Biology subjects

Peelen, M.

Publications and source records attributed to Peelen, M..

4 recordsLinked to original sources

bioRxiv2026

Dissociating representations of object shape, real-world size, and mobility in human visual cortex

Object representations in the human ventral occipitotemporal cortex (VOTC) are organized along multiple dimensions, including shape (rectilinear vs. curvilinear), real-world size (large vs. small), and mobility (stationary vs. mobile). However, these dimensions are strongly correlated in naturalistic vision, making their separate contributions to VOTC organization unclear. For example, large objects (e.g., a wardrobe, a house) are typically rectilinear and stationary, while small objects (e.g., a ball, a cup) are more curvilinear and mobile. Here, we used fMRI, together with a new stimulus set that orthogonally manipulates shape, size, and mobility, to investigate the separate influences of these dimensions on VOTC organization. Example stimuli include air balloon (large, curvilinear, mobile), radar dish (large, curvilinear, stationary), and mailbox (small, rectilinear, stationary). Contrasts revealed that large (vs. small), rectilinear (vs. curvilinear), and stationary (vs. mobile) dimensions all independently evoked strong and overlapping activity in medio-anterior VOTC. This overlapping activity was at the intersection of the parahippocampal place area (PPA) and the ventral place-memory area (VPMA). Similar results were found at the intersection of the scene-selective occipital place area and the lateral place-memory area (LPMA). Finally, large (vs. small), but not rectilinear (vs. curvilinear) or stationary (vs. mobile) activity, was found in additional posterior ventral scene-selective regions, as well as in early visual cortex. Overall, these results indicate that object shape, real-world size, and mobility dimensions all independently activate scene-selective PPA and OPA, showing joint selectivity for distinct low- and high-level object properties that are highly correlated in naturalistic vision.

neuroscience↗

bioRxiv2025

Dynamic context-based updating of object representations in visual cortex

Objects in real-world scenes are often poorly or partially visible, for example because they are occluded or appear in the periphery. An additional challenge of real-world vision is that it is dynamic, causing the appearance of objects (e.g., their size and orientation) to change as we move. Importantly, however, these changes are predictable from the 3D structure of the surrounding scene. In two fMRI studies, we find that visual cortex dynamically updates object representations using this predictive contextual information. Firstly, visual cortical representations of objects were enhanced when they rotated congruently (versus incongruently) with the surrounding scene. Secondly, the inferred orientation of the object could be decoded from visual cortex activity even when the object was fully occluded. These findings indicate that predictive processes in visual cortex follow the geometric structure of the environment, providing a mechanism to support object perception in dynamic natural vision. TeaserObjects are mentally rotated together with the changing viewpoint on a scene, affecting their representation in visual cortex.

neuroscience↗

bioRxiv2019

Population codes of prior knowledge learned through environmental regularities

How the brain makes correct inferences about its environment based on noisy and ambiguous observations, is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings and clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.

neuroscience↗

bioRxiv2017

MEG sensor patterns reflect perceptual but not categorical similarity of animate and inanimate objects

Human high-level visual cortex shows a distinction between animate and inanimate objects, as revealed by fMRI. Recent studies have shown that object animacy can similarly be decoded from MEG sensor patterns. Which object properties drive this decoding? Here, we disentangled the influence of perceptual and categorical object properties by presenting perceptually matched objects (e.g., snake and rope) that were nonetheless easily recognizable as being animate or inanimate. In a series of behavioral experiments, three aspects of perceptual dissimilarity of these objects were quantified: overall dissimilarity, outline dissimilarity, and texture dissimilarity. Neural dissimilarity of MEG sensor patterns was modeled using regression analysis, in which perceptual dissimilarity (from the behavioral experiments) and categorical dissimilarity served as predictors of neural dissimilarity. We found that perceptual dissimilarity was strongly reflected in MEG sensor patterns from 80ms after stimulus onset, with separable contributions of outline and texture dissimilarity. Surprisingly, when controlling for perceptual dissimilarity, MEG patterns did not carry information about object category (animate vs inanimate) at any time point. Nearly identical results were found in a second MEG experiment that required basic-level object recognition. These results suggest that MEG sensor patterns do not capture object animacy independently of perceptual differences between animate and inanimate objects. This is in contrast to results observed in fMRI using the same stimuli, task, and analysis approach: fMRI showed a highly reliable categorical distinction in visual cortex even when controlling for perceptual dissimilarity. Results thus point to a discrepancy in the information contained in multivariate fMRI and MEG patterns.

neuroscience↗