Experimental evaluation of policies for sequencing the presentation of associations

Author

Katsikopoulos, Konstantinos V. ; Fisher, Donald L. ; Duffy, Susan A.

Author_Institution

Dept. of Mech. & Ind. Eng., Massachusetts Univ., Amherst, MA, USA

Volume

31

Issue

1

fYear

2001

fDate

1/1/2001 12:00:00 AM

Firstpage

55

Lastpage

59

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

Keywords

Bayes methods; Markov processes; content-addressable storage; learning (artificial intelligence); Bayesian estimates; Markov state model; arithmetic function maximization; association presentation sequencing; model states; model-based policy; modified-dropout policy; policy evaluation; Arithmetic; Bayesian methods; Error correction; Humans; Machine learning; Mathematical model; Optimization methods; State estimation; Testing; Vocabulary;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on

Publisher

ieee

ISSN

1083-4427

Type

jour

DOI

10.1109/3468.903866

Filename

903866