Rethinking the role of error in attentional learning

Rethinking the role of error in attentional learning Mark R. Blair (mark.blair@sfu.ca) R . Calen Walshe (calen.walshe@sfu.ca) Jordan I. Barnes (jordanb@sfu.ca) Lihan Chen (bill.lihan@gmail.com) Department of Psychology, Cognitive Science Program Simon Fraser University, 8888 University Drive Burnaby, BC, V5A 1S6 Canada Abstract Learning how to allocate attention properly is essential for success at many tasks. Extant theories of categorization assume that learning to allocate attention is an error-driven process, where shifts in attention are made to reduce error. The present work introduces a new measure, error bias, which compares the amount of attentional change in response to incorrect responses versus correct responses during category learning. We first confirm that prominent categorization models predict high amounts of error bias. We then test this prediction against human eye-tracking data from 384 participants. Across 7 of 8 data sets we find that participants show minimal or no error bias. This finding suggests that attentional learning mechanisms, as implemented in influential computational models, cannot be generalized to account for measures of overt attention. Keywords: Attention; Error; Eyetracking; Categorization; Eye-Movements; Optimization; Learning; Modeling Introduction Giraffes have long necks, helicopters have propellers on top, and wedding cakes are taller than birthday cakes. Learning these categories often involves learning to attend to such highly predictive features. This kind of selective use of information is present very early in human development. For example, infants focus mostly on the head to discriminate cats from dogs (Quinn, Doran, Reiss, & Hoffman, 2009), but they use legs and wheels when distinguishing animals from vehicles (Rakison & Butterworth, 1998). People also learn to change how they attend to stimuli with experience, and experts with years of training can develop the ability to use subtle but highly informative stimulus dimensions (Biederman & Shiffrar, 1987). Although the process by which people learn the right information to attend – what we shall call attentional learning – is a critical part of learning, from nascent stages to the highest levels of performance, its mechanisms are not well understood. Though overt attentional allocation can be studied directly and relatively accurately with modern eye- tracking, there is no existing theory that makes specific behavioural predictions about how the allocation of overt attention changes during learning. Our work is intended to be some early steps toward the goal of building such a theory. Although there is not an existing theory intended to account for attention at the level of eye-movements, the literature on category learning has theories which contain precise descriptions of attentional learning more generally (e.g., Kruschke, 1992). Researchers have created formal, computational models of how the effective allocation of attention is learned, and how it interacts with perception, memory and decision-making to improve categorization performance. In these computational theories, attention is characterized as a weight on each stimulus feature that is adjusted to reduce error, and more specifically, adjusted such that the proportion of change is relative to the proportion of error. In the present study, we use eye movements as an index of attention and compare those measures to the model equivalents. It is not always clear what attentional weights in models are supposed to correspond to in the real world. Attention is a complex series of independent means of biasing information processing. One very important source of such biases is the overt manipulation of sensory receptors, like eye movements and although these models were not intended to account for eye-movements directly, given that there are tight connections between covert and overt forms of attention, they are an excellent starting place (McPeek, Maljkovic, & Nakayama 1999). Indeed, it is clear from several recent eye-tracking studies (Blair, Watson, Walshe, & Maj, 2009; Rehder & Hoffman, 2005a, 2005b) that over the course of an experiment people get better at ignoring irrelevant information as they get better at categorizing, a finding in accord with existing error-driven accounts. There is some evidence, though, that error may not be the sole ingredient for attentional learning. Bott, Hoffman and Murphy (2007) have shown that participants attend to more dimensions than are strictly necessary to perform well. Blair, Watson, and Meier (2009) have shown that participants continue to optimize their attention, even after feedback is removed and participants have stopped making errors. Rehder and Hoffman (2005a, 2005b; Kim & Rehder, 2009) have found that reductions in the probability of fixating irrelevant information occur several trials after reductions in incorrect responding, rather than before as one might expect. While these studies are suggestive, they are not necessarily a requiem for error-driven accounts. In the formal theories discussed above, the error that motivates change is error internal to the model, not response error. The difference is subtle, but important. Imagine believing that hockey team A was only slightly better than