Source monitoring and multivariate signal detection theory, with a model for selection

Participants in source monitoring studies,in addition to determining whether an item is old or new,also discriminate the source of the item,such as whether the item was presented in a male or female voice. This article shows how to apply multivariate signal detection theory (SDT) to source monitoring. An interesting aspect of one version of the source monitoring procedure,from the perspective of multivariate SDT,is that it involves a type of selection,in that a discrimination response is observed only if the detection decision is that an item is old. If the selection is ignored,then the estimate of the discrimination parameter can be biased; the nature and magnitude of the bias are illustrated. A bivariate signal detection model that recognizes selection is presented and its application is illustrated. The approach to source monitoring via multivariate SDT provides new results that are informative about underlying psychological processes. r 2003 Elsevier Science (USA). All rights reserved.

[1]  Wilson P. Tanner,et al.  Theory of recognition. , 1956 .

[2]  B. Muthén Latent variable structural equation modeling with categorical data , 1983 .

[3]  A. Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[4]  R D Bock,et al.  High-dimensional multivariate probit analysis. , 1996, Biometrics.

[5]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[6]  J. H. Steiger Structural Model Evaluation and Modification: An Interval Estimation Approach. , 1990, Multivariate behavioral research.

[7]  B. Muthén A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators , 1984 .

[8]  A P Yonelinas,et al.  The contribution of recollection and familiarity to recognition and source-memory judgments: a formal dual-process model and an analysis of receiver operating characteristics. , 1999, Journal of experimental psychology. Learning, memory, and cognition.

[9]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[10]  Marcia K. Johnson,et al.  Source monitoring. , 1993, Psychological bulletin.

[11]  R. P. McDonald,et al.  Structural Equations with Latent Variables , 1989 .

[12]  E. Erdfelder,et al.  Source discrimination, item detection, and multinomial models of source monitoring. , 1996 .

[13]  T. Wickens Maximum-likelihood estimation of a multivariate Gaussian rating model with excluded data , 1992 .

[14]  L. T. DeCarlo Signal detection theory with finite mixture distributions: theoretical developments with applications to recognition memory. , 2002, Psychological review.

[15]  James H. Steiger,et al.  A note on multiple sample extensions of the RMSEA fit index , 1998 .

[16]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[17]  A P Shimamura,et al.  An analysis of signal detection and threshold models of source memory. , 2000, Journal of experimental psychology. Learning, memory, and cognition.

[18]  N. L. Johnson,et al.  Continuous Multivariate Distributions: Models and Applications , 2005 .

[19]  David M. Riefer,et al.  Theoretical and empirical review of multinomial process tree modeling , 1999, Psychonomic bulletin & review.

[20]  R. Kinchla Comments on Batchelder and Riefer's multinomial model for source monitoring. , 1994, Psychological review.

[21]  P. Schmidt,et al.  Limited-Dependent and Qualitative Variables in Econometrics. , 1984 .

[22]  W P Banks,et al.  Recognition and Source Memory as Multivariate Decision Processes , 2000, Psychological science.

[23]  J. Ashford,et al.  Multi-variate probit analysis. , 1970, Biometrics.

[24]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[25]  Lawrence T DeCarlo,et al.  An application of signal detection theory with finite mixture distributions to source discrimination. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[26]  Anni-Yasmin Turhan,et al.  RACE User''s Guide and Reference Manual , 1999 .

[27]  J. G. Snodgrass,et al.  A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. , 1980, Journal of experimental psychology. Human learning and memory.

[28]  N. Perrin,et al.  Varieties of perceptual independence. , 1986, Psychological review.

[29]  David M. Riefer,et al.  Multinomial processing models of source monitoring. , 1990 .

[30]  C. L. Raye,et al.  Source ROCs are (typically) curvilinear: comment on Yonelinas (1999). , 2001, Journal of experimental psychology. Learning, memory, and cognition.

[31]  Marcia K. Johnson,et al.  STEREOTYPE RELIANCE IN SOURCE MONITORING: AGE DIFFERENCES AND NEUROPSYCHOLOGICAL TEST CORRELATES , 1999 .

[32]  Alan Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[33]  Tx Station Stata Statistical Software: Release 7. , 2001 .

[34]  Murray Glanzer,et al.  Regularities of source recognition: ROC analysis. , 2002, Journal of experimental psychology. General.

[35]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .