Neural processes for learning and monitoring sequential regularities in changeable environments

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dc.contributor.advisor Sekuler, Robert
dc.contributor.author Noyce, Abigail LaBombard
dc.date.accessioned 2014-02-04T18:56:36Z
dc.date.available 2014-02-04T18:56:36Z
dc.date.issued 2014
dc.identifier.uri http://hdl.handle.net/10192/26313
dc.description.abstract The world is largely stable and predictable. Humans and other organisms are sensitive to that stability, and use it to support cognitive processes. This work consists of a series of studies that explore how humans learn about such stability, use that information to generate predictions about forthcoming sensory input, and detect when such predictions are inadequate. First, I present a modeling study that quantified the distributions of errors that people make on a complex, visuomotor sequence learning task, and examine the serial position dynamics of several parameters describing short-term visual memory. Both precision and capacity for these sequences increases with familiarity, and the worst-represented items show the largest increases. Next, I present an experiment that used the same task to understand the effects of deviant items within familiar sequences. By measuring ERPs to new, familiar, and deviant items, I dissociate the neural activity associated with detecting a deviant from that associated with encoding task-relevant stimulus characteristics. Finally, I present an experiment investigating the role of prediction in a task that is stochastic, rather than sequential, and in which deviant events occurred among the distractors rather than among the task-relevant stimuli. Unexpected events among the distractors seem to obligatorily attract attention, enhancing or impairing performance. Further, I show that the individual differences in the neural response to such unexpected events is predicted by temperament. Together, these studies illuminate how the brain learns about predictability in a range of settings, and leverages such predictability to facilitate cognition.
dc.description.sponsorship Brandeis University, Graduate School of Arts and Sciences
dc.format.mimetype application/pdf
dc.language English
dc.language.iso eng
dc.publisher Brandeis University
dc.relation.ispartofseries Brandeis University Theses and Dissertations
dc.rights Copyright by Abigail LaBombard Noyce 2014
dc.subject cognitive neuroscience
dc.subject EEG
dc.subject working memory
dc.subject attention
dc.subject psychology
dc.title Neural processes for learning and monitoring sequential regularities in changeable environments
dc.type Thesis
dc.contributor.department Department of Psychology
dc.degree.name PhD
dc.degree.level Doctoral
dc.degree.discipline Psychology
dc.degree.grantor Brandeis University, Graduate School of Arts and Sciences


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