Mental context biases retrospective temporal judgements

Our sense of time spans multiple scales; from seconds to minutes and days to years. Judgements for different temporal intervals however do not rely on a single unitary timing system, but instead rely on separate neural networks. For example, judgements within the sub- and supra-second range rely on networks involved in motor control (basal ganglia and cerebellum), whereas brain regions involved in long term memory and spatial navigation (prefrontal cortex and hippocampus) are involved in judgements spanning weeks and years.

A new functional magnetic resonance imaging study, led by researchers from Princeton, fills the gap in our understanding of how we judge time across intermediate timescales. The study examined time perception in the range of minutes by testing how accurately participants could estimate the amount of time that had elapsed between short excerpts taken from a 25-minute, science-fiction radio story.

The study investigated the idea that temporal estimation for intermediate intervals is related to the degree to which events are associated with similar contextual cues. This idea is based on theories of memory which posit that the recency of events can be ascertained by retrieving slowly varying contextual representations associated with global mental states. These representations include external environmental features (i.e., spatial location) and internal states such as goals and emotions. According to this theory, contextual cues bias temporal judgements such that the interval between events containing similar contextual features should be underestimated, whereas the the interval between events containing few contextual features should be overestimated.

To test this theory, participants listened to the radio story while brain activity was measured with functional magnetic resonance imaging. After the scanning session, participants completed a surprise temporal judgement test. Participants were presented two short clips that were either 2 or 6 minutes apart and were asked to estimate the time that elapsed between each excerpt. The degree to which these target clips were associated with similar mental context was estimated by examining brain activity recorded while participants initially listened to each clip. The authors used multi-voxel pattern analysis, a method that exploits distributed patterns of brain activity within a region of interest to measure the neural representations formed by different perceptual and cognitive states. MVPA was carried out by correlating the pattern of neural responses (across voxels) evoked by each clip with the idea that clips that share similar content, should also evoke highly correlated patterns of brain activity.

A region of interest analysis showed that pattern similarity in the right entorhinal cortex was correlated with temporal estimates. That is, clips that evoked similar patterns of brain activity within this region were associated with shorter duration estimates in the temporal judgement test. This result was also found when the correlations between judgements and pattern similarity were calculated for each clip pair across participants, indicating that variations in temporal judgements were not solely due to clips sharing perceptual features (i.e., if clips shared similar music).

Evidence that temporal judgements are based on representations formed in the entorhinal cortex is consistent with this region’s’ role in binding event content (i.e., objects, people, actions) within a broader spatial and temporal context. Indeed, a follow up analysis that examined the auto-correlation of evoked patterns within the entorhinal cortex showed that pattern similarity in this region fluctuated more slowly throughout the story than in neighbouring lateral temporal lobes. Together these results confirm the major predictions of the mental context theory of temporal estimation: temporal judgements are based on slowly varying representations that bind event content within broader contextual cues.

It remains to be seen whether the entorhinal cortex plays a general role in retrospective duration estimates for different tasks, contexts and timeframes. One possibility is that this region is particularly attuned to the temporal relations between events in spoken narratives. Depending on the nature of the story, the temporal relationships between events in a spoken narrative may be somewhat compressed compared to everyday experiences. In this case, it might be expected that the entorhinal cortex usually supports temporal judgements over hours and days in more naturalistic contexts.

Research published by the same group has shown that brain regions appear to be attuned to different temporal frequencies, a finding that most likely reflects the kind of representations formed within a region. It might be possible that temporal judgements for other timeframes (i.e., tens of seconds or hours) or different event content (daily activities, details of a conversation) may rely on other brain regions that are better suited for retrieving key information about different experiences.

 

Bronson Harry

The MARCS Institute, Western Sydney University

Society for Neuroscience 2016

Wow! As always, SfN 2016 was a completely full-on experience. This year was in San Diego, which is obviously the only appropriate location for a conference of that size that is consistently held in October/November. Saturday afternoon featured a ‘Temporal Processing’ poster session, which looked excellent. The session included work from labs around the world presenting on a great range of timing-related topics. Unfortunately (but also fortunately), I presented my own poster during this session and so saw zero other posters. But it’s good to be busy during your own poster session. The highlight for me was that many from the session joined for a post-poster drink, so there was still an opportunity to interact with many presenters and their co-authors, some more scientifically and some more socially. There was also a session on ‘Neural Circuits for Timing, Temporal Processing, and Sequences’ on Tuesday afternoon and another ‘Temporal Processing’ session Wednesday morning. And I’m sure there were uncountable things I missed.

