Exploring the Common Mechanisms of Motion-Based Visual Prediction

• Published in: Frontiers in psychology Vol. 13; p. 827029
• Main Authors: Hu, Dan, Ison, Matias, Johnston, Alan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 22.03.2022
• Subjects: alpha activity; individual difference; motion; motion adaptation; Psychology; spatial illusion; visual prediction; motion adaptation; individual difference; motion; visual prediction; spatial illusion; alpha activity
• ISSN: 1664-1078; 1664-1078
• DOI: 10.3389/fpsyg.2022.827029

Human vision supports prediction for moving stimuli. Here we take an individual differences approach to investigate whether there could be a common processing rate for motion-based visual prediction across diverse motion phenomena. Motion Induced Spatial Conflict (MISC) refers to an incongruity arising from two edges of a combined stimulus, moving rigidly, but with different apparent speeds. This discrepancy induces an illusory jitter that has been attributed to conflict within a motion prediction mechanism. Its apparent frequency has been shown to correlate with the frequency of alpha oscillations in the brain. We asked what other psychophysical measures might correlate positively with MISC frequency. We measured the correlation between MISC jitter frequency and another three measures that might be linked to motion-based spatial prediction. We demonstrate that the illusory jitter frequency in MISC correlates significantly with the accrual rate of the Motion Induced Position Shift (MIPS) effect - the well-established observation that a carrier movement in a static envelope of a Gabor target leads to an apparent position shift of the envelope in the direction of motion. We did not observe significant correlations with the other two measures - the Adaptation Induced Spatial Shift accrual rate (AISS) and the Smooth Motion Threshold (SMT). These results suggest a shared perceptual rate between MISC and MIPS, implying a common periodic mechanism for motion-based visual prediction.