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A comparison of sharpening and dampening accounts of the role of expectation in shaping the neural fidelity of early visual representations

Created on 07 Jul 2026

Authors

Rideaux, R., Hu, Z., Chidley, K., Cloos, M., Schwarzkopf, D. S., Mattingley, J. B.

Abstract

The natural environment is spatiotemporally structured, and the brain exploits this regularity to predict and prepare for upcoming sensory stimuli. Such predictive processing is thought to increase neural efficiency by reducing metabolic expenditure and altering the fidelity with which newly encountered stimuli are encoded. Competing theoretical frameworks propose this is achieved either through sharpening, whereby expected events are encoded more precisely, or dampening, whereby expected events are suppressed and encoded less precisely. Despite clear, opposing predictions, evidence in humans for each account remains mixed due to methodological and analytical inconsistencies. Here we addressed these issues using probabilistic visual paradigm combined with functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We used population receptive field (pRF) mapping of fMRI data and inverted encoding of EEG data to compare the fidelity and timecourse of activity in visual areas in response to expected, unexpected, and random stimuli. Both methods produced a consistent pattern of results. Post hoc analysis of EEG data revealed that the apparent effect of expectancy was better explained by local spatiotemporal stimulus properties than the global expectancy manipulation. Although this pattern resembled sensory adaptation, it was more consistent with an expectation of temporal stability combined with dampening, in which both the aggregate response to expected features and their representational fidelity are suppressed. Taken together, our findings suggest that predictive processing may operate through dampening, with ecological advantages for high-fidelity encoding of unexpected sensory events.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 07 Jul 2026.

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