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Sun-Joo Cho1, Sarah Brown-Schmidt2, Paul De Boeck3

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Summary
This summary is machine-generated.

Signal detection theory (SDT) now incorporates response time data to improve diagnostic accuracy. This extended SDT model enhances understanding of how we remember conversational language.

Keywords:
generalized additive mixed modelgeneralized linear mixed-effects modelresponse timesignal detection theorysmooth function

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Area of Science:

  • Cognitive psychology
  • Psychometrics
  • Computational neuroscience

Background:

  • Signal detection theory (SDT) is a key framework for evaluating diagnostic accuracy in psychology.
  • Integrating response time data into SDT models is an emerging area for distinguishing signal from noise.
  • Previous research suggests functional response time effects in related psychometric models.

Purpose of the Study:

  • To extend signal detection theory (SDT) by incorporating functional response time effects using smooth functions.
  • To account for all sources of variability in SDT model parameters across trials, participants, and items.
  • To apply the extended SDT model to recognition memory data for understanding conversational language memory.

Main Methods:

  • Formulated an extended SDT model with smooth functions as a generalized linear mixed-effects model.
  • Implemented the model using the gamm4 R package.
  • Conducted a simulation study to evaluate parameter estimation accuracy and the importance of modeling variability.

Main Results:

  • Demonstrated the accuracy of parameter estimates in the extended SDT model.
  • Showcased the importance of modeling variability for detecting experimental condition and response time effects.
  • Evaluated the type 1 error rate for testing smooth functions of response time.

Conclusions:

  • The extended SDT model effectively incorporates functional response time effects and variability.
  • This approach enhances the analysis of diagnostic accuracy and memory research.
  • The findings support the utility of advanced statistical modeling in cognitive psychology.