Editorial: Modeling Individual Differences in Perceptual Decision Making

Researchers have been interested in how human beings accumulate and process information for decision-making since the development of experimental psychology in the late nineteenth century and then its renaissance in cognitive science in the 1960s. Whereas psychometrics and test theory, which also got their start in the nineteenth century have made individual differences the foundation of their fields, the study of cognitive processes has traditionally, and over many decades, assumed that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. The typical approach in cognitive psychology has assumed that individual variation affects perceptual processing parametrically (e.g., rate of information accumulation, response bias), but not structurally (e.g., the order of information processing). For example, when using information in working memory, some individuals may be faster, but it is assumed that all individuals use the information in the same manner. With that assumption, the usual practice of developing models is based on grouped data, rather than the individual data. However, a growing number of studies have demonstrated systematic individual differences in perceptual decision-making. These individual differences can be reflected in both parametric variation corresponding to characteristics of the participants (e.g., working memory span) and structural differences (i.e., in the same task context, different individuals search across visual-spatial information and phonetic information in sequence while others search in parallel). Hence, we as researchers need more complex modeling tools than traditional linear models with null-hypothesis testing to investigate the influences of task, context, and individual differences as well as the potential for interactions among these factors. In this special issue, we focused on a particular subset of cognitive models that explicitly allow for both structural and parametric variation across individuals, particularly multinomial processing trees, and systems factorial technology (SFT) applied to perceptual decision-making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual's performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in …