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Experimental evaluation of policies for sequencing the presentation of associations

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.

1083-4427
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
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), 55-59. (doi:10.1109/3468.903866).

Record type: Article

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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More information

Published date: 1 January 2001

Identifiers

Local EPrints ID: 439257
URI: http://eprints.soton.ac.uk/id/eprint/439257
ISSN: 1083-4427
PURE UUID: 4726a934-1355-4c52-9d4e-ab62468462a9
ORCID for Konstantinos V. Katsikopoulos: ORCID iD orcid.org/0000-0002-9572-1980

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Date deposited: 07 Apr 2020 16:31
Last modified: 09 Jan 2022 03:55

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Contributors

Author: Donald L. Fisher
Author: Susan A. Duffy

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