Abstract

Although recognition memory models have been thoroughly compared in various recognition memory paradigms, the relative reliability and validity of their parameters have not been thoroughly assessed using an individual differences approach. In two studies, I evaluated three models: the dual-process signal detection (DPSD) model, the continuous dual process (CDP) model, and the unequal variance signal detection (UVSD) model. In Study 1, participants performed a remember-know procedure that also included confidence ratings. When model parameters were estimated twice in the same individual, both key parameters from the DPSD model were reliable within an individual, whereas the CDP version of familiarity was not reliable (ICC < .40). Fitting the UVSD model also produced reliable parameters, although the variance parameter was only moderately so. In Study 2, participants performed tests of fluid intelligence, processing speed, and recall along with the same recognition procedure as in Study 1. Structural equation modeling comparing the models’ ability to predict cognitive variables suggested that the parameters accounted for an equal proportion of variability in gF. However, the DPSD was the lone model with two parameters that predicted variance in gF Assessing the reliability and construct validity of the models’ parameters within an individual differences’ framework provided a novel test of these models. Together, the results from these studies suggest that the DPSD is the most reliable model and exhibits convergent validity with other cognitive constructs, but that there is room for further assessment of the UVSD and the DPSD using an individual differences approach

Committee Chair

Sandra Hale

Committee Members

Andrew C. Butler

Degree

Doctor of Philosophy (PhD)

Author's Department

Psychology

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

Winter 12-15-2021

Language

English (en)

Author's ORCID

http://orcid.org/0000-0002-0249-2745

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Psychology Commons

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