Statistical models as cognitive models of individual differences in reasoning

Abstract

There are individual differences in reasoning which go beyond dimensions of ability. Valid models of cognition must take these differences into account, otherwise they characterise group mean phenomena which explain nobody. The gap is closing between formal cognitive models, which are designed from the ground up to explain cognitive phenomena, and statistical models, which traditionally concern the more modest task of modelling relationships in data. This paper critically reviews three illustrative statistical models of individual differences in reasoning which embed some notion of cognitive process. Although the models are each developed in different frameworks, it is shown that they are more similar than would first appear. The cognitive meaning of elements in the example models is explored and some sketches are developed for future directions of research.

Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance

“…For example, with the recent efforts to make Bayesian inference models applicable for the broader research community, probabilistic models and corresponding modeling paradigms (especially with respect to model evaluation and selection) have seen a surge in popularity (Vandekerckhove, Rouder, & Kruschke, 2018). However, critics argue that while ideal for discovering statistical relationships which can be tied to high‐level theoretical assumptions, Bayesian models cannot be used as algorithmic or process‐focused approximations of cognition (Fugard & Stenning, 2013; Stenning & Cox, 2006). Instead, some authors consider probabilistic models “a confession of ignorance, in that one might be viewed as obliged to push the model one step further and specify the mechanisms that would generate one set of probabilities as opposed to another” (Guyote & Sternberg, 1981).…”