Causal models and the acquisition of category structure.

Abstract

This article proposes that learning of categories based on cause-effect relations is guided by causal models. In addition to incorporating domain-specific knowledge, causal models can be based on knowledge of such general structural properties as the direction of the causal arrow and the variability of causal variables. Five experiments tested the influence of commoncause models and common-effect models on the ease of learning linearly separable and nonlinearly separable categories. The results show that causal models guide the interpretation of otherwise identical learning inputs, and that learning difficulty is determined by the fit between the structural implications of the causal models and the structure of the learning domain. These influences of the general properties of causal models were obtained across several different content domains, including domains for which subjects lacked prior knowledge. Tasks as apparently diverse as classical conditioning, category learning, and causal induction often require the learner to combine multiple cues in order to elicit a response. The cues may be conditioned stimuli (in conditioning), features of category instances (in category learning), or possible causes (in causal induction). Numerous learning models have been proposed in each of these areas, and a great deal of theoretical interest has focused on the extent to which common learning mechanisms may operate across these formally similar tasks. Most of these theories model learning as a domain-general process, bottom-up and basically associative in nature, that applies across diverse domains. Recently, more top-down or theory-based approaches have been proposed, which view learning as guided by domain-specific theories. In the present article we outline a position that is intermediate between these two views. We claim that a major subset of learning situations—