The most inspiring thing I personally saw at SfN was a talk by Flavio Fröhlich in a symposium on “Advances in Noninvasive Brain Stimulation Along the Space-Time Continuum”. Although neither the symposium nor the talk were on timing per se, I think the sky is the limit in terms of applying noninvasive brain stimulation techniques to understand the neural bases of timing, most obviously in the domain of rhythm and beat perception, where neural oscillations and neural entrainment might be manipulated. The talk (“From biophysics to treatment: rational design of non-invasive brain stimulation to modulate thalamo-cortical oscillations”) provided a rapid-fire introduction to work in the Fröhlich lab spanning levels from mathematical/computational modeling of thalamocortical circuits to clinical trials testing the efficacy of noninvasive brain stimulation for post-traumatic stress disorder and depression (and covering everything in between).

All of the work builds from the idea that ongoing neural oscillations can be “picked up”, or entrained, by noninvasive brain stimulation. The novel insight that it provided though, which is simultaneously completely obvious and not obvious at all (at least for me), is that it’s not necessarily sufficient to blast a brain with stimulation in order to cause entrainment or to cause the presence of a neural oscillation of a particular frequency. If the target neural signal isn’t present at the time stimulation is applied, we can’t expect it to be entrained or enhanced. For example, applying noninvasive brain stimulation that mimics the shape of sleep spindles to an awake person doesn’t do much. But, when applied to a sleeping person for whom sleep spindles are naturally present, transcranial alternating current stimulation (tACS) that enhances those sleep spindles actually enhances motor memory, in particular when the stimulation is specially designed to match the sleep spindles of that particular person! Despite being completely mind-blowing, this actually makes complete sense from a dynamical systems perspective.

Given the current hype surrounding the involvement of neural oscillations and neural entrainment in rhythm and beat perception (into which I definitely buy), it seems that a natural next step is in the direction of noninvasive brain stimulation techniques (and in particular what one might think of as “time-domain” techniques, such as tACS). Can we shift the metrical interpretation or phase alignment of the beat in an ambiguous rhythm?; can we disrupt the perception of a beat altogether?; can we improve beat perception for syncopated rhythms or for individuals that are weak beat perceivers to begin with? All of these would contribute to our understanding of how neural oscillations and entrainment are related to beat perception. But I think the key take-away message from the talk is that null results cannot and should not be interpreted as evidence that brain stimulation won’t deliver answers to these questions (a problem very much not specific to brain stimulation or beat perception). Instead, whether the neural “conditions” are right should be at the center of any forays we make into the world of noninvasive brain stimulation. I suggest that this might require paying careful attention to individual differences, which are very apparent in the context of rhythm and beat perception – this is in line with growing interest in personalized medicine. Tuning our approaches by knowing about the neural preferences an individual person in a particular context may allow us to develop more effective treatments to improve, for example, movement and gait in Parkinson’s disease or memory in Alzheimer’s disease.

– Molly Henry, University of Western Ontario

Does Sense of Smell affect Sense of Time?

Imagine you are walking in a park, which has lots of roses and the air is filled with aroma. You move softly and start taking deep breaths just to appreciate more of rose aroma. You feel nice and relaxed … but wait, what happened to your sense of time. Did you notice that your subjective time is not in sync with the physical time? Or take another example when you are walking and you happen to encounter a bad or intolerant odor, again you experience altered sense of time. Though these incidents are common, time researchers have rarely addressed this question- “Does sense of smell affects sense of time?”

A recent study published in Frontiers in Psychology (2016), by Jean-Louis MILLOT [Université de Franche-Comté, France] and his collaborators have tried to shine some light on the odor and time perception relationship. They used decanoic acid (capric acid), which is a saturated fatty acid having an unpleasant goat like odor, as their stimulant. This odorant was used, as they wanted to study the effect of aversive or negative odor on time perception. Subjects were asked to wear a mask soaked in 1ml pure decanoic acid for odor condition and 1ml diethylphtalate, an odorless diluent, for control or without odor condition.

Temporal bisection task was used to measure time perception of auditory stimuli (white noise) in the presence or absence of odorant. This study was performed with two groups (N=36 each), for one group the time range was in sub-second (centered around 400ms) and for other group it was in supra-second (centered around 2000ms). Two time ranges were used to investigate whether odor influences time perception in a multiplicative manner (i.e. by increasing the pacemaker speed). The logic is if any factor influence pacemaker speed then the effect should change as the function of actual duration.

After the initial standard duration training, the experiment consisted of two test blocks. In each group, half of the participants performed the training and first test block without odorant and second test block with odorant. On the other hand, remaining half of the participants performed the training and first test block with odorant and second test block without odorant.

Results showed that irrespective of whether the odorant was used in either first or second test block, participants underestimated the auditory stimulus in the presence of odorant compared to without odorant condition for sub-second time range whereas participants overestimated the auditory stimulus in the presence of odorant compared to without odorant condition for supra-second time range.

