Abstract
Sensitivity to the environment's sequential regularities makes it possible to predict upcoming sensory events. To investigate the mechanisms that monitor such predictions, we recorded scalp EEG as subjects learned to reproduce sequences of motions. Each sequence was seen and reproduced four successive times, with occasional deviant directions of motion inserted into otherwise-familiar and predictable sequences. To dissociate the neural activity associated with encoding new items from that associated with detecting sequence deviants, we measured ERPs to new, familiar, and deviant sequence items. Both new and deviant sequence items evoked enhanced P3 responses, with the ERP to deviant items encompassing both P300-like and Novelty P3-like subcomponents with distinct timing and topographies. These results confirm that the neural response to deviant items differs from that to new items, and that unpredicted events in newly-learned sequences are identified by processes similar to those monitoring stable sequential regularities.
Highlights
The human brain frequently operates in feedforward mode, exploiting previously-experienced regularities to build expectations for future events
Note that Maryott et al.’s task differs from traditional sequence-learning paradigms in that it required that the brain frequently update its representation of the governing sequential structure, as a new sequence began every 60–90 s. To investigate whether this dynamic, changing context would alter the brain’s response to a deviant sequence item, we recorded EEG signals from subjects as they performed a variant of the task used by Maryott et al, and measured Event-related brain potentials (ERPs) to new, familiar, and deviant sequence items. We show that both new and deviant sequence items evoke a larger P3 than familiar sequence items do, but that the topography and time course of the P3 enhancement elicited by new items differs from that elicited by deviant items
Differences between the neural responses to new and to deviant stimuli highlight the importance of prediction-monitoring in cognition
Summary
The human brain frequently operates in feedforward mode, exploiting previously-experienced regularities to build expectations for future events. This proactive operation facilitates perceptual processing (Bar, 2009) and allows appropriate behaviors to be prepared and executed in a timely fashion (e.g., Kowler, 1989; Maryott et al, 2011). In order to benefit fully from the advantages of feedforward operation, the brain must have a mechanism to detect events that violate its expectations, and to trigger appropriate responses to those violations (Winkler, 2007) Such responses might include heightening attention to the unexpected event, modifying or delaying a prepared behavior, or updating the brain’s representation of the regularity at hand. Successful prediction monitoring must distinguish prediction errors that are due to stochasticity or noise from errors that reflect a genuine change in the rules governing the environment (Yu and Dayan, 2005; Nassar et al, 2010)
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