Associating a novel situation with a specific outcome involves a cascade of cognitive processes, including selecting relevant stimuli, forming predictions regarding expected outcomes, and updating memorized predictions based on experience. The present manuscript uses computational modeling and machine learning to test the hypothesis that alpha-band (8-12 Hz) oscillations are involved in the updating of expectations based on experience. Participants learned that a visual cue predicted an aversive loud noise with a probability of 50%. The Rescorla-Wagner model of associative learning explained trial-wise changes in self-reported noise expectancy as well as alpha power changes. Experience in the past trial and self-reported expectancy for the subsequent trial were accurately decoded based on the topographical distribution of alpha power at specific latencies. Decodable information during initial association formation and contingency report recurred when viewing the conditioned cue. Findings support the idea that alpha oscillations have multiple, temporally specific, roles in the formation of associations between cues and outcomes.
Keywords: EEG; Rescorla-Wagner; alpha oscillations; computational modeling; machine classification.
© 2021 Society for Psychophysiological Research.