They used attention gate model to explain their findings for sub-second time range. In attentional gate model, it is assumed that the attention governs the gate of the internal clock, such that if more attention is allotted to temporal processing then the gate remains closed for longer time leading to more pulses getting accumulated in the accumulator. If some stimulus grabs more attention (non-temporal processing) then the amount of attention left for temporal processing reduces and hence will lead to lesser number of pulses being accumulated in the accumulator. In the sub-second range the attention was diverted away from time to the odor, which led to the accumulation of lesser number of pulses in the accumulator compared to the without odor condition.

But the story is more complicated as opposite effect of odor was seen for supra-second range. They explain this result by suggesting that unpleasant odor induce negative emotion and increase arousal. And such increase in arousal increases the pacemaker speed, leading to temporal expansion.

The current study may not answer all the questions about how odor influences time perception, but it definitely gives some initial inputs for researchers who wish to further investigate the unexplored territory of Odor affecting Time. For those who are interested in this topic these are some additional work on odor and time perception [Brand et al. (2016), Giovannelli et al. (2015), Schreuder et al. (2014), and Yue et al. (2016)].

References:
Brand, G., Thiabaud, F., & Dray, N. (2016). Influence of Ambient Odors on Time Perception in a Retrospective Paradigm. Perceptual and motor skills, DOI: 10.1177/0031512516647716
Giovannelli, F., Giganti, F., Saviozzi, A., Rebai, M., Marzi, T., Righi, S., … & Viggiano, M. P. (2015). Gender Differences in Time Perception During Olfactory Stimulation. Journal of Sensory Studies. doi:10.1111/joss.12191
Schreuder E, Hoeksma MR, Smeets MA and Semin GR (2014) The effects of odour and body posture on perceived duration. Front. Neurorobot. 8:6. doi:10.3389/fnbot.2014.00006
Yue, Z., Gao, T., Chen, L., & Wu, J. (2016). Odors Bias Time Perception in Visual and Auditory Modalities. Frontiers in Psychology, 7, 535. http://doi.org/10.3389/fpsyg.2016.00535

Source article:
Millot, J.-L., Laurent, L., & Casini, L. (2016). The Influence of Odors on Time Perception. Frontiers in Psychology, 7, 181. http://doi.org/10.3389/fpsyg.2016.00181

— Mukesh Makwana,
Doctoral Student,
Centre of Behavioural and Cognitive Science,
University of Allahabad, India.

Job opportunities in Cardiff (UK)

Information received from Dr. Marc Buehner:
The School of Psychology at Cardiff University is currently seeking to appoint up to FOUR outstanding individuals at Lecturer, Senior Lecturer, Reader or Professor level who can complement and extend our research and impact strengths, and provide engaging teaching. We are particularly looking for individuals with a strong research portfolio in Cognitive Science and/or Cognitive Neuroscience.
For more information, please visit:

Do beta oscillations predict the timing of upcoming stimuli?

Several noninvasively measured neural signatures of predicting events in time have been proposed so far. These include the contingent negative variation – a slow build-up of the EEG potential before an expected stimulus; similar time-dependent modulation of alpha-band power (8-13Hz); and low-frequency entrainment, such as delta-band (1-3Hz) synchronisation to slow rhythmic streams. Beyond these candidate mechanisms, also beta-band activity has received considerable interest – perhaps not surprisingly, given its importance for both sensory processing and motor processing. In the sensory domain, beta-band oscillations have been proposed to carry sensory predictions within the influential predictive coding framework. Accordingly, increased beta power preceding expected stimuli have been observed in several studies (reviews here). In the motor domain, however, beta (13-30 Hz) power typically decreases before voluntary movements as well as anticipated events, and motor activity has been suggested to underpin predictive timing in sensory regions. So are differences between the neural implementation of perception and action sufficient to reconcile these conflicting findings?

In their recent paper in Neuroimage, Meijer, te Woerd and Praamstra from Radboud University Nijmegen show that both decreases (event-related desynchronisation; ERD) and increases (event-related synchronisation; ERS) in the beta band can be observed after visual stimuli in a sequence with predictable timing. However, neither the positive (ERS) nor the negative (ERD) peaks seem to be shifted in time by the predicted onset of the upcoming stimulus – which contradicts the previous findings mentioned above.

In the experiment designed by Meijer et al., participants were asked to attend to a series of rhythmically presented visual stimuli and count the number of incongruent stimuli in the series. Incidentally, the stimuli consisted of clock arrows and digits; the digits could be congruent or incongruent with the hour indicated by the clock arrows. Even though clock displays were used as stimuli, what was shown on the clock was randomised over stimuli and thus unpredictable; the only predictable information was when the clocks were shown (every 1050, 1350 or 1650 ms). While participants were viewing the stimuli, their neural activity was recorded using 128-channel EEG.

The results from different groups of electrodes (frontal and parietal/occipital) show that during the first 800 ms after each stimulus, the time-course of the beta-band is not predictive of when the subsequent stimulus will appear – in the sense that beta-band ERS peaks at the same latency for all three temporal conditions. The authors rightly interpret this lack of temporal specificity of the ERS peak as disconfirming previous hypotheses – at least in their narrow interpretation – that the latency of the beta maximum before an upcoming stimulus should linearly depend on the expected onset of that stimulus.

However, the figures reported in the paper also suggest that, following the last positive peak at ~800 ms after each stimulus, the speed (or slope) of beta-band desynchronisation might be predictive of when the next stimulus will appear. While the authors address this pattern of results only in passing and dismiss them as inconclusive, it should be noted that the relatively weak modulations in beta power towards the end of the trial are offset by prominent (and largely unexplained) modulations at the beginning of the trial. This is most likely due to the choice of baseline – it would be interesting to see how the results would change if the beta time-courses were normalised with respect to a period preceding the first stimulus in a sequence, instead of the average of the entire trial. In other words, while the exact timing of the maximum beta power does not appear to be modulated by the expected onset of an upcoming stimulus, more subtle beta-band mechanisms – such as the speed of beta desynchronisation just before an anticipated stimulus – might still be at play.

Another possibility, not tested here, would be that – rather than absolute beta power in a given region –beta-band connectivity between regions (e.g. the influence of frontal/motor activity on posterior/sensory regions) would be a more likely candidate for implementing predictive signals about upcoming events, consistent with the interpretation of beta-band synchronisation as mediating neural predictions between different cortical areas. Thus, while the results reported in this paper show that the peaks of beta-band synchronisation are not temporally specific with respect to anticipated events, to actually understand the possible role of beta oscillations in signalling temporal predictions we will need more insights about the neural sources and targets of the observed beta-band increases and decreases, and the connectivity influences explaining the beta-band dynamics.

Ryszard Auksztulewicz, Oxford Centre for Human Brain Activity

Source article: Meijer D, te Woerd E, Praamstra P (2016). Timing of beta oscillatory synchronization and temporal prediction of upcoming stimuli. NeuroImage 138:233-241. doi: 10.1016/j.neuroimage.2016.05.071

Beat keeping in a Sea Lion as Coupled Oscillation: Implications for comparative understanding of human rhythm

Interest in whether the ability to pick up on and synchronize with a beat in musical rhythm is uniquely human or may be more widespread throughout the animal kingdom is on the rise. In fact, non-human animals’ musical abilities were the topic of a dedicated symposium at the International Conference on Music Perception and Cognition earlier this year (“Music Perception & Cognition Across Species”, featuring talks on chimpanzees, several bird species, and Ronan the sea lion [admittedly not all of which were necessarily focused on rhythm and beat perception]). Most tests of non-human animals’ synchronization abilities have been conducted with metronomes or metronome-like stimuli, and some evidence for synchronization abilities has been reported for bonobos, chimpanzees, and budgerigars. Several papers have also documented synchronization to more complex rhythmic stimuli, like real music – however, it’s important to distinguish between flexible, anticipatory synchronization and “bouts of synchronization” that may reflect a transient phase alignment between two oscillators with similar tempi (which will eventually and transiently occur for any two uncoupled oscillators with similar but not perfectly matched periods). Moreover, if we’re to conclude that an animal or animal species is capable of human-like beat perception or synchronization, then direct experimental manipulations such as perturbations to the rhythm are necessary – that way, we can observe compensatory dynamics of the animal’s behavior in order to infer the properties of the underlying neural or behavioral oscillator that make synchronization possible. That’s exactly what a recent paper by Andrew Rouse, Peter Cook, Edward Large, and Colleen Reichmuth has done.

The paper focused on Ronan (a sea lion), who has previously been shown to be capable of synchronization (of head bobs) to the beat in real music, even when tempo shifted (excellent YouTube video available here: https://www.youtube.com/watch?v=6yS6qU_w3JQ). In the current paper, Ronan synchronized to metronome-like stimuli that contained phase or period perturbations of different magnitudes and in different directions (advance/delay, speeding/slowing). The authors examined the patterns of compensatory behavior exhibited by Ronan in response to these perturbations. Importantly, the design allowed the authors to fit Ronan’s data with nonlinear equations that describe the behavior of coupled oscillators (here the coupling is between Ronan’s head bobs and the metronome stimulus). The model estimates the extent to which Ronan needed to adjust the phase and period of her head bobs to resynchronize with the metronome following a perturbation.

Ronan’s behavior (and model fits to that behavior) revealed that Ronan flexibly synchronized with metronomes with different tempi (similar to her music synchronization performance). She was also able to adapt to both phase and period perturbations to the stimulus, getting back into sync within a handful of intervals. Similar to what is normally observed for humans, Ronan’s phase correction was stronger than her period correction (tempo adaptation). In contrast to humans though, who normally show relatively constant phase correction estimates (and even react to subliminal phase perturbations), Ronan’s degree of adaptation to phase perturbations scaled with the magnitude of the perturbation. Ronan also seemed to show weaker period correction than humans typically do. However, with respect to these two divergences from human literature, it is notable that Ronan’s performance was not directly compared to a human sample performing the same task with the same stimuli.

