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Formal requirements of Markov state models for paired associate learning

Formal requirements of Markov state models for paired associate learning
Formal requirements of Markov state models for paired associate learning

Discrete state learning models that make Markov assumptions are a powerful tool for the analysis and optimization of performance in paired associate tasks. We seek here to derive bounds on the complexity needed by such models in order to account for the critical effects of lag and retention intervals on paired associate learning. More specifically, after establishing that two different Markov chains are needed (one for describing the effects of trials where a paired associate is presented and one for describing the effects of trials where the paired associate is not presented), we determine the minimum number of states required in a Markov model with two chains. It is shown formally that, under certain psychologically plausible assumptions, more than three states are required. A model with two chains and four states is presented and it is shown empirically that it can account for the lag and retention effects in paired associate learning.

Markov models; minimum complexity; lag effects
0022-2496
324-333
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Fisher, Donald L.
145dacf0-44d2-425d-a6e0-c253aca1b013
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Fisher, Donald L.
145dacf0-44d2-425d-a6e0-c253aca1b013

Katsikopoulos, Konstantinos V. and Fisher, Donald L. (2001) Formal requirements of Markov state models for paired associate learning. Journal of Mathematical Psychology, 45 (2), 324-333. (doi:10.1006/jmps.2000.1318).

Record type: Article

Abstract

Discrete state learning models that make Markov assumptions are a powerful tool for the analysis and optimization of performance in paired associate tasks. We seek here to derive bounds on the complexity needed by such models in order to account for the critical effects of lag and retention intervals on paired associate learning. More specifically, after establishing that two different Markov chains are needed (one for describing the effects of trials where a paired associate is presented and one for describing the effects of trials where the paired associate is not presented), we determine the minimum number of states required in a Markov model with two chains. It is shown formally that, under certain psychologically plausible assumptions, more than three states are required. A model with two chains and four states is presented and it is shown empirically that it can account for the lag and retention effects in paired associate learning.

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More information

Published date: 1 April 2001
Keywords: Markov models; minimum complexity; lag effects

Identifiers

Local EPrints ID: 439263
URI: http://eprints.soton.ac.uk/id/eprint/439263
ISSN: 0022-2496
PURE UUID: 0daaa6e3-15c7-4890-9065-653d6eca8460
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

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