Cognitive structural equation models

AgencyNational Science Foundation
PanelMethods, Measurement, and Statistics
LocationUniversity of California, Irvine
Start dateSeptember 2012
End dateAugust 2016
Budget$ 250,000.00
Agency code1230118
LeadJoachim Vandekerckhove
Project text pn-1230118.pdf

Abstract

This project will combine two types of data analysis strategies that are common in different fields. In cognitive psychology, the state of the art is cognitive process modeling. Data are analyzed by fitting mathematical representations of cognitive functions to data and interpreting the obtained parameter estimates. In psychometrics, the most common form of data analysis involves latent variable modeling. Batteries of small tests, each individual test imperfect, are jointly analyzed to uncover unobservable underlying factors, such as general intelligence or specific abilities. Cognitive modeling succeeds in extracting more information from data, whereas psychometrical methods are useful for pooling information across tasks or participants. Combining these two traditions involves the formal challenges of applying latent variable structure to cognitive model parameters, integrating the mathematical assumptions of both strategies, and investigating the effects of those combined assumptions. The project also involves technical challenges, such as implementing the methods in software. A new hybrid method called cognitive structural equation modeling will be applied in a retrospective analyses of data on cognitive executive functions and data on facets of working memory. Additionally, a cognitive structural equation model will be used to investigate the stability of participants' behavior in cognitive tasks over time.

The new method will be particularly well suited for the simultaneous analysis of different cognitive tasks in order to uncover underlying structure in participants' aptitude in the tasks. Improvements in psychological measurement are potentially useful in a variety of contexts, ranging from fundamental research in perception, cognition, memory, decision making, emotion, and development, to applied measurement in educational testing, job selection, and psychodiagnosis. Software developed as part of this project will be made freely available to researchers.

Publications

Dutilh, G., Annis, J., Brown, S., Cassey, P., Evans, N., Grasman, R., Hawkins, G., Heathcote, A., Holmes, W., Krypotos, A., Kupitz, C., Leite, F., Lerche, V., Lin, Y., Logan, G., Palmeri, T., Starns, J., Trueblood, J., van Maanen, L., van Ravenzwaaij, D., Vandekerckhove, J., Visser, I., Voss, A., White, C., Wiecki, T., Rieskamp, J., & Donkin, C. (2019). The quality of response time data inference: A blinded, collaborative approach to the validity of cognitive models. Psychonomic Bulletin & Review, 26, 1051–1069.
Mistry, P., Pothos, E., Vandekerckhove, J., & Trueblood, J. (2018). A quantum probability account of individual differences in causal reasoning. Journal of Mathematical Psychology, 87, 76–97.
Vandekerckhove, J., Rouder, J., & Kruschke, J. (2018). Editorial: Bayesian methods for advancing psychological science. Psychonomic Bulletin & Review, 25, 1–4.
Baribault, B., Donkin, C., Little, D., Trueblood, J., Oravecz, Z., van Ravenzwaaij, D., White, C., De Boeck, P., & Vandekerckhove, J. (2018). Metastudies for robust tests of theory. Proceedings of the National Academy of Sciences, 115, 2607–2612.
Matzke, D., Boehm, U., & Vandekerckhove, J. (2018). Bayesian Inference in Psychology, Part III: Bayesian parameter estimation in nonstandard models. Psychonomic Bulletin & Review, 25, 77–101.
Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25, 5–34.
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76, 117–130.
Guan, M., & Vandekerckhove, J. (2016). A Bayesian approach to mitigation of publication bias. Psychonomic Bulletin & Review, 23, 74–86.
Etz, A., & Vandekerckhove, J. (2016). A Bayesian perspective on the Reproducibility Project: Psychology. PLoS ONE, 11, e0149794.
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2016). Bayesian data analysis with the bivariate hierarchical Ornstein-Uhlenbeck process model. Multivariate Behavioral Research, 51, 106–119.
Oravecz, Z., Huentelman, M., & Vandekerckhove, J. (2016). Sequential Bayesian updating for Big Data. Big Data in Cognitive Science: From Methods to Insights, 13–33.
Guan, M., Lee, M. D., & Vandekerckhove, J. (2015). A hierarchical cognitive threshold model of human decision making on different length optimal stopping problems. Proceedings of the 37th Annual Conference of the Cognitive Science Society, 824–829.
Kupitz, C., Buschkuehl, M., Jaeggi, S., Jonides, J., Shah, P., & Vandekerckhove, J. (2015). A diffusion model account of the transfer-of-training effect. Proceedings of the 37th Annual Conference of the Cognitive Science Society.
Vandekerckhove, J., Matzke, D., & Wagenmakers, E. (2015). Model comparison and the principle of parsimony. Oxford Handbook of Computational and Mathematical Psychology, 300–317.
Nunez, M. D., Srinivasan, R., & Vandekerckhove, J. (2015). Individual differences in attention influence perceptual decision making. Frontiers in Psychology, 6, 18.
Zhang, S., Lee, M. D., Vandekerckhove, J., Maris, G., & Wagenmakers, E. (2014). Time-varying boundaries for diffusion models of decision making and response time. Frontiers in Psychology, 5, 1364.
Lee, M. D., Newell, B., & Vandekerckhove, J. (2014). Modeling the adaptation of search termination in human decision making. Decision, 1, 223–251.
Murphy, P., Vandekerckhove, J., & Nieuwenhuis, S. (2014). Pupil-linked arousal determines variability in perceptual decision making. PLOS Computational Biology, 10, e1003854.
Vandekerckhove, J. (2014). A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. Journal of Mathematical Psychology, 60, 58–71.
Wiech, K., Vandekerckhove, J., Zaman, J., Tuerlinckx, F., Vlaeyen, J., & Tracey, I. (2014). Influence of prior information on pain involves biased perceptual decision-making. Current Biology, 24, R679–R681.
Wabersich, D., & Vandekerckhove, J. (2014). The RWiener package: an R package providing distribution functions for the Wiener diffusion model. The R Journal, 6, 49–56.
Oravecz, Z., Vandekerckhove, J., & Batchelder, W. (2014). Bayesian Cultural Consensus Theory. Field Methods, 26, 207–222.
Wabersich, D., & Vandekerckhove, J. (2014). Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example). Behavior Research Methods, 46, 15-28.