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Model evaluation via stochastic parameter convergence as on‐line system identification
Author(s) -
Gregson Robert A. M.
Publication year - 1980
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.1980.tb00774.x
Subject(s) - convergence (economics) , consistency (knowledge bases) , identification (biology) , estimation theory , computer science , similarity (geometry) , system identification , model parameter , algorithm , mathematics , mathematical optimization , data mining , artificial intelligence , measure (data warehouse) , botany , economics , image (mathematics) , biology , economic growth
The comparative testing of models of cognitive judgements has previously been done by post hoc parameter estimation on data gathered without feedback. If on any trial within an experiment responses are tested against competing models, and feedback is provided to the subject on his partial consistency with a currently best‐fitting model, then under some conditions behavioural and parameter convergence may be stochastically achieved. An algorithm SIMFBS with parameter convergence potential is described and its performance examined on up to five models of similarity, with various forms of memory built into the algorithm.