Month: January 2017

The heart has held a central place in theories of human function for centuries. It was proclaimed as the seat of life in Ancient Greek and Byzantine medical literature, and the Ancient Egyptians embalmed their dead by carefully preserving the heart while unceremoniously discarding the brain¹. This stands as a stark inversion of today’s physiological priorities. However, researchers have recently again been investigating human cognition and behaviour from the perspective of cardiac physiology.

My colleagues and I recently published a paper showing that temporal reproduction is associated with heart rate. In this post I will briefly discuss this paper, and review the literature on why heart rate — and autonomic nervous system function in general — is deeply connected to cognition.

Cardiac physiology

The idea that periodic physiological signals might constitute a biological clock is not new². The most popular, recent account of this idea, endorsed by Marc Wittmann and Bud Craig, suggests that time perception is a function of integrated interoceptive signals³⁴, which explains why physiological factors such as arousal⁵ and body temperature⁶ can affect time perception.

However, a simple linear relationship between heart rate and time perception is almost definitely an oversimplification. For instance, it’s clear that increasing heart rate (by exercise for instance) doesn’t affect time perception significantly⁷. But there are other elements of cardiac signals that provide a wider overview of the autonomic nervous system. This arises because the heart is innervated by both the sympathetic (SNS) and parasympathetic nervous system (PNS), and each of these arms have slightly different latencies (the PNS acts more rapidly on the heart). Thus, different components of the variability of heart rate can be attributed to the SNS and PNS. This means, for instance, that if analysed in the time-frequency domain, power in the high-frequency band of heart rate variability reflects the functioning of the PNS⁸.

Over the last decade or so, researchers have used these types of analysis techniques to link cardiac signals with different types of cognitive processes. This includes emotion regulation⁹, self-control¹⁰, and working memory¹¹. Further to this, directly stimulating the ANS via the vagus nerve can also enhance memory processes¹².

Temporal reproduction and heart rate variability

In our study, we assessed whether performance in a temporal reproduction task was associated with these different measures of cardiac activity. Healthy participants completed a standard temporal reproduction task with durations spanning from 2 – 15 seconds, while we recorded electrocardiogram (ECG). We characterised participants temporal reproductions with a two parameter psychophysical function (Steven’s power law), where the exponent parameter indicates the concavity of the function and thus whether participants tend to under-reproduce (exponent > 1), or over-reproduce (exponent < 1) intervals.

We found that that different frequency components of heart rate variability were associated with differences in participants’ reproductions. Specifically, we found that the high-frequency component was negatively associated with the exponent parameter of the psychophysical function. This suggests that individuals with higher PNS cardiac influence under-reproduced longer intervals compared with those with lower PNS cardiac influence. We also found a similar negative association between the low-frequency component and the exponent. The interpretation of the low-frequency component of heart rate variability is not as clear as that of the high-frequency component, as it reflects physiological processes other than purely SNS function¹³. However, increases in the low-frequency component have been previously been associated with decreases in attention and fatigue due to time-on-task¹⁴, thus it is possible that individuals who experienced more task-related fatigue performed more poorly on the reproduction task. Overall, these results show that time perception is associated with autonomic nervous system function.

Existing literature

This is not the first time that researchers have found links between time perception and heart rate. For instance, one study found that individuals have better absolute accuracy at reproducing durations when their heart rate slows down during the encoding of the sample interval¹⁵. Individuals with higher resting heart rate variability are also more accurate at reproducing durations¹⁶. Similarly, individuals with overall higher heart rate variability are also more accurate in a temporal bisection task¹⁷. As the PNS is responsible for slowing down heart rate, and is also the principle determinant of heart rate variability, this would suggest that individuals with higher PNS function are more accurate at duration reproduction in general.

However, other studies that have directly stimulated the vagus nerve (the main nerve of the PNS), have found that this can cause overestimations (under-reproductions) of time in the ranges of 34 – 230 seconds¹⁸. This is approximately consistent with our results showing that individuals with higher PNS function under-reproduced durations, however it conflicts somewhat with the results of the above studies. One possible discrepancy is that the above studies used absolute measures of accuracy, whereas we observed a directional effect. Whatever the reason for these differences, the relationship between autonomic nervous system function and temporal reproduction clearly merits further investigation.

