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Sequential effects in two-choice reaction time tasks: decomposition and synthesis of mechanisms

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Gao, Juan, Wong-Lin, KongFatt, Holmes, Philip, Simen, Patrick and Cohen, Jonathan (2009) Sequential effects in two-choice reaction time tasks: decomposition and synthesis of mechanisms. Neural Computation, 21 (9). pp. 2407-2436. [Journal article]

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URL: http://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.09-08-866?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed

DOI: 10.1162/neco.2009.09-08-866

Abstract

Performance on serial tasks is influenced by first- and higher-order sequential effects, respectively, due to the immediately previous and earlier trials. As response-to-stimulus interval (RSI) increases, the pattern of reaction times transits from a benefit-only mode, traditionally ascribed to automatic facilitation (AF), to a cost-benefit mode, due to strategic expectancy (SE). To illuminate the sources of such effects, we develop a connectionist network of two mutually inhibiting neural decision units subject to feedback from previous trials. A study of separate biasing mechanisms shows that residual decision unit activity can lead to only first-order AF, but higher-order AF can result from strategic priming mediated by conflict monitoring, which we instantiate in two distinct versions. A further mechanism mediates expectation-related biases that grow during RSI toward saturation levels determined by weighted repetition (or alternation) sequence lengths. Equipped with these mechanisms, the network, consistent with known neurophysiology, accounts for several sets of behavioral data over a wide range of RSIs. The results also suggest that practice speeds up all the mechanisms rather than adjusting their relative strengths.

Item Type:Journal article
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
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21349
Deposited By:Dr Kongfatt Wong-Lin
Deposited On:09 Mar 2012 14:58
Last Modified:09 Mar 2012 14:58

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