Category: Blog

Does Sense of Smell affect Sense of Time?

November 29, 2016

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.

Do beta oscillations predict the timing of upcoming stimuli?

November 27, 2016

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

November 24, 2016

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

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

November 1, 2016

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?

November 1, 2016

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.

picture1

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.

 

picture2               picture3

 

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.

Subsecond timing relies on dynamic excitability of local cortical circuits

October 31, 2016

We know that the passage of time at multiple time scales can influence neural activity. But what brain mechanisms are responsible for the encoding of time itself? Is there a specialised mechanism or module that measures the elapsed time and informs other brain areas or neuronal populations about the current position on the time axis? Or is time encoded in a distributed way, with – in principle – any part of the cortex involved in processing some information also intrinsically containing information about time?

In a recent article in Neuron, Goel & Buonomano argue for the latter, showing that neurons in a cortical slice in vitro – by definition isolated from any external circuit dedicated to measuring time – can be trained to represent time elapsed (at a subsecond scale) between an electric stimulus and optogenetic activation. The experiments were conducted in slices of cortical tissue implanted with electrodes used to deliver electric shocks which evoked neural activity. The slices were also transfected with a virus expressing channelrhodopsin, thanks to which cells were activated by optical stimulation (blue light shone above the tissue). First, the slices were subject to prolonged “training”, in which optical stimulation was delivered after the electric shock and a brief, constant time interval (100, 250, or 500 ms). After training, slices were “tested” – the electric shocks were delivered without being followed by the light. However, most superficial pyramidal cells in the slices spiked not only in response to shock, but also later, several hundreds of milliseconds after the initial peak. Crucially, the interval between the two waves of activity depended on previous training: the second wave of activity appeared around 100 ms if the slices had been trained to receive light 100 ms after the electric shock, and progressively later if the training had involved longer intervals.

While this finding shows that a generic part of the cortex can be trained to represent temporal regularities in its activity – without receiving inputs from another area serving as an external “clock” – these results might be explained either by single cells adapting their time constants in an experience-dependent way, or by the whole network of neurons encoding time in a dynamically evolving pattern of its activity. To disambiguate between these two possibilities, another experiment was conducted. This time the slices were implanted with two electrodes. In the training phase, one electrode was paired with an optical stimulus as before, delivered 100 ms after the shock. The other electrode was used to stimulate the cortical slice without an associated light stimulus (“unpaired” pathway). In the test phase, after a spike evoked by the electric shock, many of the neurons that had been classified as lying on either the paired or the unpaired pathway spiked again later. Crucially, this second wave of activity in neurons on the paired pathway was more pronounced and occurred markedly closer to the 100 ms mark than the secondary activation of neurons on the unpaired pathway. These results can be interpreted as pathway-sensitive learning, where the paired pathway is associated with more polysynaptic activity, and the timing of this activity – while not always coinciding with the expected interval (see below) – is closer to the expected interval.

To better understand the time-specific secondary response of the network in a more dynamic and mechanistic way, the authors performed two further experiments. First, they interleaved the training phase with test phases after 1 and 2 hours of pairing electric shocks with light. The results of this experiment showed that after 1 hour, the activity along the paired pathway increased (compared to the unpaired pathway) but not in a time-specific way. However, after 2 hours of training, the secondary peak of activity also depended on the trained interval, with the neurons trained using the 100 ms interval firing earlier than those trained at 500 ms.

Interestingly, the authors also observed that the probability of a light-evoked spike changed over the course of training. In cells which typically responded to light alone, just after pairing the light stimulus with an electric shock, the probability of a spike evoked by the light decreased dramatically to only 25%, and recovered during training to around 70%. This suggests that, during the initial phase of the training, the electric shock induces strong inhibition in the network which prevents the optical stimulus from activating the light-sensitive neurons. Later, this inhibition might decrease, leading to more neurons firing in response to light. The authors tested this hypothesis by separately measuring the excitatory and inhibitory currents evoked by the electric shock. It turned out that the ratio of excitation and inhibition differed between the paired and unpaired pathways only around the trained time interval, but not earlier or later. This result demonstrates that the network might have learned to dynamically shift the balance of excitation and inhibition in a training-dependent way.

