Ingmar de Vries

@ingmardevries

Cognitive neuroscientist. I use M/EEG to study how our brain predicts and attends to the world around us. Based at CIMeC, University of Trento (IT). Guest at Donders Institute, Radboud University (NL).

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Ingmar de Vries@ingmardevries· Jun 30

Disrupting holistic priors by turning videos upside down selectively reduced high-level view-invariant motion predictions. Instead, disrupting kinematics priors by temporal scrambling eliminated all motion prediction, with neural activity merely reacting to, rather than predicting, visual input.

Main dynamic RSA results. Region of interest (ROI)-based analysis, with dRSA regression weights illustrated as latency plots. The three viewing conditions are displayed in rows with normal on top (A), inverted in the middle (B), and temporally scrambled at the bottom (C). Different stimulus models are displayed in columns with pixelwise motion reflecting optical flow vector direction in each pixel, and body motion reflecting 3D motion of 13 kinematic markers placed on the dancer. (D) Individual-participant peak magnitude in normal versus inverted condition. A dot in the upper left or lower right triangle indicates a larger peak magnitude in the inverted or normal condition, respectively. The red dot reflects the participant average. (E) Same as (D) but for normal versus scrambled condition (n = 37). ** p < 0.01, *** p < 0.001. Results indicate that predictive representation for high-level view-invariant body motion is selectively attenuated with top-down inversion, indicating that such high-level prediction relies on holistic priors. Instead, temporal scrambling attenuates all predictive representation, indicating that view-dependent body and pixelwise motion prediction rely on kinematics priors.

Ingmar de Vries@ingmardevries· Feb 23