A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data‐fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are reviewed, with a focus on parameter space partitioning (PSP), a general‐purpose method for analyzing and comparing all classes of cognitive models. PSP (...) is then demonstrated in the second part of the article in which two connectionist models of speech perception (TRACE and ARTphone) are compared to learn how design differences affect model flexibility. (shrink)

Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). In this article, (...) we explore the problem space of these three parameters in the context of a task whose completion entails some combination of 36 state–action pairs, where all intermediate states (i.e., after the initial state and prior to the end state) represent progressive but partial completion of the task. Different choices produce profoundly different learning paths and outcomes, with the strongest effect for moment. Unfortunately, there is little discussion in the literature of the effect of such choices. This absence is disappointing, as the choice of when, what, and how much needs to be made by a modeler for every learning model. (shrink)

The following values have no corresponding Zotero field: PB - JSTOR.

Two tasks where failures of cognitive control are especially prevalent are task switching and spatial Simon task paradigms. Both tasks require considerable strategic control for the participant to avoid the costs associated with switching tasks (task-switching paradigm) and to minimize the influence of spatial location (Simon task). In the current study, we assessed whether the use of a self-regulatory strategy known as “implementation intentions” would have any beneficial effects on performance in each of these task domains. Forming an implementation intention (...) (i.e., an if–then plan) is a self- regulatory strategy in which a mental link is created between a pre-specified future cue and a desired goal-directed response, resulting in facilitated goal attainment (Gollwitzer in European Review of Social Psychology, 4, 141–185, 1993, American Psychologist, 54, 493–503, 1999). In Experiment 1, forming implementation intentions in the context of a taskswitching paradigm led to a reduction in switch costs. In Experiment 2, forming implementation intentions reduced the effects of spatial location in a Simon task for the stimulus specified in the implementation intention. Results supported the prediction that the need for high levels of cognitive control can be alleviated to some degree by making if–then plans that specify how one responds to that critical stimuli. (shrink)