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Incorporating Functional Response Time Effects into a Signal Detection Theory Model

Author

Listed:
  • Sun-Joo Cho

    (Vanderbilt University)

  • Sarah Brown-Schmidt

    (Vanderbilt University)

  • Paul De Boeck

    (The Ohio State University and KU Leuven)

  • Matthew Naveiras

    (Vanderbilt University)

  • Si On Yoon

    (University of Iowa)

  • Aaron Benjamin

    (University of Illinois at Urbana-Champaign)

Abstract

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401–409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4 R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.

Suggested Citation

  • Sun-Joo Cho & Sarah Brown-Schmidt & Paul De Boeck & Matthew Naveiras & Si On Yoon & Aaron Benjamin, 2023. "Incorporating Functional Response Time Effects into a Signal Detection Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1056-1086, September.
  • Handle: RePEc:spr:psycho:v:88:y:2023:i:3:d:10.1007_s11336-023-09906-9
    DOI: 10.1007/s11336-023-09906-9
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    References listed on IDEAS

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    1. Inhan Kang & Paul Boeck & Roger Ratcliff, 2022. "Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 725-748, June.
    2. Simon N. Wood, 2013. "On p-values for smooth components of an extended generalized additive model," Biometrika, Biometrika Trust, vol. 100(1), pages 221-228.
    3. Florin Vaida & Suzette Blanchard, 2005. "Conditional Akaike information for mixed-effects models," Biometrika, Biometrika Trust, vol. 92(2), pages 351-370, June.
    4. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    5. Giampiero Marra & Simon N. Wood, 2012. "Coverage Properties of Confidence Intervals for Generalized Additive Model Components," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(1), pages 53-74, March.
    6. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    7. Chen, Haiqin & De Boeck, Paul & Grady, Matthew & Yang, Chien-Lin & Waldschmidt, David, 2018. "Curvilinear dependency of response accuracy on response time in cognitive tests," Intelligence, Elsevier, vol. 69(C), pages 16-23.
    8. Simon N. Wood, 2006. "Low-Rank Scale-Invariant Tensor Product Smooths for Generalized Additive Mixed Models," Biometrics, The International Biometric Society, vol. 62(4), pages 1025-1036, December.
    9. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    10. Jeffrey Rouder & Jun Lu & Dongchu Sun & Paul Speckman & Richard Morey & Moshe Naveh-Benjamin, 2007. "Signal Detection Models with Random Participant and Item Effects," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 621-642, December.
    11. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.
    12. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    13. Kang, Inhan & De Boeck, Paul & Partchev, Ivailo, 2022. "A randomness perspective on intelligence processes," Intelligence, Elsevier, vol. 91(C).
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