etc., please visit the conference website – https://trf-strasbourg.sciencesconf.org.
We thank our current Sponsors – Wellcome Trust, INSERM, CNRS, Neuropole Strasbourg, FMTS and the University of Strasbourg and look forward to further proposals of sponsorship. Our sponsorship booklet is available here.
Anyone interested in joining the Timing Research Forum can do so by following the instructions at this link.
We look forward to seeing you in Strasbourg!
Anne Giersch & Jenny Coull 
Conference Co-Chairs
&
Sundeep Teki & Argiro Vatakis
(On behalf of Timing Research Forum)

The phase of pre-stimulus alpha oscillations influences the visual perception of stimulus timing

Over the last decade or so, there’s been an absolute explosion in the interest in neural oscillations and their role in perception. In particular, I and others are very interested in how neural phase, assumed to reflect fluctuations in neuronal excitability, affects perception on a moment-to-moment basis. A new paper by Alex Milton and Christopher Pleydell-Pearce uses EEG to examine the role of neural alpha phase (in the 8–13 Hz range) in the perception of timing – in this case, asynchrony versus simultaneity of visual onsets.

Participants are cued (validly or invalidly) to either the left or right, and then two peripheral LEDs are illuminated with a stimulus onset asynchrony (SOA) chosen to keep asynchrony detection at threshold for an individual participant (sometimes they are simultaneous, but that’s rare and only to estimate false-alarm rates). When the LEDs were illuminated during the trough of the alpha oscillation (measured over a handful of left, posterior sensors), they were more likely to be correctly perceived as asynchronous, but when they were illuminated during the peak of the oscillation, they were more likely to be incorrectly perceived as simultaneous. The results replicate and extend older work by Varela. And they provide a new source of trial-to-trial variability in asynchrony judgments that may also have individual-differences components due to e.g., differences in individual alpha frequency.

I found myself wondering while reading what the explanation for their result was – specifically, was asynchrony more likely to be perceived because the individual events and their onsets were better perceived during more excitable phases of the neural oscillation [based on my current understanding of near-threshold detection/discrimination data]? –OR– are individual LED onsets that are “transmitted” during successive alpha cycles perceived as separate, but bound if they end up in the same alpha cycle together [a la Lisman & Idiart’s theory that individual items in working memory are “stored” in single cycles of a high-frequency gamma oscillation that are nested in a single cycle of a low-frequency theta oscillation, and consistent with Varela’s ideas]? The authors address this question in the Discussion, and assign the two possibilities to the two sides of the debate about whether perception and underlying “processing epochs” are continuous or discrete, respectively. They suggest that fluctuations in neuronal excitability leading to enhancement of the perception of the LEDs and their temporal relation would be an unlikely mechanism to strictly quantize sensory input (and suggest that their own results are more compatible with a continuous view of perception). On the other hand, assuming that the LEDs might be perceived as asynchronous when they ride along in separate alpha cycles is compatible with a “temporal framing” hypothesis; sensory information is gated into discrete “packets” (see for example VanRullen & Koch, 2003).

The arguments for continuity generally appeal to intuition; it’s of course true that our perception of the world flows from one moment to the next without sharp boundaries. But the most basic building block of brain function, a spike from a single neuron, is all or none – a neuron fires or it doesn’t. In between, there are psychophysical and neural data that can be interpreted as supporting both views. So the issue is far from solved. I dislike fractionation and dichotomies, especially in the context of brains, which seem quite hard to parcel up cleanly. So I’m a fan of the idea that both continuity and discreteness are not only present, but necessary for brain function and cognition (Fingelkurts & Fingelkurts, 2006). I don’t have time in a short blog post to talk about HOW perception and cognition might arise from a critical combination of continuous and discrete neural processes, but highly recommend the cited paper as supplementary reading. The authors suggest directions for future research (such as examining symptoms of certain neuropsychological disorders) that may help to better understand the continuity/discreteness trade-off. And with a better understanding of how both discreteness and continuity might be essential for consciousness and cognition, our interpretation of results like those of Milton and Pleydell-Pearce might allow us some insight into neural mechanisms that doesn’t rely on assuming one or the other, but not both, must be true with respect to continuity vs. discreteness.

