Dampened sensory representations for expected input across the ventral visual stream

This repository contains all data and code necessary to replicate the results reported in:
Richter D, Heilbron M, and de Lange FP (2022) Dampened sensory representations for expected input across the ventral visual stream. Oxford Open Neuroscience.

Corresponding author:
David Richter (david.richter.work [at] gmail.com)

Abstract:
Expectations, derived from previous experience, can help in making perception faster, more reliable and informative. A key neural signature of perceptual expectations is expectation suppression, an attenuated neural response to expected compared to unexpected stimuli. While expectation suppression has been reported using a variety of paradigms and recording methods, it remains unclear what neural modulation underlies this response attenuation. Sharpening models propose that neural populations tuned away from an expected stimulus are particularly suppressed by expectations, thereby resulting in an attenuated, but sharper population response. In contrast, dampening models suggest that neural populations tuned towards the expected stimulus are most suppressed, thus resulting in a dampened, less redundant population response. Empirical support is divided, with some studies favoring sharpening, while others support dampening. A key limitation of previous neuroimaging studies is the ability to draw inferences about neural-level modulations based on population (e.g., voxel) level signals, which integrate over millions of neurons (e.g., the BOLD response). Indeed, recent simulations of repetition suppression showed that opposite neural modulations can lead to comparable population-level modulations. Forward models provide one possible solution to this inference limitation. Here we used forward models to implement both sharpening and dampening models, mapping individual neural modulations to voxel-level data. By comparing simulated neural responses to a combined analysis of two previously published fMRI studies, we show that a model that incorporates feature-specific gain modulation, suppressing neurons tuned towards the expected stimulus, best explain the empirical fMRI data. Thus, our results are in line with the dampening account of expectation suppression, suggesting that expectations may reduce redundancy in sensory cortex, and thereby promote updating of internal models on the basis of surprising information.

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