Study of Brain Activity Underlying Predictive Mechanisms During the Perception of Visual Scenes

Study Overview
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Brief Summary
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This study aims to clarify the mechanisms by which the predictions we have about our visual environment influence the processing of expected or unexpected visual stimuli at the cerebral level.

Detailed Description
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Current models of visual perception agree that perception is a proactive process. According to these models, perception of the visual environment would allow to continuously generate expectations or predictions about the likely characteristics of a visual scene, which would facilitate their processing and visual recognition. At the neurobiological level, these models postulate that visual perception is the result of a permanent exchange between prediction signals (i.e., predicted characteristics of the visual stimulus) and prediction error signals (i.e., unprovevised characteristics of the stimulus to update predictions) between consecutive levels of the hierarchy of cortical visual areas. However, the neurophysiological correlates of these mechanisms remain debated. The results of some work suggest that the prediction signals generated by high-level cortical areas would make it possible to pre-process the predicted characteristics of a stimulus within lower-level areas, by inhibiting the activity of neurons dedicated to their processing. Conversely, other work postulates that the prediction signals generated by high-level areas would increase the sensitivity of neurons encoding expected characteristics while inhibiting the response of neurons encoding unexpected features in lower-level areas. Accordingly, brain activity in these regions would rather reflect the processing of expected features of visual stimuli. It has also been proposed that these two mechanisms coexist but that they intervene alternately during the temporal course of brain processing and depending on the quality of the visual signal. However, this hypothesis has never been systematically tested. The objective of the project is to improve fundamental knowledge about the mechanisms of visual perception by studying at the cerebral level how predictions about the visual environment influence its visual perception. Specifically, investigators will use electroencephalography (EEG) recordings from healthy volunteer participants to measure how brain activity related to visual processing of images of objects and scenes is modulated by their expected or unexpected character, taking into account the temporal course of brain processing and considering the quality of visual signals.

Official Title
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Etude de l'activité cérébrale Sous-jacente Aux mécanismes prédictifs Lors de la Perception de scènes Visuelles

Conditions
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Physiological Reactivity to Cues

Intervention / Treatment
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* Other: Visual stimulation


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Participants between 18 and 35 years old Normal or corrected-to-normal visual acuity Ability to consent or oppose to the research No opposition to the research Exclusion Criteria: Important visual impairments Neuropsychiatric pathology Use of drug or medication with neurocognitive effects Minors, or persons under psychiatric care, or protected persons

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 35 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Basic Science
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Healthy adults<br>A single group of healthy adults will undergo all experimental conditions (within subject design) | Other: Visual stimulation<br>* Participants will be displayed with photographs of scenes and objects which predictability and sharpness will be manipulated<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Performance (% of correct classification) of a support vector machine algorithm in classifying the category of objects and scenes based on electroencephalography signals evoked by the visual perception of expected and unexpected objects and scenes | We will use EEG data acquired while participants look at neutral objects and scenes of different categories to train a classifier in decoding the category of these objects and scenes. This classifier will then be tested using EEG data acquired while participants look at novel expected and unexpected objects and scenes. We will record the % accuracy of the classifier in this test phase. | Through study completion, an average of 1.5 year |