Overall, the experimental design (introducing perturbations in rhythmic stimuli) and approach to analyzing the data (involving fitting coupled-oscillator models) are, in my opinion, a model of where investigations into non-human beat-keeping abilities should be going. To my mind, the only missing piece is the direct comparison to a human sample, as it’s unclear whether discrepancies between Ronan’s performance and previous human data are fundamental or might stem from something uninteresting like the nature of the stimulus (which for Ronan is filled with a cycle of a frequency modulation, diverging from the types of stimuli that are often used in human studies). Regardless, the similarities between Ronan’s performance and typical human performance certainly outweigh the differences, and provide further evidence that beat perception and synchronization may not be specific to humans or to vocal learning species. Instead, the ability to synchronize in a flexible, anticipatory way might be more universal than once thought, potentially stemming from common neural circuitry that gives rise to oscillatory activity capable of synchronizing with environmental rhythms. Demonstrating this in different animal species may simply be a matter of identifying an appropriate behavior or task that allows a particular species to show off their skills, rather than expecting that a human-centric task like finger tapping will be the key to revealing cross-species similarities in beat perception abilities and music abilities more generally.

–Molly Henry, University of Western Ontario

Source article: http://journal.frontiersin.org/article/10.3389/fnins.2016.00257/full

PhD studentship for a study on interval timing

The School of Psychology, University of East Anglia (Norwich, UK),  is offering fully-funded opportunities for doctoral students for 2017/18 entry (https://www.uea.ac.uk/psychology/research-degrees). One of the selected topic is on interval timing (under the supervision of Dr Zhenguang Cai). The studentship will cover the tuition fee and stipend and is eligible to UK, EU and international students. The deadline for application is 30 January 2017.

University of East Anglia is a research-intensive university located in the beautiful city of Norwich, east of England. In the 2016 National Student Survey, the university was ranked joint 3rd out of all English mainstream universities, with the School of Psychology receiving an overall satisfaction rating of 92 per cent. Guardian University Guide 2017 has ranked the School of Psychology in the Top 10 Psychology departments in the UK.

Interested applicants please email Zhenguang Cai (Zhenguang.cai@uea.ac.uk) for inquiry about the topic on timing and Dr Martin Doherty (Martin.Doherty@uea.ac.uk) for other matters.

TRF NEWSLETTER III-November 2016

TRF UPDATE

Our membership is growing strongly and we have now surpassed 415 members!

We have now expanded to 148 (+12%) followers on Twitter and 151 (+12%) subscribers on Facebook, respectively. We would like to acknowledge the efforts of our new Social Media Manager, Bowen Fund in spreading the work of TRF through social media. Join us and share your work and news!

TRF CONFERENCE

We are pleased to confirm the dates for the first TRF conference that will be held from October 23-25, 2017 at the University of Strasbourg, France. Led by Anne Giersch and Jenny Coull, conference organization and planning is underway. We will soon launch the conference website and announce the Scientific Program Committee, as well as further details about the meeting.

TRF BLOGS

We are pleased to share the first series of blogs written by TRF’s Associate Members. Please find below the links to the respective articles. You are welcome to discuss the article in the comments section of each page.

  1. Spontaneous eye blinks may explain moment to moment changes in time perception.

http://timingforum.org/spontaneous-eye-blinks-may-explain-moment-to-moment-changes-in-time-perception/

Review of:

Terhune DB, Sullivan JG, Simola JM (2016) Time dilates after spontaneous blinking. Curr Biol 26:R459–R460.

Author:

Mukesh Makwana

Centre of Behavioural and Cognitive Sciences, University of Allahabad

  1. Subsecond timing relies on dynamic excitability of cortical circuits.

Subsecond timing relies on dynamic excitability of local cortical circuits

Review of:

Goel A, Buonomano DV (2016) Temporal Interval Learning in Cortical Cultures Is Encoded in Intrinsic Network Dynamics. Neuron 91:320–327.

Author:

Ryszard Auksztulewicz

Oxford Centre for Human Brain Activity

  1. Does judgment certainty influence systematic under-reproduction of time?

http://timingforum.org/does-judgment-certainty-influence-systematic-under-reproduction-of-time/

Review of:

Riemer M, Rhodes D, Wolbers T (2016) Systematic Underreproduction of Time Is Independent of Judgment Certainty. Neural Plast 2016:6890674.

