Biology subjects

Schyns, P. G.

Publications and source records attributed to Schyns, P. G..

12 recordsLinked to original sources

bioRxiv2026

Task demands dynamically structure feature selection, routing, and integration in the human brain

AbstractFlexible visual categorization requires selecting task-relevant information from complex stimuli and transforming it through structured computations. Yet how task demands flexibly control these computations in the human brain remains unresolved. Dynamic faces provide a powerful test case, conveying stable identity through 3D shape and transient emotional expression through 4D movement. Using generative 4D face modelling, participant-specific MEG (N = 24), and information-theoretic analyses, we show that the brain implements a structured processing architecture. Task relevance first gates feature representations in occipital cortex, selectively sustaining relevant features while suppressing irrelevant ones. These features are then routed along pathway-specific channels--3D identity features to the ventral pathway and 4D emotion features to the lateral pathway. Finally, these inputs converge in temporal cortex, where identity and emotion features are synergistically integrated for learned identities. These results show how task demands dynamically structure feature selection, gating, routing, and integration, thereby constraining the computations that support flexible categorization.

neuroscience↗

bioRxiv2024

The Brain Computes Dynamic Facial Movements for Emotion Categorization Using a Third Pathway

Recent theories suggest a new brain pathway dedicated to processing social movement is involved in understanding emotions from biological motion, beyond the well-known ventral and dorsal pathways. However, how this social pathway functions as a network that computes dynamic biological motion signals for perceptual behavior is unchartered. Here, we used a generative model of important facial movements that participants (N = 10) categorized as "happy," "surprise," "fear," "anger," "disgust," "sad" while we recorded their MEG brain responses. Using new representational interaction measures (between facial features, MEGt source, and behavioral responses), we reveal per participant a functional social pathway extending from occipital cortex to superior temporal gyrus. Its MEG sources selectively represent, communicate and compose facial movements to disambiguate emotion categorization behavior, while occipital cortex swiftly filters out task-irrelevant identity-defining face shape features. Our findings reveal how social pathway selectively computes complex dynamic social signals to categorize emotions in individual participants.

neuroscience↗

bioRxiv2023

Neural representation strength of predicted category features biases decision behavior

Theories of prediction-for-perception propose that the brain predicts the information contents of upcoming stimuli to facilitate their perceptual categorization. A mechanistic understanding should therefore address where, when, and how the brain predicts the stimulus features that change behavior. However, typical approaches do not address these predicted stimulus features. Instead, multivariate classifiers are trained to contrast the bottom-up patterns of neural activity between two stimulus categories. These classifiers then quantify top-down predictions as reactivations of the category contrast. However, a category-contrast cannot quantify the features reactivated for each category-which might be from either category, or both. To study the predicted category-features, we randomly sampled features of stimuli that afford two categorical perceptions and trained multivariate classifiers to discriminate the features specific to each. In a cueing design, we show where, when and how trial-by-trial category-feature reactivation strength directly biases decision behavior, transforming our conceptual and mechanistic understanding of prediction-for-perception.

neuroscience↗

bioRxiv2022

Network predictions sharpen the representation of visual features for categorization

Models of visual cognition assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, the specific network mechanisms of this facilitation remain unclear. Here, we studied them in 11 individual participants cued to the spatial location (left vs. right) and contents (Low vs. High Spatial Frequency, LSF vs. HSF) of an upcoming Gabor stimulus that they categorized. Using concurrent MEG recordings, we reconstructed in each participant the network that communicates the predicted contents and the network that represents these contents from the stimulus for categorization. We show that predictions of LSF vs. HSF propagate top-down from temporal to contra-lateral occipital cortex, with modulatory supervision from frontal cortex. In occipital cortex, predictions sharpen bottom-up stimulus LSF vs. HSF representations, leading to faster categorizations. Our results therefore reveal functional networks that predict visual contents to sharpen their representations from the stimulus to facilitate categorization behavior.

