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Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Hierarchical Temporal Expectations

Maysan Bader1, Leon Deouell1, Israel Nelken1,2; 1Hebrew University of Jerusalem, 2Technion

Presenter: Maysan Bader

Predictive coding theory suggests that the brain continuously generates and updates predictions about sensory inputs across multiple modules and hierarchical levels. Prediction errors occur when the actual sensory information does not match the prediction, and these errors are propagated bottom-up to update the statistical representation. Prediction error has several neural correlates, including mismatch negativity (MMN), an auditory event-related potential evoked by the automatic detection of violations of expectations, typically occurring 100–250 ms after stimulus onset, and the P3b (a subcomponent of the P300), which peaks between 250–500 ms and reflects attentive, task-relevant global errors within a broader context. Here we contrast violations of local vs. global predictions. Local deviants consist of a deviation from a local prediction (e.g., xxxxY). These local deviants can themselves become predictable in repeating sequences, creating a situation in which the local standard (e.g., xxxxx) can be considered a violation in the global context (). Global–local paradigms based on manipulations of pure-tone frequency failed to show MMN in the global context under unattended conditions. Here, we applied a similar local–global paradigm to investigate the hierarchical organization of temporal expectations by manipulating the Inter-Stimulus Interval (ISI) instead of the frequency. Using this approach, we can explore the hierarchy of neural mechanisms underlying the processing of expectations in the temporal domain. Preliminary results showed a mismatch effect in the global deviant responses, peaking around 250 ms after the deviant's onset, with positive polarity in the pure global deviant and negative polarity in the local–global deviant.

Topic Area: Auditory, Speech & Language Processing