Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (...) (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children’s classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults. (shrink)
We critically review key lines of evidence and theoretical argument relevant to Machery's These include interactions between different kinds of concept representations, unified approaches to explaining contextual effects on concept retrieval, and a critique of empirical dissociations as evidence for concept heterogeneity. We suggest there are good grounds for retaining the concept construct in human cognition.
The proposal regarding rules and similarity is considered in terms of ability to provide insights regarding previous work on reasoning and categorization. For reasoning, the issue is the relation between this proposal and one-process as well as two-process accounts of deduction and induction. For categorization, the issue is how the proposal would simultaneously explain both similarity-to-rule and rule-to-similarity shifts.
Bayesian accounts are currently popular in the field of inductive reasoning. This commentary briefly reviews the limitations of one such account, the Rational Model (Anderson 1991b), in explaining how inferences are made about objects whose category membership is uncertain. These shortcomings are symptomatic of what Jones & Love (J&L) refer to as Bayesian approaches.
The discovery of a quaternary complexity limitation to mature cognitive operations raises important theoretical issues. We propose that cognitive limitations arise naturally in a complex dynamic information processing system, and consider problems such as the distinction between parallel and serial processes and the representativeness of the empirical data base used by Halford et al. to support their relational complexity scheme.
