Biology subjects

Vercillo, T.

Publications and source records attributed to Vercillo, T..

3 recordsLinked to original sources

bioRxiv2026

Scene Structure Predicts Perceptual Decisions in Naturalistic Detection Tasks

The human visual system can identify objects in complex natural scenes, yet the mechanisms supporting robust perception under such variable conditions remain incompletely understood. Here, we investigate how the statistical structure of natural scenes shapes perceptual evidence formation and determines whether near-threshold stimuli are perceived correctly or incorrectly. We combine controlled psychophysics, outdoor augmented reality (AR), deep neural networks (DNNs), image-feature analysis, and EEG to examine how background context modulates perceptual decisions. Across multiple detection tasks, human performance was systematically influenced by probe-free background structure. DNNs trained on background images alone predicted correct and incorrect behavioral outcomes, with stronger effects in postcue conditions, suggesting that global scene context contributes to local perceptual decisions when spatial uncertainty is higher. AR experiments further showed that these context-driven effects persist in naturalistic viewing environments. To identify the visual information underlying these effects, we analyzed low-level image statistics. Texture entropy and edge density emerged as informative features, and conventional machine-learning models trained on these measures achieved meaningful correct/incorrect classification. Finally, EEG analyses revealed neural signatures of image-driven perceptual variability: activity during the probe-free preimage window distinguished later correct from incorrect trials, and combining EEG with image-derived features improved decoding performance. Together, these findings show that perception in natural scenes is not determined solely by the target, but is shaped by the statistical structure of the surrounding context. By linking psychophysics, AR, DNN modeling, image statistics, and EEG, this work provides a unified framework for understanding how environmental structure and neural dynamics jointly support perceptual decision-making.

neuroscience↗

bioRxiv2026

Watching Yourself Talk: Motor Experience Sharpens Sensitivity to Gesture-Speech Asynchrony

Language is inherently multisensory, with speech often accompanied by iconic gestures that convey semantic meaning related to actions, objects, or spatial relationships. Although the temporal coordination between speech and gesture is variable, the brain integrates these signals seamlessly. Yet, much of the cognitive mechanisms behind this integration remain unclear. This study investigates whether sensorimotor experience, specifically with ones own speech and gestures, enhances temporal sensitivity through internal forward models that guide audiovisual prediction. Participants first produced sentences with corresponding iconic gestures, which were audiovisually recorded. These recordings were later temporally manipulated and presented in a simultaneity judgment task, where participants evaluated both their own and others recordings. Results revealed narrower temporal binding windows (TBWs), indicating heightened sensitivity to audiovisual asynchrony, when participants judged their own speech-gesture recordings compared to those of others. To further explore the role of motor experience, we analysed individual variability in gesture-speech timing during production and found no reliable relationship between production variability and perceptual sensitivity, suggesting that perceptual precision is not simply a reflection of motor consistency. These findings demonstrate that sensorimotor experience with self-generated movements sharpens multisensory temporal integration, likely via predictive internal models, and underscore the functional role of predictive motor mechanisms in supporting temporal integration across perceptual and action systems.

neuroscience↗

bioRxiv2021

Learning a new class of multisensory associations: High-density electrophysiological mapping of the temporal course of audio-visual object processing.

Multisensory objects that are frequently encountered in the natural environment lead to strong associations across a distributed sensory cortical network, with the end result experience of a unitary percept. Remarkably little is known, however, about the cortical processes sub-serving multisensory object formation and recognition. To advance our understanding in this important domain, the present study investigated the brain processes involved in learning and identification of novel visual-auditory objects. Specifically, we introduce and test a rudimentary three-stage model of multisensory object-formation and processing. Thirty adults were remotely trained for a week to recognize a novel class of multisensory objects (3D shapes paired to complex sounds), and high-density event related potentials (ERPs) were recorded to the corresponding unisensory (shapes or sounds only) and multisensory (shapes and sounds) stimuli, before and after intensive training. We identified three major stages of multisensory processing: 1) an early, multisensory, automatic effect (<100 ms) in occipital areas, related to the detection of simultaneous audiovisual signals and not related to multisensory learning 2) an intermediate object-processing stage (100-200 ms) in occipital and parietal areas, sensitive to the learned multisensory associations and 3) a late multisensory processing stage (>250 ms) that appears to be involved in both object recognition and possibly memory consolidation. Results from this study provide support for multiple stages of multisensory object learning and recognition that are subserved by an extended network of cortical areas.

neuroscience↗