Across the experiments, the exact timing of the secondary activity did not always coincide with the trained interval. For example, looking at slices trained at the 100 ms interval, experiment 1 – with only the paired pathway – resulted in secondary activity after approximately 100 ms, but further experiments – with both the paired and unpaired pathways – showed secondary activity peaking closer to 200 ms or even 250 ms. While the authors do not address these discrepancies, one could argue that the paired and unpaired pathway will strongly overlap in a densely interconnected slice, with neurons being indirectly depolarised by inputs from both pathways.

The time-specific balancing of excitation and inhibition suggests that, when looking at dynamic modulations of neural excitability, subtle measures of network activity might be a better choice than overall firing rates. Similar arguments have recently been raised in cognitive neuroscience, where non-invasive measurements often show dynamic changes of neural states without accompanying persistent activity.

It is important to note that the experiments were confined to a subsecond time scale. Different mechanisms might be at play at longer time scales. Crucially, temporal encoding in the order of seconds might more likely rely on cortico-hippocampal loops rather than local cortical networks.

— Ryszard Auksztulewicz, Oxford Centre for Human Brain Activity

Source article: Goel A, Buonomano D (2016) Temporal interval learning in cortical cultures is encoded in intrinsic network dynamics. Neuron 91(2):320-327. doi: http://dx.doi.org/10.1016/j.neuron.2016.05.042

Spontaneous Eye Blinks may explain moment to moment changes in time perception

October 31, 2016

Everyday we encounter so many events which alters our sense of time. For example, waiting for someone over telephone or experiencing pain seems longer in time then actual, whereas spending time with loved ones or playing video games seems shorter. Many factors like attention, arousal, emotion, etc are known to influence subjective time. But a recent study by Dr. Devin Terhune (Department of Psychology, Goldsmiths, University of London) and his collaborators [Jake Sullivan, Department of Experimental Psychology, University of Oxford & Jaana Simola, Neuroscience Center, University of Helsinki] published in Current Biology, for the first time showed that the moment to moment time perception changes can also occur due to spontaneous fluctuations in the striatal dopamine.

The study by Dr. Devin Terhune, very cleverly utilized the findings from two line of work, first are those pharmacological studies which links time perception and level of dopamine. And second are those studies which links the level of striatal dopamine and rate of spontaneous eye blinks. Based on these studies they hypothesized that, since spontaneous eye blink are indicator of rise in striatal dopamine, then the time perception for the trials after the blink should be different from those trials which were not preceded by an eye blink. So they had two conditions, post-blink trials and post-no-blink trials. They used temporal bisection task wherein participants were first trained for two standard durations (for sub-second range, short standard-300ms and long standard-700ms) before the main experiment and were suppose to judge whether they perceived the duration of the visual test stimuli (randomly 300ms, 367ms, 433ms, 500ms, 567ms, 633ms, and 700ms) as closer to short or long standard duration. While participants performed the temporal bisection task their eye moments and blinks were recorded using an eye tracker. Monitoring the eye blinks enabled them to categorize each trial based on whether the participants blinked or not in the previous trial. They found that participants perceived the duration of the post-blink trials as longer compared to post-no-blink trials, as indicated by the leftward shift of psychometric function in the post-blink condition compared to post-no-blink condition.

These results were successfully replicated even for auditory stimuli and also for supra second visual stimuli (short standard-1400ms, long standard-2600ms; duration of test stimuli-1400ms, 1600ms, 1800ms, 2000ms, 2200ms, 2400ms, and 2600ms). Moreover, in all the three experiments there was no difference in the temporal precision, indicated by no difference in Weber fraction (WF) and difference limen (DL) between the two critical conditions. WF and DL are measures of temporal sensitivity where smaller values means better sensitivity.

The key contribution of this study is that along with demonstrating the moment-to-moment intra-individual changes in time perception, it also provides a new, simple and innovative design for studying intra-individual changes in time perception associated with endogenous fluctuations in striatal dopamine. It also has implications in understanding the temporal perception in clinical disorders associated with dopamine like Parkinson’s.

Source: Terhune, D. B., Sullivan, J. G., & Simola, J. M. (2016). Time dilates after spontaneous blinking. Current Biology, 26(11), R459-R460. DOI: http://dx.doi.org/10.1016/j.cub.2016.04.010

—Mukesh Makwana, mukesh@cbcs.ac.in
Doctoral student,
Centre of Behavioural and Cognitive Sciences,
University of Allahabad, India.