neuroscience↗

bioRxiv2021

A common neural currency account for social and non-social decisions

To date, social and non-social decisions have been studied in isolation. Consequently, the extent to which social and non-social forms of decision uncertainty are integrated using shared neurocomputational resources remains elusive. Here, we address this question using simultaneous EEG-fMRI and a task in which decision evidence in social and non-social contexts varies along comparable scales. First, we identify comparable time-resolved build-up of activity in the EEG, akin to a process of evidence accumulation. We then use the endogenous trial-by-trial variability in the slopes of these accumulating signals to construct parametric fMRI predictors. We show that a region of the posterior-medial frontal cortex (pMFC) uniquely explains trial-wise variability in the process of evidence accumulation in both the social and non-social contexts. We further demonstrate a task-dependent coupling between the pMFC and regions of the human valuation system in dorso- and ventro-medial prefrontal cortex (dmPFC/vmPFC) across both contexts. Finally, we report domain-specific representations in regions known to encode the early decision evidence for each context. These results are suggestive of a domain-general decision-making architecture, whereupon domain-specific information is likely converted into a "common currency" in the dmPFC/vmPFC and accumulated for the decision in the pMFC.

neuroscience↗

bioRxiv2021

Top-down predictions of visual features dynamically reverse their bottom-up processing in the occipito-ventral pathway to facilitate stimulus disambiguation and behavior

The prevalent conception of vision-for-categorization suggests an interplay of two dynamic flows of information within the occipito-ventral pathway. The bottom-up flow progressively reduces the high-dimensional input into a lower-dimensional representation that is compared with memory to produce categorization behavior. The top-down flow predicts category information (i.e. features) from memory that propagates down the same hierarchy to facilitate input processing and behavior. However, the neural mechanisms that support such dynamic feature propagation up and down the visual hierarchy and how they facilitate behavior remain unclear. Here, we studied them using a prediction experiment that cued participants (N = 11) to the spatial location (left vs. right) and spatial frequency (SF, Low, LSF, vs. High, HSF) contents of an upcoming Gabor patch. Using concurrent MEG recordings of each participants neural activity, we compared the top-down flow of representation of the predicted Gabor contents (i.e. left vs. right; LSF vs. HSF) to their bottom-up flow. We show (1) that top-down prediction improves speed of categorization in all participants, (2) the top-down flow of prediction reverses the bottom-up representation of the Gabor stimuli, going from deep right fusiform gyrus sources down to occipital cortex sources contra-lateral to the expected Gabor location and (3) that predicted Gabors are better represented when the stimulus is eventually shown, leading to faster categorizations. Our results therefore trace the dynamic top-down flow of a predicted visual content that chronologically and hierarchically reversed bottom-up processing, further facilitates visual representations in early visual cortex and subsequent categorization behavior.

neuroscience↗

bioRxiv2021

Spatio-temporal dynamics of visual information representation and transformation in brain networks resolving algorithmic functions

A key challenge in systems neuroscience remains to understand where, when and now particularly how brain networks compute over sensory inputs to achieve behavior. We used XOR, OR and AND functions as behavioral tasks, because each requires a different computation over the same inputs to produce correct outputs. In each task, source-localized magnetoencephalographic activity progresses through four systems-level computations identified within individual participants (N = 10/task): (1) linear discrimination of each visual input, first contra-laterally in occipital cortex then (2) jointly in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex and finally (4) behavioral response representation in post-central gyrus. Our results show how network algorithms differently compute over the same inputs to produce different behaviors. One sentence summaryFour stages of task-specific computations over the same visual inputs achieve different behaviors in dynamic brain networks

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bioRxiv2019

Making the brain-activity-to-information leap using a novel framework: Stimulus Information Representation (SIR)

A fundamental challenge in neuroscience is to understand how the brain processes information. Neuroscientists have approached this question partly by measuring brain activity in space, time and at different levels of granularity. However, our aim is not to discover brain activity per se, but to understand the processing of information that this activity reflects. To make this brain-activity-to-information leap, we believe that we should reconsider brain imaging from the methodological foundations of psychology. With this goal in mind, we have developed a new data-driven framework, called Stimulus Information Representation (SIR), that enables us to better understand how the brain processes information from measures of brain activity and behavioral responses. In this article, we explain this approach, its strengths and limitations, and how it can be applied to understand how the brain processes information to perform behavior in a task.\n\n\"It is no good poking around in the brain without some idea of what one is looking for. That would be like trying to find a needle in a haystack without having any idea what needles look like. The theorist is the [person] who might reasonably be asked for [their] opinion about the appearance of needles.\" HC Longuet-Higgins, 1969.

