Motor Prediction Sharpens Early Visual Representations of Action Outcomes Independent of Prior Expectations

Poster Session F - Tuesday, April 1, 2025, 8:00 – 10:00 am EDT, Back Bay Ballroom/Republic Ballroom

EDWARD ODY¹, Tilo Kircher¹, Benjamin Straube¹, Yifei He¹; ¹University of Marburg

According to forward model theories of motor control, sensory consequences of actions are predicted based on an efference copy of the motor command. Correct predictions result in the modulation of action feedback while incorrect predictions result in prediction errors. An alternative view, based on Bayesian models, suggests that incoming sensory feedback is predicted based on the accumulation of prior evidence. Here, in an EEG study (N = 24), we examined whether motor prediction sharpens the neural representation of action outcomes independently of prior expectations. In separate blocks, participants either actively triggered Gabor patches with a button press or passively observed them. The patches had 50% probability of having left or right orientation and the order was randomised. To retain attention, participants were asked to respond to vertical catch trials (4/60 per block). We ran a time-resolved decoding analysis by training a classifier (SVM, 5-fold) at each time point to decode the orientation separately for the active and passive conditions. Both conditions showed above-chance decoding shortly after (~100 ms) the onset of the Gabor patch. However, active showed significantly higher decoding than passive, suggesting a sharper representation of the grating orientation in early visual processing. This result demonstrates that forward model motor prediction contributes to sharpening the representation of action outcomes, even when those outcomes can not be predicted based on the accumulation of prior knowledge.

Topic Area: PERCEPTION & ACTION: Motor control