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Research & Publications

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Temporal

cognition

How can the brain perceive our environment in time, when perception takes time?

Time is a curious object, which can be both in the content of our thoughts (“do I have time to stop by the bakery before my bus arrives?”, and a dimension structuring them: every thought is the result of time-consuming mental processes. Perceiving the world around us, making sense of what we see or hear takes time.

Every day, we estimate durations, perform series of movements in a certain order, project ourselves into the future or remember the past. In short, we experience time over and over again and it feels very natural. Knowing whether an event happens after of before another seems for instance trivial. Yet, things can get tricky when the two events are very close to each other, for instance if there are a few hundreds of milliseconds between the two. When the chronology is ambiguous, different individuals will perceive it differently.

 

How do we perceive the chronology of events? How does my subjective experience of time differ from someone else's?

The neurons fires to transmit information, which create electrical and magnetic fields that can be measured with electroencephalography (EEG) and magnetoencephalography (MEG). Periodicities can be observed in this neuronal activity. The alpha oscillation is for instance one of the most ubiquitous brain rhythm occurring around 10Hz. Alpha rhythm is associated with performance in perceptual and attentional tasks, in working-memory and may play a role in inhibiting irrelevant sensory processing. It may modulate other frequency bands activity and may act as a temporal window of processing.

What role(s) does alpha rhythm play in perception?

Understanding the role of alpha rhythm will ultimately require an understanding of the generation of these oscillations at the neural level. The measure we have with MEG-EEG is very indirect: the measured signal captures a mixture of overlapping populations of ~100 000 neurons with a poor spatial resolution.

 

One solution to this problem is to use computational modeling.

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Brain

rhythms

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Perceptual biases

The brain is a biased organ: we all live in a world distorted by our own prism of assumptions and expectations. These biases come from models of the world that we use to process information faster and more efficiently. Most of the time, there are helpful, but they can sometimes elicit illusions and misconceptions, that neuroscientists can use to trick the brain and understand the mechanisms underlying perception.

What neural mechanisms correlate with idiosyncratic biases?

Biases can come from context-based expectations (e.g people are more likely to taste strawberries if they eat red yogurt than green yogurt) and can therefore be temporary and flexible. On the other hand, some biases are more resistant and may have a structural origin.

How does those context-based and intrinsic biases interact?

publications

Publications

7

Merholz G., Grabot L., VanRullen R., Dugué L. (2022) Periodic attention operates faster during more complex visual search. Scientific Reports. 12, 6688. https://doi.org/10.1038/s41598-022-10647-5

6

Grabot L., Kayser C., van Wassenhove V. (2021) Postdiction: when temporal regularity drives space perception through pre-stimulus alpha oscillations. eNeuro 11 August 2021, ENEURO.0030-21.2021; DOI: 10.1523/ENEURO.0030-21.2021

5

Grabot L., Kayser C., (2020) Alpha activity reflects the magnitude of an individual bias in human perception. Journal of Neuroscience. DOI: 10.1523/JNEUROSCI.2359-19.2020

4

Grabot L.*, Kononowitcz T. W.*, Dupré la Tour T., Gramfort A., Doyère V., van Wassenhove V., (2019) The strength of alpha-beta oscillatory coupling predicts motor timing precision. Journal of Neuroscience (2473-18) DOI: 10.1523/JNEUROSCI.2473-18.2018

3

Grabot L. & van Wassenhove V., (2017) Time order as psychological bias. Psychological Science. 28(5):670-678, DOI: 10.1177/0956797616689369

2

Dupré la Tour T., Talot L., Grabot L., Doyère V., van Wassenhove V., Grenier Y., Gramfort A., (2017) Non-linear auto-regressive models for cross-frequency coupling in neural time series. PLOS Computational Biology. 13(12): e1005893

1

Grabot L., Kösem A., Azizi L. & van Wassenhove V., (2017) Prestimulus alpha oscillations and the temporal sequencing of audiovisual events. Journal of Cognitive Neuroscience. 29(9):1566-1582, DOI: 10.1162/jocn_a_01145

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