Author:

Bharath Talluri

University Medical Centre, Hamburg-Eppendorf

  1. Structural coupling between auditory and motor networks is associated with sensorimotor synchronisation performance.

http://timingforum.org/structural-coupling-between-auditory-and-motor-networks-is-associated-with-sensorimotor-synchronisation-performance/

Review of:

Blecher T, Tal I, Ben-Shachar M (2016) White matter microstructural properties correlate with sensorimotor synchronization abilities. NeuroImage 138:1–12.

Author:

Bronson Harry

MARCS Institute for Brain, Behaviour and Development

 

BLOG YOUR PAPER

We would like to invite TRF members to submit short summaries of their recently published articles on timing. Articles should be no longer than 500 words and not include more than one representative figure. Please submit your entries after your paper is published by emailing us at trf@timingforum.org. Submissions are open anytime and will be featured on the TRF blog page – http://timingforum.org/category/blog/.

 

BLOG YOUR CONFERENCE

We would like to invite TRF members to write about their experience of a timing conference/meeting/workshop that they have recently attended. Submissions can highlight prominent talks/papers presented, new methods, trends and your personal views about the conference. Pictures may also be included. Submissions should be no longer than 1000 words. Please submit your entries to trf@timingforum.org within two months from the date of the conference.

To kickstart this new initiative, we request participants at the Annual Meeting of the Society for Neuroscience to share their views and highlights from timing research from the meeting.

 

TRF on ResearchGate

TRF is now accessible on ResearchGate as a project and has >80 followers already!  

https://www.researchgate.net/project/Timing-Research-Forum

Everyone is invited to follow the page to receive regular scientific updates from TRF. Please share and join!

 

Timing Research at SfN

Several TRF members will be attending the Annual Meeting of the Society for Neuroscience. Please feel free to share your abstract links, live updates, pictures etc. with us on Twitter (use #SfN16TRF).

Some relevant timing sessions are highlighted below –

Human Cognition: Temporal Processing I

http://www.abstractsonline.com/pp8/index.html#!/4071/session/1232

Human Cognition: Temporal Processing II

http://www.abstractsonline.com/pp8/index.html#!/4071/session/1236

Temporal factors of Crossmodal integration

http://www.abstractsonline.com/pp8/index.html#!/4071/session/506

Neural circuits for timing, temporal processing, and sequences

http://www.abstractsonline.com/pp8/index.html#!/4071/session/716

 

MODULARITY IN TIMING WORKSHOP – January 19, 2017, Liverpool

Ruth Ogden and Alexis Makin are hosting a one day workshop in Liverpool (UK) to explore modularity in time perception. The aim of the workshop is to explore whether current research supports the idea of single or multiple timers. Abstracts for talks and posters are being accepted until the end of November.

Further information and registration can be found at: https://www.ljmu.ac.uk/conferences/eps-workshop.

Postgraduate students and post-docs wishing to attend can apply to the Experimental Psychology Society’s Grindley Grant scheme to fund travel and accommodation: http://www.eps.ac.uk/index.php/grindley-grants-for-conference-attendance

Please contact Ruth or Alexis if you have any questions r.s.ogden@ljmu.ac.uk

 

CONTRIBUTE TO TRF

TRF aims to host timing related resources, so that TRF‘s website will be the one stop for everything related to timing. Currently, the TRF website has these resources: all members’ publications, timing related special issues, and books on timing, a list of meetings focused on timing, a list of timing related societies/groups, as well as code and mentoring resources.

Structural coupling between auditory and motor networks is associated with sensorimotor synchronisation performance

Paced finger tapping tasks have been used extensively in brain imaging research to investigate the sensory and motor networks involved in the coordination of rhythmic movements. In comparison, much less is known about how these networks communicate to produce precisely timed actions. A paper published recently in Neuroimage provides new insight into the structural brain connections that underpin sensorimotor synchronisation (SMS) performance.

The study, conducted by Tal Blecher, Idan Tal and Michael Ben-Shachar at Bar Ilan University, explored the structural networks associated with two latent processes widely assumed to be associated with SMS performance: adaptation and anticipation. Adaptation and anticipation are two dissociable sensorimotor processes that are argued to help stabilise performance in SMS tasks. Adaptation refers to various reactive correction mechanisms that fine tune motor plans to minimise the asynchrony between actions and events. Anticipation on the other hand has been linked to the observation that actions (typically finger taps) tend to precede the pacing stimulus. Termed the negative mean asynchrony – the propensity for actions to occur before stimulus onset in SMS tasks suggests that participants do not merely react to stimulus onsets, but instead predict the timing of future events to ensure motor commands coincide with target stimuli.

To assess anticipation and adaptation, participants were instructed to tap in time with an auditory pacing stimulus that incorporated meter. Meter was marked by emphasising either every second tone (1 / 2 meter) or every third tone (3 / 4 meter). Participants were instructed to tap with their index finger for each emphasised tone, and to tap with their middle finger for all tones that were not emphasised. To assess adaptation, the meter presented to participants was changed at random intervals. The time taken to adjust the coordination of the index and middle fingers to the new meter – called time to resynchronise – was used as an index of adaptation. In contrast, to measure anticipation the mean asynchrony was calculated from performance data collected during auditory sequences that did not incorporate changes in meter (constant meter condition).

