Quantitative modeling of human information processing systems, neural networks and memory.
Associated with any cognitive activity is a flow of information originating from the senses. This information flows through higher and higher levels in the brain, until motor responses are executed. In this way we perform very simple tasks, such as pressing a button when a red light appears, or very complex tasks, such as hitting a fast ball or writing down the solution to an algebraic equation. The study of the way information flows and is transformed during cognitive operations is called human information processing, which is my primary interest. In particular, I am concerned with the quantitative modeling of the structures involved in simple cognitive tasks. Often, the predictions of a particular information processing model are unclear until that model is quantified: that is, until we have defined a mathematical description of how the physical variables of a task might be transformed into the observed behavior.
Another of my interests involves the application of information-processing to issues of memory and performance. For example, neural network models have been very successful in reproducing basic memory phenomena, but it is often difficult to find ways to test network models. By drawing comparisons between network models and more traditional, information-processing models, we can test basic assumptions of the network models and increase our understanding of the ways that information is stored and retrieved.
Turner, B.M., Dennis, S. & Van Zandt, T. (2013). Likelihood-free Bayesian analysis of memory models. Psychological Review, 120, 667-678.
Turner, B.M. & Van Zandt, T. (2012). A tutorial on Approximate Bayesian Computation. Journal of Mathematical Psychology, 56, 69-85.
Turner, B.M., Van Zandt, T. & Brown, S. (2011). A dynamic, stimulus-driven model of signal detection. Psychological Review, 118, 583-613.
Craigmile, P.F., Peruggia, M., & Van Zandt, T. (2010). Hierarchical Bayes models for response time data. Psychometrika, 75, 613-632.
Merkle, E.C., Sieck, W.R., & Van Zandt, T. (2008). Response error and processing biases in confidence judgment. Journal of Behavioral Decision Making, 21, 428-448. 19.
Otter, T., Allenby, G.M., & Van Zandt, T. (2008). An integrated model of discrete choice and response time.Journal of Marketing Resesarch, 45, 593-607.
Sieck, W.R., Merkle, E.C., & Van Zandt, T. (2007). Option fixation: A cognitive contributor to overconfidence. Organizational Behavior and Human Decision Processes, 103, 68-83.
Merkle, E.C. & Van Zandt, T. (2006). An application of the Poisson race model to confidence calibration. Journal of Experimental Psychology: General, 135, 391-408.
Van Zandt, T. & Maldonado-Molina, M.A. (2004). Response reversals in recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1147-1166.
Peruggia, M., Van Zandt, T., & Chen, M. (2002). Was it a car or a cat I saw? An analysis of response times for word recognition. Case Studies in Bayesian Statistics , 7.
Van Zandt, T.(2002). Analysis of response time distributions. In J. T. Wixted (Vol. Ed.) & H. Pashler (Series Ed.) Stevens' Handbook of Experimental Psychology (3rd Edition), Volume 4: Methodology in Experimental Psychology(pp. 461-516). New York: Wiley Press.
Smith, P.L. & Van Zandt, T.(2002). Time-dependent Poisson counter models of response latency in simple judgment. British Journal of Mathematical and Statistical Psychology, 53, 293-315.
Van Zandt, T.(2000). How to fit a response time distribution. Psychonomic Bulletin and Review, 7, 424-465.
Van Zandt, T., Colonius, H, and Proctor, R. W. (2000). A comparison of two reaction-time models applied to perceptual matching. Psychonomic Bulletin and Review, 7, 208-256.
Van Zandt, T. (2000). ROC curves and confidence judgments in recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 582-600.