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Aaron B Hoffman

Publications and source records attributed to Aaron B Hoffman.

4 recordsLinked to original sources
PubMed2006

Category dimensionality and feature knowledge: when more features are learned as easily as fewer.

Three experiments compared the learning of lower-dimensional family resemblance categories (4 dimensions) with the learning of higher-dimensional ones (8 dimensions). Category-learning models incorporating error-driven learning, hypothesis testing, or limited capacity attention predict that additional dimensions should either increase learning difficulty or decrease learning of individual features. Contrary to these predictions, the experiments showed no slower learning of high-dimensional categories; instead, subjects learned more features from high-dimensional categories than from low-dimensional categories. This result obtained both in standard learning with feedback and in noncontingent, observational learning. These results show that rather than interfering with learning, categories with more dimensions cause individuals to learn more. The authors contrast the learning of family resemblance categories with learning in classical conditioning and probability learning paradigms, in which competition among features is well documented.

Concept Formation↗
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↗