To examine the structural brain networks associated with adaptation and anticipation, mean asynchrony and time to resynchronise were correlated with brain imaging measures of white matter integrity. The authors used diffusion tensor imaging (DTI) – a technique that measures water diffusion – to identify the major white matter pathways in the brain. DTI exploits the propensity of water to diffuse freely only along the longitudinal axis of axons to delineate tissues that are composed of axons with uniform orientation, such as the major fibre tracts. In addition to tract identification, DTI can be used to estimate the microstructural integrity of the white matter pathways. One measure – called fractional anisotropy – quantifies the proportion of the total diffusion observed within a voxel that coincides with the primary direction of diffusion. High fractional anisotropy is related to microstructural tissue properties, such as the degree of axonal myelination, that are argued to facilitate communication between connected brain regions.

Using deterministic tractography, the authors focused their analysis on two white matter pathways involved in sensorimotor integration: the arcuate fasciculus and the corpus callosum. The arcuate fasciculus connects the superior temporal, inferior parietal and frontal lobes, and plays a prominent role in speech production, speech perception and action observation. In contrast, the corpus callosum connects homologous cortical regions in the left and right hemisphere. To limit the analysis of the corpus callosum to fibre tracts that link motor and auditory regions, the authors only examined the sections of the corpus callosum that corresponded to the pathways connecting bilateral pre-central gyrus (i.e., motor cortex) and bilateral temporal lobes (auditory cortex).

Analysis of the left arcuate fasciculus revealed a significant positive correlation between mean asynchrony and fractional anisotropy that was confined to an anterior portion of the tract. Given that observed mean asynchrony values were negative (i.e., distributed between -150ms and 0ms), this result indicates that participants with higher fractional anisotropy values were better able to synchronise with the auditory stimulus. The authors concluded that this finding adds evidence to the view that sensory motor integration relies on bidirectional coupling of brain regions involved in perception and action. Interpreting mean asynchrony as a measure of anticipation, these findings suggest that feedforward and feedback signals between frontal and temporal regions may be used to form predictions about the timing of upcoming auditory stimuli.

Fractional anisotropy in the pre-central segment of the corpus callosum was found to be negatively correlated with the time to resynchronise measure, indicating that increased integrity of the tract linking the left and right motor cortex was related to faster adaptation to changes in meter. To understand the behavioural significance of this finding, the authors decomposed the changing meter task into several underlying cognitive processes; meter change detection, new meter analysis, old meter inhibition, and execution of new motor plans. Based on evidence that callosal connections are predominantly inhibitory, the authors suggest that the pre-central callosal connections facilitate adaptation in the changing meter task via inhibition of the old meter.

Unexpectedly, fractional anisotropy in the temporal segment of the corpus callosum was found to be negatively correlated with mean asynchrony. Moreover, fractional anisotropy in this tract also correlated negatively with the standard deviation of asynchronies observed in the constant meter task. Taken together, these results indicate that participants with increased fractional anisotropy in this tract demonstrated less accurate and more variable performance in the tapping tasks. The authors provide two possible explanations to account for these apparently contradictory findings. Firstly the authors point out that the transmission of action potentials can be facilitated by either increased myelination and thicker axons. However, fibres comprising neurons with thicker axons should also demonstrate lower fractional anisotropy, as water would be free to diffuse more in directions perpendicular to the orientation of the axon. Alternatively, the authors also suggest that analysis of the auditory input might simply benefit from more lateralised analysis. In this case, sensorimotor synchronisation performance would benefit from decreased communication between the hemispheres.

In summary, these results seemingly point to the view that SMS performance is related to intra-hemispheric coupling between sensorimotor networks, with inter-hemispheric communication benefiting more complex tasks incorporating inhibitory processing. However, it is worth noting that the measures of SMS performance, particularly adaptation, depart considerably from those typically examined in sensorimotor synchronisation research. As noted by the authors, the changing meter task is likely associated with a range of cognitive processes. In contrast, models of SMS focus on much simpler forms of adaptation namely phase correction and period correction. These processes are thought to be carried about by functionally segregated timing networks not examined in this study. Future studies will need to examine these fundamental adaptive processes to determine whether they rely on different timing networks.

Does judgment certainty influence systematic under-reproduction of time?

