4.7 Review

Evaluating the evidence for expectation suppression in the visual system

Journal

NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
Volume 126, Issue -, Pages 368-381

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neubiorev.2021.04.002

Keywords

Expectation suppression; Repetition suppression; Attention; Prediction; Vision; fMRI; Electrophysiology

Funding

  1. University of Melbourne Decision Science Fellowship
  2. Deutsche Forschungsgemeinschaft [KO 3918/51]
  3. Fonds voor Wetenschappelijk Onderzoek (FWO) Vlaanderen [G088418N]
  4. KU Leuven [C14/17/109]

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Recent research has identified confounding factors that may impact the true effects of expectation suppression, casting doubt on its findings across different experimental contexts. However, evidence for genuine expectation suppression was found in specific statistical learning designs.
Reports of expectation suppression have shaped the development of influential predictive coding-based theories of visual perception. However recent work has highlighted confounding factors that may mimic or inflate expectation suppression effects. In this review, we describe four confounds that are prevalent across experiments that tested for expectation suppression: effects of surprise, attention, stimulus repetition and adaptation, and stimulus novelty. With these confounds in mind we then critically review the evidence for expectation suppression across probabilistic cueing, statistical learning, oddball, action-outcome learning and apparent motion designs. We found evidence for expectation suppression within a specific subset of statistical learning designs that involved weeks of sequence learning prior to neural activity measurement. Across other experimental contexts, whereby stimulus appearance probabilities were learned within one or two testing sessions, there was inconsistent evidence for genuine expectation suppression. We discuss how an absence of expectation suppression could inform models of predictive processing, repetition suppression and perceptual decision-making. We also provide suggestions for designing experiments that may better test for expectation suppression in future work.

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