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Competition, gating and learning: A new computational model of task switching

Todd, Michael, Wong, Kong-Fatt and Cohen, Jonathan (2007) Competition, gating and learning: A new computational model of task switching. In: Society for Neuroscience 2007, San Diego. Society for Neuroscience. 1 pp. [Conference contribution]

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Task switching has been the subject of extensive research, as it offers a valuable window into the dynamics by which cognitive control states interact with each other and are updated in response to shifting contextual demands. Recent work suggests that gating, learning, and performance monitoring mechanisms play key, interrelated roles in the emergence of counterintuitive task switching phenomena such as residual switch costs (Rogers & Monsell, 1995), backward inhibition (Mayr & Keele, 2000), and asymmetrical switch costs (Wylie & Allport, 2000). We introduce a new computational model of these phenomena. Based on Guided Activation Theory (Miller & Cohen, 2001), the model adds gating (Braver & Cohen, 1999), performance monitoring (Botvinick et al., 2001), and learning mechanisms, combining the latter two into a novel Competition-Driven Learning (CDL) rule which is related to Norman et al.’s biologically plausible oscillatory learning rule (2006). CDL uses response conflict to drive inhibitory learning between incompatible task representations, punishing irrelevant, interfering tasks. The model successfully reproduces the above phenomena. Lesions of the model’s gating, learning, and performance monitoring components demonstrate that a simpler model cannot accommodate the same findings equally well. Finally, the model makes novel predictions about task switching, such as the shape of an entire time series of RT distributions, which would not have been testable without an explicit computational model.

Item Type:Conference contribution (Poster)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:21437
Deposited By: Dr Kongfatt Wong-Lin
Deposited On:15 Mar 2012 15:07
Last Modified:15 Mar 2012 15:07

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