Biomedical subjects

Bob Rehder

Publications and source records attributed to Bob Rehder.

8 recordsLinked to original sources

PubMed2006

How causal knowledge affects classification: A generative theory of categorization.

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes.

Association Learning↗

PubMed2006

When similarity and causality compete in category-based property generalization.

Five experiments were performed to investigate the category-based generalization of nonblank properties, properties that were novel but that were attributed to existing category features with causal explanations. Experiments 1-3 tested how such explanations interact with the well-known effects of similarity on such generalizations. The results showed that when the causal explanations were used, standard effects of typicality (Experiment 1), diversity (Experiment 2), or similarity itself (Experiment 3) were almost completely eliminated. Experiments 4 and 5 demonstrated that category-based generalizations exhibit some of the standard properties of causal reasoning; for example, an effect (i.e., a novel category property) is judged to be more prevalent when its cause (i.e., an existing category feature) is also prevalent. These findings suggest that category-based property generalization is often an instance of causal inference.

Animals↗

PubMed2005

Eyetracking and selective attention in category learning.

An eyetracking version of the classic Shepard, Hovland, and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning often involves learning to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. We also found that learners tend to fixate all stimulus dimensions early in learning. This result obtained despite evidence that participants were also testing one-dimensional rules during this period. Finally, the restriction of eye movements to only relevant dimensions tended to occur only after errors were largely (or completely) eliminated. We interpret these findings as consistent with multiple-systems theories of learning which maximize information input in order to maximize the number of learning modules involved, and which focus solely on relevant information only after one module has solved the learning problem.

Association Learning↗

PubMed2005

Thirty-something categorization results explained: selective attention, eyetracking, and models of category learning.

An eyetracking study testing D. L. Medin and M. M. Schaffer's (1978) 5-4 category structure was conducted. Over 30 studies have shown that the exemplar-based generalized context model (GCM) usually provides a better quantitative account of 5-4 learning data as compared with the prototype model. However, J. D. Smith and J. P. Minda (2000) argued that the GCM is a psychologically implausible account of 5-4 learning because it implies suboptimal attention weights. To test this claim, the authors recorded undergraduates' eye movements while the students learned the 5-4 category structure. Eye fixations matched the attention weights estimated by the GCM but not those of the prototype model. This result confirms that the GCM is a realistic model of the processes involved in learning the 5-4 structure and that learners do not always optimize attention, as commonly supposed. The conditions under which learners are likely to optimize attention during category learning are discussed.

Attention↗

PubMed2004

Feature inference and the causal structure of categories.

The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes' nets generally predicted participants' feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes' nets-the causal Markov condition-because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a "well functioning" category member when it has most or all of the category's characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.

Analysis of Variance↗

PubMed2004

Category coherence and category-based property induction.

One important property of human object categories is that they define the sets of exemplars to which newly observed properties are generalized. We manipulated the causal knowledge associated with novel categories and assessed the resulting strength of property inductions. We found that the theoretical coherence afforded to a category by inter-feature causal relationships strengthened inductive projections. However, this effect depended on the degree to which the exemplar with the to-be-projected predicate manifested or satisfied its category's causal laws. That is, the coherence that supports inductive generalizations is a property of individual category members rather than categories. Moreover, we found that an exemplar's coherence was mediated by its degree of category membership. These results were obtained across a variety of causal network topologies and kinds of categories, including biological kinds, non-living natural kinds, and artifacts.

Cognition↗

PubMed2003

A causal-model theory of conceptual representation and categorization.

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.

Causality↗

PubMed2003

A knowledge-resonance (KRES) model of category learning.

This article introduces a connectionist model of category learning that takes into account the prior knowledge that people bring to new learning situations. In contrast to connectionist learning models that assume a feedforward network and learn by the delta rule or backpropagation, this model, the knowledge-resonance model, or KRES, employs a recurrent network with bidirectional symmetric connection whose weights are updated according to a contrastive Hebbian learning rule. We demonstrate that when prior knowledge is represented in the network, KRES accounts for a considerable range of empirical results regarding the effects of prior knowledge on category learning, including (1) the accelerated learning that occurs in the presence of knowledge, (2) the better learning in the presence of knowledge of category features that are not related to prior knowledge, (3) the reinterpretation of features with ambiguous interpretations in light of error-corrective feedback, and (4) the unlearning of prior knowledge when that knowledge is inappropriate in the context of a particular category.

Animals↗