Temporal confounds emulate multivariate fMRI measures of perceptual learning
- 1Institute of Ophthalmology, University College London, London, United Kingdom
- 2Experimental Psychology, Division of Psychology and Language Sciences, University College London, London, United Kingdom
- 3Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
Abstract
Any study of learning must capture neural or behavioural changes over time. Stimuli are therefore presented more than once, before and after learning takes place—a sequence that cannot be randomised across time. This design creates vulnerability to temporal confounds that cause illusory neural effects. We investigate this issue in a classic ‘one-shot’ learning paradigm, in which ambiguous two-tone images become recognisable after viewing a photo cue. Across common fMRI designs, we found that spurious correlations driven by the temporal proximity of stimuli created strong artefacts in multivariate data. Among certain experimental designs, we found strong neural markers of perceptual learning depending on how stimuli were distributed across these artefacts. This paradigm-dependent effect was present not only in the primary visual cortex (V1), but also in a non-visual control region (S1), indicative of an artefactual origin rather than a genuine perceptual learning effect. We present a principled approach to correct for this temporal order confound. Once applied, the artefactual learning signature was eliminated from both V1 and S1, and while the corrected signal retained information about image identity in V1, this was absent in S1. Our work demonstrates how current multivariate neuroimaging approaches to understanding learning could be influenced by methodological artefacts that misdirect interpretations towards distributed neural processing, and offers a practical framework to distinguish true learning from measurement error.
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BibTeX
@article{milne2026temporal,
title = {{Temporal confounds emulate multivariate fMRI measures of perceptual learning}},
author = {Georgia A. Milne and Kim Staeubli and Hugo T. Chow-Wing-Bom and John A. Greenwood and Peter Kok and Roni O. Maimon-Mor and Tessa M. Dekker},
journal = {Imaging Neuroscience},
year = {2026},
month = nov,
doi = {10.1162/imag.a.1435},
url = {https://doi.org/10.1162/imag.a.1435},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Temporal confounds emulate multivariate fMRI measures of perceptual learning
AU - Georgia A. Milne
AU - Kim Staeubli
AU - Hugo T. Chow-Wing-Bom
AU - John A. Greenwood
AU - Peter Kok
AU - Roni O. Maimon-Mor
AU - Tessa M. Dekker
PY - 2026
DA - 2026/11/04
JO - Imaging Neuroscience
DO - 10.1162/imag.a.1435
UR - https://doi.org/10.1162/imag.a.1435
AB - Any study of learning must capture neural or behavioural changes over time. Stimuli are therefore presented more than once, before and after learning takes place—a sequence that cannot be randomised across time. This design creates vulnerability to temporal confounds that cause illusory neural effects. We investigate this issue in a classic ‘one-shot’ learning paradigm, in which ambiguous two-tone images become recognisable after viewing a photo cue. Across common fMRI designs, we found that spurious correlations driven by the temporal proximity of stimuli created strong artefacts in multivariate data. Among certain experimental designs, we found strong neural markers of perceptual learning depending on how stimuli were distributed across these artefacts. This paradigm-dependent effect was present not only in the primary visual cortex (V1), but also in a non-visual control region (S1), indicative of an artefactual origin rather than a genuine perceptual learning effect. We present a principled approach to correct for this temporal order confound. Once applied, the artefactual learning signature was eliminated from both V1 and S1, and while the corrected signal retained information about image identity in V1, this was absent in S1. Our work demonstrates how current multivariate neuroimaging approaches to understanding learning could be influenced by methodological artefacts that misdirect interpretations towards distributed neural processing, and offers a practical framework to distinguish true learning from measurement error.
SN - 2837-6056
ER -