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We can rapidly learn recurring patterns that occur within our sensory environments. This knowledge allows us to form expectations about future sensory events. Several influential predictive coding models posit that, when a stimulus matches our expectations, the activity of feature-selective neurons in the visual cortex will be suppressed relative to when that stimulus is unexpected. However, after accounting for known critical confounds, there is currently scant evidence for these hypothesized effects from studies recording electrophysiological neural activity. To provide a strong test for expectation effects on stimulus-evoked responses in the visual cortex, we performed a probabilistic cueing experiment while recording electroencephalographic (EEG) data. Participants (n = 48) learned associations between visual cues and subsequently presented gratings. A given cue predicted the appearance of a certain grating orientation with 10%, 25%, 50%, 75%, or 90% validity. We did not observe any stimulus expectancy effects on grating-evoked event-related potentials. Multivariate classifiers trained to discriminate between grating orientations performed better when classifying 10% compared to 90% probability gratings. However, classification performance did not substantively differ across any other stimulus expectancy conditions. Our findings provide very limited evidence for modulations of prediction error signaling by probabilistic expectations as specified in contemporary predictive coding models.
Ouden et al. (Thu,) studied this question.