–Source article: Milton & Pleydell-Pearce.The phase of pre-stimulus alpha oscillations influences the visual perception of stimulus timing. NeuroImage.

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, mukesh@cbcs.ac.in

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.

Iterated reproduction task reveals rhythmic priors associated with exposure to music

According to Bayesian theories of cognition, perception involves integration of noisy sensory information with probabilistic internal models. These internal models reflect the net sum of all of our prior experiences and assist in structuring perception in the presence of unreliable sensory input. Known as priors, the influence of internal models can most clearly be observed in situations where sensory input is weak. In these cases, the prior makes a much larger contribution to perception, effectively biasing perception toward events that are more commonly encountered.

 

Musical practices are observed throughout all cultures and each musical system emphasises different rhythmic signatures. Is it possible that exposure to music forms rhythmic priors that help structure our perception of auditory sequences, and if so, are these rhythmic priors influenced by culture?

 

To assess rhythmic priors, Nori Jacoby and Josh McDermott from MIT devised an iterated reproduction task wherein participants tapped in time with auditory sequences comprised of repeating three-interval rhythms (e.g., 3:2:1, 1:2:1). On each trial the researchers surreptitiously replaced the auditory sequence with the rhythm produced by the participant from the previous trial. The idea of this procedure is that if temporal priors help structure the perception of musical sequences, then the rhythms produced by participants over successive trials should gradually become biased toward these priors. Indeed, the authors showed that reproductions tended to drift from the initial sequence and then stabalise after only five trials.

 

However, to ensure that the task itself was not biased toward cultural norms, the initial rhythm was randomly generated. In western music, interval ratios are usually comprised of integers. So to prevent the task from being influenced by western music conventions, the initial trial was randomly selected from all possible interval ratios, including non-integer values.

 

Despite the rhythms being randomly generated, reproductions tended to converge toward sequences with integer ratios. Importantly this effect was observed in a range of control experiments designed to rule out the role of motor demands. For example, the result was not specific to the effector since an integer bias was found when participants provided a verbal response. Likewise, a ratio bias was observed when sequences were reproduced from memory, indicating that the effect was not due to auditory-motor entrainment associated with synchronisation tasks.

 

Indeed, the effect of priors was also apparent in perceptual discrimination tasks. Participants were presented sequences that varied along a continuum between 3:2:3 and 1:1:1 and performed a same-different judgement task on pairs of sequences. Discrimination performance showed a pattern characteristic of categorical perception, with increased sensitivity found for non-integer rhythms and decreased sensitivity for rhythms near to integer ratios. The loss of perceptual sensitivity near integer patterns is indicative of a prior drawing the perception of patterns toward integer rhythms.

 

Crucially, the integer bias uncovered by the iterated reproduction task was influenced by exposure to music. In American participants, biases were observed only for ratios commonly found in western music. Likewise, a remote Amazonian population – the Tsimane – also showed a bias for integer ratios, however in this case, biases were only shown for intervals found in Tsimane music. However, the effect of the priors appeared to reflect passive exposure to common rhythmic structures, as American musicians also showed the same pattern of integer bias as Americans with no musical training.

 

Although Amercian and Tsimane cultures differed in the profile of intervals associated with priors, both cultures showed preferences for integer ratios. The Tsimane are a remote population with almost no exposure to western culture so it is unlikely that cultural transmission can explain a preference for integer ratios in the Tsimane. So this begs the question, how is it that both groups show priors for integer rhythms? Although iterated reproductions are often used in social science to explore the dynamics associated with the formation of shared practices, attitudes and beliefs, the authors’ stress that the task used here does not recapitulate the development of rhythmic preferences. Instead they argue the task only uncovers pre-existing internal preferences. How widespread such preferences are across different cultures and why preferences for integer rhythms emerge remains to be seen.

 

Bronson Harry

The MARCS Institute, Western Sydney University

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