Experimental evaluation of policies for sequencing the presentation of associations
Experimental evaluation of policies for sequencing the presentation of associations
Two policies for sequencing the presentation of associations are compared to the standard policy of randomly cycling through the list of associations. According to the modified-dropout policy, on each trial an association is presented that has not been presented on the two most recent trials and on which the observed number of correct responses since the last error is minimum. The second policy is based on a Markov state model of learning: on each trial, an association is presented that maximizes an arithmetic function of Bayesian estimates of residence in model states, a function that approximately indexes how unlearned associations are. Retention is improved relative to the standard policy only for the model-based policy.
55-59
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Fisher, Donald L.
145dacf0-44d2-425d-a6e0-c253aca1b013
Duffy, Susan A.
79fd4355-c7bb-4ec6-a5a0-9897e5c2d96f
1 January 2001
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Fisher, Donald L.
145dacf0-44d2-425d-a6e0-c253aca1b013
Duffy, Susan A.
79fd4355-c7bb-4ec6-a5a0-9897e5c2d96f
Katsikopoulos, Konstantinos V., Fisher, Donald L. and Duffy, Susan A.
(2001)
Experimental evaluation of policies for sequencing the presentation of associations.
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 31 (1), .
(doi:10.1109/3468.903866).
Abstract
Two policies for sequencing the presentation of associations are compared to the standard policy of randomly cycling through the list of associations. According to the modified-dropout policy, on each trial an association is presented that has not been presented on the two most recent trials and on which the observed number of correct responses since the last error is minimum. The second policy is based on a Markov state model of learning: on each trial, an association is presented that maximizes an arithmetic function of Bayesian estimates of residence in model states, a function that approximately indexes how unlearned associations are. Retention is improved relative to the standard policy only for the model-based policy.
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Published date: 1 January 2001
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Local EPrints ID: 439257
URI: http://eprints.soton.ac.uk/id/eprint/439257
ISSN: 1083-4427
PURE UUID: 4726a934-1355-4c52-9d4e-ab62468462a9
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Date deposited: 07 Apr 2020 16:31
Last modified: 18 Mar 2024 03:38
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Author:
Donald L. Fisher
Author:
Susan A. Duffy
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