neuroscience↗

bioRxiv2018

Neural Processing of the Same, Behaviourally Relevant Face Features is Delayed by 40 ms in Healthy Ageing

Fast and accurate face processing is critical for everyday social interactions, but it declines and becomes delayed with age, as measured by both neural and behavioural responses. Here, we addressed the critical challenge of understanding how ageing changes neural information processing mechanisms to delay behaviour. Young (20-36 years) and older (60-86 years) adults performed the basic social interaction task detecting a face vs. noise while we recorded their electroencephalogram (EEG). In each participant, using a new information theoretic framework we reconstructed the features supporting face detection behaviour, and also where, when and how EEG activity represents them. We found that occipital-temporal pathway activity dynamically represents the eyes of the face images for behaviour [~]170 ms post-stimulus, with a 40 ms delay in older adults that underlies their 200 ms behavioural deficit of slower reaction times. Our results therefore demonstrate how ageing can change neural information processing mechanisms that underlie behavioural slow down.\n\nAuthor summaryOlder adults are consistently slower than young adults in a variety of behavioural perceptual tasks. So far, it has been unclear if the underlying cause of the behavioural delay relates to attentional or perceptual differences in encoding visual information, or slower neural processing speed, or other neural factors. Our study addresses these questions by showing that in a basic social interaction task (discriminating faces from noise), young and older adults encoded the same visual information (eyes of the face) to perform the task. Moreover, early brain activity (within 200 ms following stimulus onset) encoded the same visual information (again, eyes of the face) in both groups, but was delayed and weaker in older adults. These early delays in information encoding were directly related to the observed behavioural slowing in older adults, showing that differences in early perceptual brain processes can contribute to the motor response.

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bioRxiv2017

Entrained audiovisual speech integration implemented by two independent computational mechanisms: Redundancy in left posterior superior temporal gyrus and Synergy in left motor cortex

Information integration is fundamental to many aspects of human behavior, and yet its neural mechanism remains to be understood. For example, during face-to-face communication we know that the brain integrates the auditory and visual inputs but we do not yet understand where and how such integration mechanisms support speech comprehension. Here we show that two independent mechanisms forge audiovisual representations for speech comprehension in different brain regions. With a novel information theoretic measure, we found that theta (3-7 Hz) oscillations in the posterior superior temporal gyrus/sulcus (pSTG/S) code speech information that is common (i.e. redundant) to the auditory and visual inputs whereas the same oscillations in left motor and inferior temporal cortex code synergistic information between the same inputs. Importantly, redundant coding in the left pSTG/S and synergistic coding in the left motor cortex predict behavior - i.e. speech comprehension performance. Our findings therefore demonstrate that processes classically described as integration effectively reflect independent mechanisms that occur in different brain regions to support audiovisual speech comprehension.

neuroscience↗

bioRxiv2017

Efficient Information Contents Flow Down from Memory to Predict the Identity of Faces

In the sciences of cognition, an influential idea is that the brain makes predictions about incoming sensory information to reduce inherent ambiguity. In the visual hierarchy, this implies that information content originating in memory-the identity of a face-propagates down to disambiguate incoming stimulus information. However, understanding this powerful prediction-for-recognition mechanism will remain elusive until we uncover the content of the information propagating down from memory. Here, we address this foundational limitation with a task ubiquitous to humans-familiar face identification. We developed a unique computer graphics platform that combines a generative model of random face identity information with the subjectivity of perception. In 14 individual participants, we reverse engineered the predicted information contents propagating down from memory to identify 4 familiar faces. In a follow-up validation, we used the predicted face information to synthesize the identity of new faces and confirmed the causal role of the predictions in face identification. We show these predictions comprise both local 3D surface patches, such as a particularly thin and pointy nose combined with a square chin and a prominent brow, or more global surface characteristics, such as a longer or broader face. Further analyses reveal that the predicted contents are efficient because they represent objective features that maximally distinguish each identity from a model norm. Our results reveal the contents that propagate down the visual hierarchy from memory, showing this coding scheme is efficient and compatible with norm-based coding, with implications for mechanistic accounts of brain and machine intelligence.

neuroscience↗

bioRxiv2016

Contributions of local speech encoding and functional connectivity to audio-visual speech integration

Seeing a speakers face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioural benefit arising from seeing the speaker's face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-motor interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments.

neuroscience↗