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bioRxiv · 10.1101/2023.04.18.537382

Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions

Abstract

Expectations are often dynamic: any sports fan knows that expectations are rapidly updated as games unfold. Yet expectations have traditionally been studied as static. Here, using slot machines as a case study, we provide parallel behavioral and electrophysiological evidence of sub-second moment-to-moment changes in expectations. In Study 1, we show that the dynamics of the EEG signal before the slot machine stopped differed depending on the nature of the outcome, including not only whether the participant won or lost, but also how close they came to winning. In line with our predictions, Near Win Before outcomes (the slot machine stops one item before a match) were similar to Wins, but different than Near Win After (the machine stops one item after a match) and Full Miss (the machine stops two or three items from a match). In Study 2, we designed a novel behavioral paradigm to measure moment-to-moment changes in expectations via dynamic betting. We found that different outcomes also elicited unique expectation trajectories in the deceleration phase. Notably, these behavioral expectation trajectories paralleled Study 1s EEG activity in the last second prior to the machines stop. In Studies 3 (EEG) and 4 (behavior) we replicated these findings in the loss domain where a match entails a loss. Again, we found a significant correlation between behavioral and EEG results. These four studies provide the first evidence that dynamic sub-second updates in expectations can be behaviorally and electrophysiologically measured. Our findings open up new avenues for studying the ongoing dynamics of reward expectations and their role in healthy and unhealthy cognition.

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BibTeXRIS

Marciano, D., Bellier, L., Mayer, I., Ruvalcaba, M., Lee, S., Hsu, M., Knight, R. T.. 2023-04-18. Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions. https://doi.org/10.1101/2023.04.18.537382

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