Biology subjects

Quek, G.

Publications and source records attributed to Quek, G..

4 recordsLinked to original sources

bioRxiv2025

A comprehensive EEG dataset for investigating visual touch perception

Visual touch perception is fundamental to body awareness and social bonding, helping us perceive ourselves and connect with others. However, understanding its neural basis has been limited by a lack of comprehensive public datasets. To address this, we present an electroencephalography (EEG) and self-report dataset from 80 participants. It includes neural responses from 2,880 trials per participant, during which participants viewed touch interactions between two hands involving either direct skin contact or object-mediated touch. Complementing the EEG data are self-report measures on empathy, perspective-taking, vicarious touch, and mirror-touch synaesthesia, providing a robust resource for exploring individual differences in visual touch perception. Ninety unique videos were presented in four orientations, with trials evenly split between self- and other-perspectives, and showing touch to either the left or right hand. This design supports investigation into how viewing perspective and hand laterality influence touch processing. Technical validation reveals distinct neural patterns associated with visual, sensory, and emotional touch dimensions, highlighting the datasets potential to advance research into diverse aspects of visual touch perception.

neuroscience↗

bioRxiv2025

Multimodal Human Perception of Object Dimensions: Evidence from Deep Neural Networks And Large Language Models

Object recognition in the human visual system is implemented within a hierarchy characterised by increasing feature complexity. Here, we investigated whether human-derived dimensions of object knowledge show a similar progressive emergence across layers in deep neural networks (DNNs), and how this emergence is shaped by architecture, learning objective, and stimulus statistics. To test this, we predicted human-derived dimensions from layer-wise activations of multiple DNNs and transformer models trained on large-scale datasets. Results showed that trained DNNs exhibit emergence profiles resembling theoretical expectations from human vision, with behaviourally relevant object dimensions largely absent in early layers, strengthening across layers, and peaking in later layers. Architectural mechanisms such as recurrence and skip connections amplified this encoding, learning objectives redistributed information across layers, and changes in stimulus statistics confirm that hierarchical emergence is a general principle extending to material perception. These findings demonstrate that the hierarchical emergence of human-derived dimensions is a fundamental property of trained networks and highlight design and input factors that shape layer-wise representational organisation, providing hypotheses for the structure of visual representations in the brain.

neuroscience↗

bioRxiv2023

Movement trajectories as a window into the dynamics of emerging neural representations.

Transforming sensory inputs into meaningful neural representations is critical to adaptive behaviour in everyday environments. While non-invasive neuroimaging methods are the de-facto method for investigating neural representations, they remain expensive, not widely available, time-consuming, and restrictive in terms of the experimental conditions and participant populations they can be used with. Here we show that movement trajectories collected in online behavioural experiments can be used to measure the emergence and dynamics of neural representations with fine temporal resolution. By combining online computer mouse-tracking and publicly available neuroimaging (MEG and fMRI) data via Representational Similarity Analysis (RSA), we show that movement trajectories track the evolution of visual representations over time. We used a time constrained face/object categorization task on a previously published set of images containing human faces, illusory faces and objects to demonstrate that time-resolved representational structures derived from movement trajectories correlate with those derived from MEG, revealing the unfolding of category representations in comparable temporal detail (albeit delayed) to MEG. Furthermore, we show that movement-derived representational structures correlate with those derived from fMRI in most task-relevant brain areas, faces and objects selective areas in this proof of concept. Our results highlight the richness of movement trajectories and the power of the RSA framework to reveal and compare their information content, opening new avenues to better understand human perception.

neuroscience↗

bioRxiv2022

Statistical learning of distractor co-occurrences facilitates visual search

Visual search is facilitated by knowledge of the relationship between the target and the distractors, including both where the target is likely to be amongst the distractors and how it differs from the distractors. Whether the statistical structure amongst distractors themselves, unrelated to target properties, facilitates search is less well understood. Here, we assessed the benefit of distractor structure using novel shapes whose relationship to each other was learned implicitly during visual search. Participants searched for target items in arrays of shapes that comprised either four pairs of co-occurring distractor shapes (structured scenes) or eight distractor shapes randomly partitioned into four pairs on each trial (unstructured scenes). Across five online experiments (N=1140), we found that after a period of search training, participants were more efficient when searching for targets in structured than unstructured scenes. This structure-benefit emerged independently of whether the position of the shapes within each pair was fixed or variable, and despite participants having no explicit knowledge of the structured pairs they had seen. These results show that implicitly learned co-occurrence statistics between distractor shapes increases search efficiency. Increased efficiency in the rejection of regularly co-occurring distractors may contribute to the efficiency of visual search in natural scenes, where such regularities are abundant.

neuroscience↗