Systematic under-reproduction of time is consistently observed in time reproduction tasks. One explanation for this bias is that it is impossible to implement the method of limits in time reproduction tasks due to anisotropy of time (Riemer, 2015). Since time, as a physical quantity, does not allow researchers to implement the method of limits or manipulations that other physical quantities enable, identifying the factors that influence the under-reproduction of time might help explain this bias and to identify the underlying mechanisms. The paper by Riemer et al., (Riemer, Rhodes, & Wolbers, 2016) investigates whether manipulating judgment certainty affects the magnitude of bias in time reproduction tasks. Judgment certainty is manipulated by applying continuous theta- burst stimulation (cTBS) to the right posterior parietal cortex (PPC) using non-invasive transcranial magnetic stimulation (TMS). In the present work, Riemer and colleagues used two different tasks, each under TMS and sham conditions to study the effect of judgment certainty on time reproduction.

A 2-AFC time discrimination task was used to verify and quantify the effect of the TMS on judgment certainty. Participants were required to compare two supra-second time intervals presented as filled acoustic stimuli and separated by a short inter-stimulus interval (ISI), and report whether the stimulus in the second interval was longer or shorter than the stimulus in the first interval. Applying cTBS over right PPC would give rise to transient inhibitory effects in the region and would result in an increase in the precision of judgments in the discrimination task, without affecting the overall mean accuracy. Behavioural data showed an average increase in the slope of psychometric function and an average decrease in the difference limen for TMS condition relative to sham condition (figure 1 below). This change in performance shows that inhibiting the right PPC indeed improves precision in time discrimination performance by increasing the judgment certainty.

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Figure 1: Psychometric functions showing average discrimination performance across subjects for TMS using cTBS of right PPC and sham conditions. Inhibiting right PPC improved sensitivity of subjects without affecting the mean accuracy, indicating an increase in judgment certainty (from Riemer, Rhodes, & Wolbers, 2016).

If judgment certainty is one of the factors that influences the systematic under-reproduction of time, then a similar inhibition of right PPC as above should result in a reduction of negative errors and reduction in response variability in time reproduction task. To verify the same, a time reproduction task was used where participants were required to reproduce a standard duration played in the first interval using filled acoustic stimuli. The standard interval had variable supra-second durations across trials and is separated from the reproduction interval by a short ISI. The reproduced intervals are then quantified as exponentials of the standard durations. Even though the behavioural data showed a systematic under-reproduction of time in both conditions, there was no difference in the reproduced durations (as quantified by the power functions) between TMS and sham conditions. Interestingly, even the variability of reproduced responses remained unchanged between the conditions (figure 2 below). This shows that increasing judgment certainty by inhibiting the right PPC did not have any effect on the under-reproduction of time. The authors also found no interaction of the estimates quantifying change in behaviour (if any) across TMS and sham conditions between discrimination task and reproduction task. This leads to a hypothesis that both tasks are based on different neural mechanisms.

 

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Figure 2: Left: Power functions quantifying reproduced durations on vertical axis as a function of standard durations on horizontal axis, averaged across subjects in both cTBS of right PPC and sham conditions. Increasing the judgment certainty by inhibiting right PPC did not have any effect on the under-reproduction of time. Right: Variability in reproduced durations for cTBS and sham conditions. Error bars represent SEM (from Riemer, Rhodes, & Wolbers, 2016).

Various factors might influence the mechanisms that give rise to negative errors in time reproduction tasks. If time discrimination and time reproduction are driven by the same neural mechanisms, then the factors that influence performance in time discrimination tasks should also influence performance in time reproduction tasks. In other words, judgment certainty which increases the precision in time discrimination judgments should also reduce the extent of under-reproduction in a time reproduction task. However, in the current study, it has been found that judgment certainty does not improve performance in a time reproduction task. This has two implications: either time reproduction tasks involve completely different mechanisms other than time discrimination or judgment certainty does not play a role in time reproduction tasks. It is difficult to tease apart these two based on the observations made in the current work. It was also indicated in the paper that negative errors in time reproduction might be caused due to adaptation to the short ISI or because of an urgency signal that pushes subjects towards giving their response due to anisotropy of time. Future studies might help verify these ideas, for example by varying the ISI and comparing the magnitude of under-reproduction for different values of the ISI. Another recent study showed that stimulus duration, modality and intensity affect time reproduction performance (Indraccolo, Spence, Vatakis, & Harrar, 2016). These results combined with the above study suggest that time perception employs multiple brain areas and depends on a number of factors that are otherwise generally thought to have no effect. This further stresses the need for more controlled studies to identify the neural mechanisms underlying time perception.

Source Article: Riemer M; Rhodes D; Wolbers T, 2016. Systematic Underreproduction of Time Is Independent of Judgment Certainty.  Neural Plasticity 2016:6890674

Articles cited:

Indraccolo, A., Spence, C., Vatakis, A., & Harrar, V. (2016). Combined effects of motor response, sensory modality, and stimulus intensity on temporal reproduction. Experimental Brain Research, 234(5), 1189-1198.

Riemer, M. (2015). Psychophysics and the anisotropy of time. Consciousness and Cognition, 38, 191-197.