Applying signal detection theory to contingency assessment

Siegel, Shepard, Allan, Lorraine G., Hannah, Samuel D. and Crump, Matthew J. C. (2009) Applying signal detection theory to contingency assessment. Comparative Cognition and Behavior Review, 4 116-134. doi:10.3819/ccbr.2009.40012

Author Siegel, Shepard
Allan, Lorraine G.
Hannah, Samuel D.
Crump, Matthew J. C.
Title Applying signal detection theory to contingency assessment
Journal name Comparative Cognition and Behavior Review   Check publisher's open access policy
ISSN 1911-4745
Publication date 2009
Sub-type Article (original research)
DOI 10.3819/ccbr.2009.40012
Open Access Status DOI
Volume 4
Start page 116
End page 134
Total pages 19
Publisher Comparative Cognition Society
Language eng
Abstract In most studies of contingency assessment participants judge the magnitude of the relationship between cues and outcomes. This judgment is a conflated measure of the participant’s sensitivity to the cue-outcome relationship, and his or her response bias. A psychophysical model (signal detection theory, SDT) can be used to dissect the independent contributions of sensitivity and bias to contingency judgment. Results of an experiment concerning cue-interaction (blocking) illustrate the utility of applying SDT to understanding contingency assessment. Most accounts of such assessment are associative (derived primarily from Pavlovian conditioning experiments with non-human animals). A psychophysical analysis of contingency assessment is not an alternative to such associative accounts. The SDT analysis supplements (not replaces) learning principles with psychophysical principles.
Keyword Associative learning
Contingency assessment
Cue interaction
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Faculty of Humanities and Social Sciences - Publications
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Created: Thu, 01 Dec 2011, 16:27:24 EST by Dr. Samuel Hannah on behalf of Faculty of Social & Behavioural Sciences