A Tale of Two Processes: Categorization Accuracy and Attentional Learning Dissociate with Imperfect Feedback

A Tale of Two Processes: Categorization Accuracy and Attentional Learning Dissociate with Imperfect Feedback Caitlyn M. McColeman (caitlyn_mccoleman@sfu.ca) Department of Psychology, Simon Fraser University 8888 University Drive, Burnaby, BC V5A 1S6 Aaron J. Ancell (aaron_ancell@sfu.ca) Department of Philosophy, Simon Fraser University 8888 University Drive, Burnaby, BC V5A 1S6 Mark R. Blair (mark_blair@sfu.ca) Cognitive Science Program and Department of Psychology, Simon Fraser University 8888 University Drive, Burnaby, BC V5A 1S6 Abstract around us is a valid predictor of the state of the environment is an important skill. One way in which probabilistic environments are especially interesting is that they make it difficult for learners to determine which information is relevant. For example, in a classic study of learning from probabilistic cues, Castellan (1973) demonstrated that the mere presence of irrelevant cues tended to reduce the use of relevant ones. Further, he found that use of relevant cues was impacted most when they were moderately predictive. When the relevant cues were highly predictive, participants seldom used irrelevant information, and when cues were only slightly predictive, participants could not learn which which of them were predictive. Not surprisingly, the concept of attention is often invoked to account for data of this type, as in the case of Kruschke and Johanson’s (1999) Rapid Attention Shifts ‘N’ Learning (RASHNL) model of probabilistic cue learning. In RASHNL attention to cues shifts after the agent receives an error signal. The purpose of attentional shift is solely for error reduction. The model performs its task well, in that it successfully learns to classify categories in a probabilistic environment. However the literature is beginning to show that attentional shifts in probabilistic category learning serve purposes above and beyond error reduction. In a recent study, Little and Lewandowsky (2009) found that participants learning categories with probabilistic cues retained more information about an irrelevant cue than participants in a non-probabilistic condition. This suggests that attention to irrelevant cues increases in probabilistic environments, and that participants in such environments may allocate their attention for purposes other than reducing errors. It also suggests that people may attend to information that they do not use in their category judgments. Unfortunately, differentiating attention to a cue from cue use has been difficult. Often methods of gauging attention to a cue have equated attention with utilization through the use of transfer stimuli (e.g. Blair and Homa, 2005). While this method does provide a great deal of insight into learning, it is unable to tell us when participants are sampling information without actually The present study used eye-tracking to examine the relationship between attention and category learning in probabilistic environments. While training, participants received either perfect feedback (100% accurate), or one of three different levels of probabilistic feedback (87.5%, 75% or 62.5% accurate). It was found that participants in the 87.5% condition were more accurate than participants in the other two probabilistic feedback conditions. However, despite their greater accuracy, participants in the 87.5% condition continued to attend to irrelevant information as frequently as those in the other two probabilistic conditions. This shows that: (1) cues that are not utilized in making a categorization decision may still be frequently attended to, and (2) attentional learning is not as tightly coupled to improving accuracy as current formal models suggest. Keywords: Category Learning; Attention; Eye-Tracking; Categorization; Probability Learning. Introduction Although our actual learning environment is frequently probabilistic, most of what we know about human category learning comes from experiments where participants are provided with an ideal learning environment: both perfectly predictive cues and completely accurate feedback. While there is valuable information to be gained from such studies, it is also possible that they overlook interesting learning processes found only in more natural, probabilistic environments. In the real world, people must frequently assess the quality of the information they receive to guide their actions. Although the weather forecast might predict sunshine for the day a cloud formation over head may then further change our expectation: we may prepare for rain instead. In this probabilistic environment, the forecast is not a perfect predictor of outcome. Alternatively, the user of a damaged GPS system is unlikely to rely on the device for directions to an important meeting given that it is unreliable, while that same driver is very likely to let off the gas when the very reliable speedometer gets too far above the speed limit. Knowing what information