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Investigating Perceptual Thresholds for Faces and Houses Using A General Linear Model Approach

Year: 2025

Type: article

Abstract: This study investigates how perceptual thresholds for recognising faces and houses differ under varying levels of visual noise, using a General Linear Model (GLM) framework. We examine how individual electrode locations and their corresponding anatomical regions contribute to perceptual thresholds by analysing electrocorticography (ECoG) data. Channel-specific analyses reveal localised brain activity highly correlated with task performance, offering new perspectives on neural selectivity and perceptual robustness. Additionally, we compare GLM approach implementations using Python and MATLAB to evaluate differences in efficiency, accuracy, and analytical insights across computational platforms. This comparison highlights how analytical tools can influence the interpretation of neural data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15315812

Authors: Oner, B., Damirchi, R. A., Noughabi, A.M.V., Hnazaee, M. F.

Institutions: Boğaziçi University, Islamic Azad University, Science and Research Branch, University of East London, Wellcome Centre for Human Neuroimaging, University College London

Language: English

Topic: Face Recognition and Perception

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W6949685335

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