Academic Journal
Statistically efficient neural encoding of natural object variability shapes the temporal dynamics of visual processing.
| Title: | Statistically efficient neural encoding of natural object variability shapes the temporal dynamics of visual processing. |
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| Authors: | Watson DM; Department of Psychology, University of York, York, UK; York Neuroimaging Centre, University of York, York, UK. Electronic address: david.watson@york.ac.uk., Aveyard R; York Neuroimaging Centre, University of York, York, UK., Andrews TJ; Department of Psychology, University of York, York, UK; York Neuroimaging Centre, University of York, York, UK. |
| Source: | NeuroImage [Neuroimage] 2026 Aug 01; Vol. 336, pp. 122012. Date of Electronic Publication: 2026 May 20. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Academic Press Country of Publication: United States NLM ID: 9215515 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9572 (Electronic) Linking ISSN: 10538119 NLM ISO Abbreviation: Neuroimage Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Orlando, FL : Academic Press, c1992- |
| MeSH Terms: | Magnetoencephalography*/methods , Electroencephalography*/methods , Pattern Recognition, Visual*/physiology , Visual Perception*/physiology , Visual Cortex*/physiology , Brain*/physiology, Brain Mapping/methods ; Humans ; Male ; Female ; Adult |
| Abstract: | Object perception unfolds dynamically over millisecond timescales, yet the organisational principles that shape the emerging neural responses are not fully understood. Traditional hypothesis-driven approaches risk constraining interpretations by focusing on pre-selected object features. To circumvent this limitation, we applied a data-driven framework to behavioural and neuroimaging data obtained from the THINGS initiative, which provides a systematic sampling of real-world objects. Behaviourally relevant stimulus dimensions were derived from prior large-scale similarity judgements, offering an unbiased, ecologically grounded representation of object space. Using Partial Least Squares Regression (PLSR), we generated neural encoding models to predict time-resolved evoked responses in EEG and MEG from these dimensions. Across both modalities, the PLSR identified a small set of latent components that reliably captured the temporal dynamics of the neural activity. These components were similar across the EEG and MEG datasets and with a prior MRI analysis. The components encoded a diverse range of object features, including visual and semantic properties, yet did not map straightforwardly onto canonical accounts of visual cortical organisation. Instead, our findings suggest that object representations in the brain are structured by principles of statistical efficiency, capturing the co-occurrence of features amongst natural variability in real-world objects to support dynamic visual processing. (Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Given his role as an Editorial Intern at Neuroimage, David Watson had no involvement in the peer review of this article and had no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to another journal editor. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Data-driven; EEG; MEG; Neural encoding; Object perception; Visual perception |
| Entry Date(s): | Date Created: 20260521 Date Completed: 20260610 Latest Revision: 20260610 |
| Update Code: | 20260611 |
| DOI: | 10.1016/j.neuroimage.2026.122012 |
| PMID: | 42167705 |
| Database: | MEDLINE |
| ISSN: | 1095-9572 |
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| DOI: | 10.1016/j.neuroimage.2026.122012 |