Humans have an extremely flexible ability to categorize regularities in their environment, in part because of attentional systems that allow them to focus on important perceptual information. In formal theories of categorization, attention is typically modeled with weights that selectively bias the processing of stimulus features. These theories make differing predictions about the degree of flexibility with which attention can be deployed in response to stimulus properties. Results from 2 eye-tracking studies show that humans can rapidly learn to differently allocate attention to members of different categories. These results provide the first unequivocal demonstration of stimulus-responsive attention in a categorization task. Furthermore, the authors found clear temporal patterns in the shifting of attention within trials that follow from the informativeness of particular stimulus features. These data provide new insights into the attention processes involved in categorization.

Three experiments explored the learning of categories where the training instances either repeated in each training block or appeared only once during the entire learning phase, followed by a classification transfer (Experiment 1) or a recognition transfer test (Experiments 2 and 3). Subjects received training instances from either two (Experiment 2) or three categories (Experiments 1-3) for either 15 or 20 training blocks. The results showed substantial learning in each experiment, with the notable result that learning was not slowed in the non-repeating condition in any of the three experiments. Furthermore, subsequent transfer was marginally better in the non-repeating condition. The recognition results showed that subjects in the repeat condition had substantial memory for the training instances, whereas subjects in the non-repeat condition had no measurable memory for the training instances, as measured either by hit and false-alarm rates or by signal detectability measures. These outcomes are consistent with prototype models of category learning, at least when patterns never repeat in learning, and place severe constraints on exemplar views that posit transfer mechanisms to stored individual traces. A formal model, which incorporates changing similarity relationships during learning, was shown to explain the major results.

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Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertise only yields deep insight into development where differences are important throughout skill acquisition. This reliance may be pernicious where the predictive importance of variables is not constant across levels of expertise. Before the development of sophisticated machine learning tools for data mining larger samples, and indeed, before such samples were available, it was difficult to test the implicit assumption of static variable importance in expertise development. To investigate if this reliance may have imposed critical restrictions on the understanding of complex skill development, we adopted an alternative method, the online acquisition of telemetry data from a common daily activity for many: video gaming. Using measures of cognitive-motor, attentional, and perceptual processing extracted from game data from 3360 Real-Time Strategy players at 7 different levels of expertise, we identified 12 variables relevant to expertise. We show that the static variable importance assumption is false - the predictive importance of these variables shifted as the levels of expertise increased - and, at least in our dataset, that a contrastive approach would have been misleading. The finding that variable importance is not static across levels of expertise suggests that large, diverse datasets of sustained cognitive-motor performance are crucial for an understanding of expertise in real-world contexts. We also identify plausible cognitive markers of expertise.

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