This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a (...) more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways. (shrink)

Journal Name: Cognitive Linguistics Issue: Ahead of print.

Recent studies have documented substantial variability among typical listeners in how gradiently they categorize speech sounds, and this variability in categorization gradience may link to how listeners weight different cues in the incoming signal. The present study tested the relationship between categorization gradience and cue weighting across two sets of English contrasts, each varying orthogonally in two acoustic dimensions. Participants performed a four‐alternative forced‐choice identification task in a visual world paradigm while their eye movements were monitored. We found that (a) (...) greater categorization gradience derived from behavioral identification responses corresponds to larger secondary cue weights derived from eye movements; (b) the relationship between categorization gradience and secondary cue weighting is observed across cues and contrasts, suggesting that categorization gradience may be a consistent within‐individual property in speech perception; and (c) listeners who showed greater categorization gradience tend to adopt a buffered processing strategy, especially when cues arrive asynchronously in time. (shrink)

Research in education and cognitive development suggests that explaining plays a key role in learning and generalization: When learners provide explanations—even to themselves—they learn more effectively and generalize more readily to novel situations. This paper proposes and tests a subsumptive constraints account of this effect. Motivated by philosophical theories of explanation, this account predicts that explaining guides learners to interpret what they are learning in terms of unifying patterns or regularities, which promotes the discovery of broad generalizations. Three experiments provide (...) evidence for the subsumptive constraints account: prompting participants to explain while learning artificial categories promotes the induction of a broad generalization underlying category membership, relative to describing items (Exp. 1), thinking aloud (Exp. 2), or free study (Exp. 3). Although explaining facilitates discovery, Experiment 1 finds that description is more beneficial for learning item details. Experiment 2 additionally suggests that explaining anomalous observations may play a special role in belief revision. The findings provide insight into explanation’s role in discovery and generalization. (shrink)