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Project

Dynamic cognitive psychometrics: modeling temporal changes in response processes.

The unobserved processes that operate when people make decisions are a topic of much scientific interest. One aspect of decision making processes that has recently received some consideration is how properties of the processes change over short time spans (e.g. during an experiment). For examples, participants may become more cautions after a series of errors, or less careful if their accuracy is high but they consider their speed insufficient. Not only is it important to account for such temporal dynamics from a statistical point of view (typical contemporary models falsely assume interchangeability of responses under identical experimental conditions), but the dynamics of temporal change may reveal certain participant dispositions (fast/slow adaptation, high/low desired level of accuracy, etc.) or properties of the cognitive architecture. While several dynamical models have been proposed, two aspects have typically been lacking. First, across-trial dynamics are often detached from response processes - meaning that changes are modeled in raw data rather than in (more interpretable) latent process parameters. We intend to add a laten (psychometric) measurement level to dynamical models. Second, these models require appropirate fifting algorithms. We will exploit similarities to the statistical literature of dynamical systems modeling to fit "dynamical psychometric models" to experimental data.
Date:1 Oct 2010 →  30 Oct 2012
Keywords:Dynamical psychometrics, Mathematical modeling, Process models
Disciplines:Applied psychology, Applied mathematics in specific fields, Statistics and numerical methods, Biological and physiological psychology, Human experimental psychology