In sum, this research seems to suggest that while the beats of the heart themselves are not analogous to a pacemaker, changes in the autonomic nervous system are either a corollary of a timing mechanism, or the autonomic nervous system directly plays role in how humans perceive time. Given the non-invasive nature of heart rate recordings, future research may be able to clarify exactly how signals from the heart inform our perception of time.

Source paper:

Fung, B. J., Crone, D. L., Bode, S., & Murawski, C. (2017). Cardiac Signals Are Independently Associated with Temporal Discounting and Time Perception. Frontiers in Behavioral Neuroscience, 11, 369. http://doi.org/10.3389/fnbeh.2017.00001

More reading on how cardiac signals relate to cognition:

Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216. http://doi.org/10.1016/S0165-0327(00)00338-4

Thayer, J. F., & Lane, R. D. (2009). Claude Bernard and the heart–brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews, 33(2), 81–88. http://doi.org/10.1016/j.neubiorev.2008.08.004

Attending to specific moments in time improves the quality of sensory information presented at these moments. Behavioural and neural benefits of temporal orienting have been shown in several contexts, including rhythmic regularities of the presented stimuli, cues informing about the relevance of specific time windows, and evolving probabilities of events occurring over time. However, it is not clear to what extent the effects and mechanisms of temporal attention are shared with other domains of attentional selection. For example, spatial attention not only improves the processing of stimuli presented at the attended location, but also has detrimental effects on processing stimuli presented elsewhere, as compared to neutral baseline conditions. This is exactly the focus of a recent paper by Denison, Heeger and Carrasco, who investigate whether similar perceptual trade-offs characterise temporal attention.

In a series of behavioural experiments, the authors present cues that inform participants of the latency of targets about which they will be most likely prompted for a response. Specifically, participants are asked to discriminate the visual orientation of gratings presented at various latencies after the auditory cue. In the simplest scenario (Experiment 1), the cue informs (with 75% validity) whether the orientation of the first (1000 ms after the cue) or the second target (250 ms later) will need to be reported. Crucially, 20% of the trials were left neutral and contained an uninformative cue, allowing for a baseline comparison. An analysis of accuracy and reaction times showed that when participants believe they will be asked about the first target, they are better and faster at discriminating its orientation than when they can’t predict which target will be relevant, and their performance is weakest if they are invalidly cued to the second target. This result suggests that participants attending a later time window will perform worse when asked about stimuli presented before this time window. But is this effect symmetric – i.e., is task performance worse for targets presented late if participants attend an earlier time window? While the behavioural effects show a pattern consistent with such an effect, the pairwise comparisons of the three experimental conditions (valid, neutral, and invalid cues) are not significant. This is not entirely surprising, given that at the moment of report the late targets might be accessed more easily since they are more recent and have not been interrupted by other irrelevant targets.

Thus, in a second experiment, the same task was administered but this time with three consecutively presented targets. However, the results were only partly consistent with the simpler version of the experiment. For example, attentional costs for targets presented before an attended time window were marginally significant or not observed. Interestingly, while attentional benefits of cueing were not observed for the intermediate latency, targets presented at this latency showed robust costs when participants attended an earlier time window, suggesting that in some contexts temporal attention might disrupt processing of targets presented after the attended time window, perhaps similar to an attentional blink.

To address the question whether temporal attention actually improves the quality of visual representations (as previously shown for rhythmic orienting) or can be explained by other factors such as missing the unattended targets (as in attentional blink) or mistakes in which targets are reported, another version of the task was run. This time, again using two targets, participants had to reproduce the orientation of targets instead of only reporting whether they were tilted clockwise or counter-clockwise. The results of these experiment suggested that attention primarily affected the precision of sensory representation but not the rates of guesses (expected if unattended targets were completely missed) or target swaps. However, as in the first experiment, these effects were largely confined to targets presented early, with no evidence of benefits or costs at longer latencies.

