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SIGNAL‐DETECTION ANALYSES OF CONDITIONAL DISCRIMINATION AND DELAYED MATCHING‐TO‐SAMPLE PERFORMANCE
Author(s) -
Alsop Brent
Publication year - 2004
Publication title -
journal of the experimental analysis of behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.75
H-Index - 61
eISSN - 1938-3711
pISSN - 0022-5002
DOI - 10.1901/jeab.2004.82-57
Subject(s) - computer science , matching (statistics) , detection theory , sample (material) , monte carlo method , artificial intelligence , reinforcement , stimulus control , stimulus (psychology) , machine learning , psychology , statistics , econometrics , cognitive psychology , social psychology , mathematics , neuroscience , telecommunications , chemistry , chromatography , detector , nicotine
Quantitative analyses of stimulus control and reinforcer control in conditional discriminations and delayed matching‐to‐sample procedures often encounter a problem; it is not clear how to analyze data when subjects have not made errors. The present article examines two common methods for overcoming this problem. Monte Carlo simulations of performance demonstrated that both methods introduced systematic deviations into the results, and that there were genuine risks of drawing misleading conclusions concerning behavioral models of signal detection and animal short‐term memory.