Dan Arista, Graduate Student, RPI

 

Dan Arista, Graduate Student, RPI

Sage 4101

September 13, 2017 12:00 PM - 1:30 PM

The implicit learning of unattended, task-irrelevant, statistical regularities that coincide with perceived successful task completion is a well observed phenomena. The cues to recall these memories and the ensuing guiding of attention to the reward associated property are also driven by implicit cognitive processes. This ability of implicit processes to learn and recognize contexts, and to automatically guide attention to reward associated features can provide obvious behavioral benefits. However what if these environmental patterns are mere coincidences? Could these contextual memories be updated to reflect what the organism consciously believes is task-relevant as to dampen or remove their cueing effect? In this talk I will propose a computational model of how relevance-based reconsolidation of contextual memories occur. Leveraging the rich literature of Contextual Cueing (Chun & Jiang 1998) and the Arcadia cognitive architecture, I’ve built a computational model of the proposed theory which performs a difficult visual search task: learning, recalling, and consolidating contextual memories with human like results. The modelling effort availed some interesting theoretical implications for Cognitive Science and Artificial Intelligence via the interaction of reward, attention, and memory; which may be discussed if time permits.

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