Taken together, while these studies provide further evidence for behavioural benefits of temporal attention and for the first time directly address attentional costs in the unattended time windows, the results aren’t always symmetric or consistent across experiments. Some of these differences can be explained by task specifics; however, it would be interesting to see whether similar effects can be identified in a more continuous version of the task, where the influence of each consecutively presented stimulus – attended or not – on the final orientation report could be quantified using established modelling techniques. Finally, the paper by Denison et al., does provoke further questions about the possible neural implementation of the observed perceptual trade-offs – where any differences that might be observed between the neural mechanisms of temporal and spatial attention will likely transform our understanding on attentional selection.

– Ryszard Auksztulewicz, Oxford Centre for Human Brain Activity

Source article: Denison RN, Heeger DJ, Carrasco M (2017) Attention flexibly trades off across points in time. Psychon Bull Rev, Jan 4. doi: 10.3758/s13423-016-1216-1.

Listening to music typically induces a strong sense of an underlying beat. Although clearly related to periodicities contained in the stimulus, beat perception is internally generated since beat induction can occur in instances where no stimulation is present (i.e., in syncopated rhythms). Thus the capacity to extract a beat from periodic input provides an interesting phenomenon for examining the neural processes that temporally organise and ulitmately structure our perception of the world.

The neural mechanisms associated with beat-induction can be measured with a frequency tagging technique applied to EEG data recorded while participants listen to music. This approach involves examining the peaks in the frequency spectrum of EEG data that correspond to periodicities contained in the stimulus. By examining the activity evoked by syncopated rhythms (where the beat percept does not correspond to the sensory input), previous studies have shown that this technique indexes both periodic activity associated with the sensory input, and endogenous processes involved in extracting temporal structures from periodic input.

To further demonstrate the functional significance of this approach, Nozaradan, Peretz and Keller examined how neural entrainment measured with frequency tagging is correlated with individual differences in rhythmic motor control. The authors presented both unsyncopated and syncopated auditory rhythms whilst brain activity was recorded with EEG. Individual differences in ability to detect the beat in these rhythms were assessed offline with finger tapping tasks. In addition participants also completed another finger tapping task designed to assess temporal prediction. This stimulus comprised an aperiodic, predictable sequence of tones whereby the tempo continually varied between 400-600 ms (with a sinusoidal contour). This sequence was used to assess participants’ ability to anticipate the upcoming stimulus interval by quantifying the lag-1 and lag-0 correlation between the inter-stimulus interval and the inter-tap interval. Accurate prediction of the stimulus sequence would result in a larger lag-0 correlation than the lag-1 correlation, whereas tracking behaviour would be observed as a larger lag-1 correlation than lag-0 correlation.

The results showed that periodic stimulation produced peaks in the frequency spectrum of the recorded EEG data that corresponded to beat and non-beat frequencies. Importantly however, behavioural performance correlated selectively with the strength of entrainment in the beat frequencies. Tapping accuracy (mean asynchrony in the beat perception tasks) and the temporal prediction index (from the tempo change task) correlated positively with the height of the peaks for the beat induced frequencies, whereas the degree of entrainment in non-beat frequencies was negatively correlated with periodic tapping accuracy and was uncorrelated with prediction in tempo changing sequences. Together, these results highlight the functional significance of processes indexed by the frequency tagging approach, and show that beat perception is related to selective entrainment of neural activity to beat related frequencies.

The authors argue that the relationship between neural entrainment and temporal prediction is consistent with predictive coding models, whereby the brain optimises behaviour by forming internal models of the causes of sensory events. These internal models act as templates based on past experience that optimise sensory processing by providing predictions about the timing of upcoming sensory input. This argument is supported by another study published in 2016, which showed that temporal predictions associated with both periodic and aperiodic sequences lower sensory thresholds in a pitch discrimination task. Intriguingly, this study showed that isochronous stimulation also produced faster response times, whereas aperiodic stimulation did not. The authors of this paper argued that this dissociation may reflect the lowering of motor thresholds caused by simple sensory-motor coupling in the isochronous context, whereas changes in sensory processing may have been due to controlled processes that update internal models (i.e., linked to period correction). It remains to be seen whether the frequency tagging approach can be used to further dissociate the exogenous and endogenous processes involved in temporal prediction.

