Structural Parameter Interdependencies in Cognitive Models

Computational modeling of cognition allows measurement of latent psychological variables, such as risk aversion or attention, by means of free model parameters. The estimation and interpretation of these variables is impaired, however, if parameters strongly correlate with each other. We suggest that strong parameter intercorrelations are especially likely to emerge in models that combine a subjective value function with a probabilistic choice rule—a common structure in the literature. We demonstrate high intercorrelation between parameters in the value function and the probabilistic choice rule across several prominent computational models, including risky choice (cumulative prospect theory), categorization (the generalized context model), and memory (the SIMPLE model of free recall). Based on simulation studies, we show that the presence of parameter intercorrelations severely hampers efforts to achieve estimation accuracy, to detect group differences on the parameters, and to detect associations of the parameters with external variables. Further, we show that these problems can be alleviated by changing the error component, such as assuming parameter stochasticity or a constant error term. Our analyses highlight a common but typically neglected problem of computational modeling of cognition and point to ways in which the design and application of such models can be improved.