From Rachmaninov to RIFT: Probing our perceptual priors

In this talk, I will present results from two recent MEG studies that are seemingly disparate, yet tell an overarching tale. First, I will present work that heavily features computational modelling of naturalistic stimuli, and uses this to shed light on neural surprise responses when people listen to (actual, real-life) music. Second, I will show data employing Rapid Invisible Frequency Tagging (RIFT) that demonstrate how people learn spatially predictive priors in a visual search task. Finally, I will speculate on how computational modelling of naturalistic stimuli and RIFT may prove an ideal marriage when one wishes to understand the neural dynamics underlying real-world perception.

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