Bronson Harry

The MARCS Institute, Western Sydney University

Imagine you have a device which can help you control your perception of time, so that you can speed up or slow down the subjective time at your will. Sounds like a science fiction but not in the near future. Yes a step has been taken to control time perception in mice using optogenetics. A recent study by Sofia Soares, Bassam Atallah, and Joseph Paton published in Science, not only measured the activity of the Dopaminergic (DA) neurons in substantia nigra pars compacta (SNc) in mice but also manipulated the activity of these neurons using optogentics resulting in altered timing behaviour.

They first trained mice to categorize variable interval between two tones (0.6, 1.05, 1.26, 1.38, 1.62, 1.74, 1.95 and 2.4 sec) as shorter or longer than 1.5 sec by touching the right or left port with their nose. The correct response was rewarded. After around 2 months of training mice were very accurate in performing this task.

Next, to confirm that the activity in SNc-DA neurons is essential for accurate timing, they used pharmacogenetic suppression approach. In this, they injected the SNc of a set of mice with the viral vector carrying the genes (hM4D(Gi)-mCherry). When hM4Di is expressed it does not affect the normal functioning of the neurons, but when an inert molecule like clozapine-N-oxide (CNO) is introduced it activates the hM4Di which then results in silencing of those neurons. They found that the timing behavior become very poor in the set of mice treated with CNO compared to the control set which were treated with saline. They also checked for timing accuracy before and after the CNO treatment showing that mice regained its timing accuracy.

Further, to establish a strong molecular basis for trial-by-trial variation in timing, they used the fiber photometry approach. In this, they measured the activity in the DA neurons while mice were performing the timing task. To accomplish this they injected SNc of a set of mice with the viral vector carrying the genes (GCaMP6f and tdTomato). When neurons are active there is lot of intake of Ca2+, which then binds with GCaMP6f and tdTomato protein leading to conformation changes and fluorescence. The amount of fluorescence is the indicator of activity of the cells. To assess whether trial-by-trial change in the activity within these neurons correlates with the timing behavior of mice, they distributed all the trials based on the measured activity of DA neurons into three categories i.e. low activity, medium activity, and high activity. They found that when the activity in the dopamine neurons was high, mice more often reported shorter response, whereas when the activity was low, mice more often reported longer response. Thus, the activity in the DA neurons of SNc could predict the mice behavioural response.

Finally, to establish a causal link between the activity of the DA neurons in SNc and timing behavior, they used optogenetic approach. They injected neurons in SNc with viral vectors carrying genes (ChR2-EYFP, and ArchT-GFP, or eNpHR3.0.EYFP). ChR2 codes for photo sensitive channel rhodopsin which when stimulated with 473nm (blue light) leads to influx of positive ions (cations) inside the cells thus activating the cell. On the other hand, eNpHR3 codes for halorhodopsin which when stimulated with 596nm (yellow light) leads to influx of chloride ions thus silencing the neurons by hyperpolarization. They found that when these neurons were optogentically activated using blue light, mice responded shorter response more often. Whereas when these neurons were optogentically silenced using yellow light, mice responded longer response more often. These changes in timing behavior were transient as mice timing accuracy was again restored in the absence of light. Thus, the authors were able to manipulate the timing behaviour of the mice using optogenetics.

The study explains the underestimation of time during fun or pleasurable activity where dopamine is supposedly high and overestimation of time during stress and fear where dopamine is low. Although, this study suggests that increased activity in the DA neurons leads to underestimation of time, however there are studies which suggest that increase in dopamine leads to overestimation of time (1,2,3).

Overall, this study happens to be the first to manipulate timing behaviour in mice using optogenetics, giving hope for the future where individuals could one day manipulate their own subjective time.

Source article: Soares, S., Atallah, B. V., & Paton, J. J. (2016). Midbrain dopamine neurons control judgment of time. Science, 354(6317), 1273-1277.

References:

1. Buhusi, C. V., & Meck, W. H. (2002). Differential effects of methamphetamine and haloperidol on the control of an internal clock. Behavioral neuroscience, 116(2), 291.
2. Failing, M., & Theeuwes, J. (2016). Reward alters the perception of time. Cognition, 148, 19-26.
3. Terhune, D. B., Sullivan, J. G., & Simola, J. M. (2016). Time dilates after spontaneous blinking. Current Biology, 26(11), R459-R460.

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