Knowledge effect the selective attention in category learning: An eyetracking study

Knowledge Effect and Selective Attention in Category Learning: An Eyetracking Study ShinWoo Kim (shinwoo.kim@nyu.edu) Bob Rehder (bob.rehder@nyu.edu) Department of Psychology, New York University 6 Washington Place, New York, NY 10003 USA Abstract Two experiments tested the effect of prior knowledge on attention allocation in category learning. Using eyetracking, we found that (a) knowledge affects dimensional attention allocation, with knowledge-relevant features being fixated more often than irrelevant ones, (b) this effect was not due to initial attention bias to the relevant dimensions but rather gradually emerged in response to observing category members, and (c) the effect grew even after the last error trial, that is, in the absence of error. These results pose challenge to current models of knowledge-based category learning. Because of the importance of categories for human cognition, the manner in which people learn categories has received intensive study. Among many procedures, supervised classification learning has been popular, and a number of basic facts have been established. One of these concerns the role of selective attention in category learning. Selective attention has played a prominent role in theories of category learning since the finding that learning difficulty correlated with the number of diagnostic dimensions needed for classification (Shepard, Hovland, & Jenkins, 1961). In both exemplar models and prototype models, selective attention is explicitly formalized in terms of the weights of dimensions on classification (e.g., Kruschke, 1993; Nosofsky, 1992). Rule-based models also assume selective attention to the dimensions referred to by the current hypothesis (Smith, Patalano, & Jonides, 1998). A second finding concerns the role of the prior knowledge. Although early research focused on the effect of empirical information, subsequent research has shown that theoretical knowledge influences every type of category-based judgment that has been tested, including learning, induction, and categorization (Murphy, 2002). For example, subjects usually learn categories far faster when a category's diagnostic features can be related to a common theme, and also show better learning of those related features versus unrelated ones (Murphy & Allopenna, 1994). Although attention and knowledge each has been shown to be important, little is understood on how knowledge affects attention in category learning. This question is important because any theory that accounts for knowledge- based category learning (e.g., Heit & Bott, 2000; Rehder & Murphy, 2003) is incomplete in the absence of attention mechanism. However, this omission is understandable because virtually nothing is known about how knowledge affects attention: modeling attentional effects is impossible if there is no data to model. Knowledge Effect on Selective Attention Using eyetracking, we addressed three open questions regarding how knowledge affects dimensional attention during category learning. The first concerns whether in fact prior knowledge affects dimensional attention. If a subset of category features is knowledge-related, it is likely that such knowledge directs attention to the related features and away from unrelated ones (Kaplan & Murphy, 2000). However, knowledge might instead change how features are processed and encoded without changing what is attended. For example, knowledge might allow knowledge-related features to be associated more strongly with its underlying representation. Thus, our first goal is to determine whether knowledge indeed induces any change to what is attended. Assuming that it does, the second question concerns the time course of that effect. Some theorists have suggested that one role of knowledge is to preselect dimensions for further testing. For example, Pazzani's (1991) rule-based PostHoc model selectively attends to goal-relevant features and thus predicts preselection of those dimensions. Kruschke (1993) suggested that his associative ALCOVE model can account for knowledge by setting initial attention weights on the related dimensions higher than on others. However, knowledge effect on attention might emerge gradually in response to observing category members. Because prior knowledge consists of representations in the long-term memory, multiple exemplars may need to be observed for it to become sufficiently active. Or, a learner may only begin to make use of knowledge when a simpler strategy yields error signals. Thus, our second goal concerns whether preference for attending to related dimensions emerges gradually with experience of category members. Assuming that it does, the third question concerns whether error feedback is required to mediate change in attention. One possibility is that (even) greater attention to related features occurs in the absence of error, because merely observing related features might be sufficient to further activate prior knowledge. However, supervised learning is often characterized as learners' adapting their responses to error feedback so as to minimize error (Kruschke, 2001). It is assumed that doing so requires adjusting attention to more diagnostic dimensions. This error-driven account might be extended for knowledge- based category learning, for example, error feedback might serve as cue indicating to the learner to use prior knowledge (and attention might shift as a result). Thus, our third