Eyetracking and selective attention in category learning

Abstract

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.

Introduction

Selective attention has played a prominent role in theories of categorization ever since Roger Shepard’s influential work (Shepard, Hovland & Jenkins, 1961) demonstrated that a simple stimulus generalization account of category learning is untenable. The stimulus generalization account took category learning to be a process of simple associations between stimuli and category labels. This account predicted that it should be easy for participants to associate stimuli that shared many features with one category label, and difficult to associate such stimuli with different labels. Unexpectedly, one important determiner of difficulty was the number of stimulus dimensions needed for correct classification. It has been generally accepted that this pattern of results is best understood in terms of learners optimally allocating their selective attention to those dimensions diagnostic of category membership (Medin and Schaffer, 1978, Nosofsky, 1984, Shepard et al., 1961).

Currently, selective attention is an integral component of all major categorization theories. For example, in both exemplar models (Hampton, 1995; Medin and Schaffer, 1978, Nosofsky, 1986) and prototype models (Nosofsky, 1992; Smith & Minda, 1998), selective attention is formalized in terms of the influence, or weight, that different stimulus dimensions have on a classification decision. Rule-based models also implicitly assume the operation of selective attention to those stimulus dimensions referred to by the current hypothesis (i.e., rule) being tested (Smith, Patalano, & Jonides, 1998).

Moreover, in more recent years, these theories have been extended to include the mechanisms by which selective attention changes with learning. One prominent example is Kruschke’s (1992) ALCOVE, a connectionist exemplar model that changes attention weights as a function of error feedback. Another is Nosofsky, Palmeri, and McKinley’s (1994) rule-plus-exception (RULEX) model, which first performs hypothesis (rule) testing on single dimensions, then on multi-dimensional rules and exceptions to those rules if needed.

Despite its prominence in modern categorization theory, however, evidence for the operation of selective attention has always amounted to demonstrations that dimensions vary in their influence on explicit categorization judgments (or same-different judgments, Goldstone, 1994), but not on the operation of selective attention per se (Lamberts, 1998). Accordingly, this study had two main goals. The first was to determine if eyetracking data would support the claim that learners allocate their attention to optimize classification performance. To this end, we replicated the Shepard et al. (1961) category learning experiment with an eyetracker. Specifically, we asked whether Shepard et al.’s claims regarding learners’ reallocation of attention to only those stimulus dimensions relevant to producing correct classification decisions would be directly corroborated by eyetracking data.

To our knowledge, the current work is the first to apply eyetracking to the domain of categorization research. At the outset then, one concern that must be addressed is the interpretation of eye movements as a surrogate measure of attention during category learning. It is of course well known that attention can dissociate from eye gaze under certain circumstances (Posner, 1980). However, in many cases changes in attention are immediately followed by the corresponding eye movements (e.g., Kowler, Anderson, Dosher, & Blaser, 1995), and there is evidence that attention and eye movements are tightly coupled for all but the simplest stimuli (Deubel & Schneider, 1996). Not surprisingly then, eye tracking has proven to be an effective tool in many areas of research, most notably of course reading (Ferreira and Clifton, 1986, Just and Carpenter, 1984, Makie et al., 2002, Rayner, 1998, Tanenhaus et al., 1995) but also language production (Griffin and Bock, 2000, Meyer et al., 1998), scene perception (Biederman et al., 1982, Henderson, 1999, Loftus and Mackworth, 1978), problem solving (Grant and Spivey, 2003, Hegarty and Just, 1993), skill acquisition (Haider & Frensch, 1999), and face perception (Althoff & Cohen, 1999), to name a few. In the current study, we will take the presence of eye fixations to spatially separated stimulus dimensions as a proxy measure of attention to those dimensions, and predict that fixations to dimensions irrelevant to correct classification will cease as a result of classification experience. An important feature of the category learning task is the availability of an overt behavioral measure (the elimination of classification errors) as a source of converging evidence about which aspects of stimuli are being attended. Specifically, learning entails that a participant attend to those stimulus dimensions needed to discriminate members of the categories. Thus, confirmation that learners primarily attend to relevant dimensions will not only corroborate the basic claim of Shepard et al.’s, it will also cross-validate the use of eyetracking as an index of attention in category learning.

The second goal of our study was to use eyetracking data to determine whether the manner in which attention changes during the course of learning was well described by ALCOVE, RULEX, or either model. Of course, these models were not specifically designed to account for eye movements. Nevertheless, eye movement predictions for each can be derived if we assume, on the basis of the research reviewed above, that the mapping between selective attention and eye movements is roughly one-to-one (an assumption we revisit later). For example, according to ALCOVE, learners will generally start off attending to all stimulus dimensions equally (or perhaps in a manner that reflects differences in their perceptual salience), and then gradually shift attention to only relevant dimensions as a result of error feedback. In the experiment which follows, dimensions will be of roughly equal salience, and thus the prediction we derive from ALCOVE is that learners will initially spend an equal amount of time fixating each stimulus dimension. As learning proceeds, fixations to irrelevant dimensions will gradually decrease until they are eliminated altogether.

In contrast, a hypothesis-testing model like RULEX makes very different predictions regarding how selective attention changes during learning. According to RULEX, learners first search for a single-dimension rule that successfully discriminates members of the two categories. Thus, our RULEX-derived prediction is that learners will fixate single dimensions early in learning. When no single-dimension rule is found, learners will fixate multiple dimensions as they attempt to form more complex rules (e.g., conjunctions, disjunctions, etc.), or to memorize exceptions to an imperfect rule. That is, whereas the ALCOVE-derived predictions are that learners will initially fixate all dimensions and then gradually reduce the number fixated to the minimum needed, the RULEX-derived predictions are that they will first fixate one dimension, and then increase the number fixated as needed.

Once again, an important characteristic of the category learning task is the presence of an overt behavioral measure in the form of classification errors that can corroborate any conclusions we reach regarding changes in selective attention on the basis of eye movements. For example, one diagnostic feature of hypothesis-testing models is the all-or-none learning (i.e., the sudden elimination of classification errors) that obtains when a learner discovers a correct single-dimension rule (Bower & Trabasso, 1963). Thus, the RULEX-derived prediction is that the fixations to a single dimension which are supposed to reflect rule application should be closely accompanied by the elimination of classification errors when that dimension is one which can be used to discriminate category members. Similarly, an important characteristic of associationist learning models like ALCOVE is the gradual learning that obtains as a result of the incremental adjustment of connection weights on the basis of error feedback.¹ Thus, the ALCOVE-derived prediction is that a gradual shift of eye movements should be accompanied by a gradual decrease of errors. More generally, a close correspondence between error reduction and changes in eye movements will not only provide evidence for one or the other model of learning, it would also validate eyetracking as an effective measure of the changes in selective attention during category learning.

Although we believe our predictions provide a useful initial framework for the evaluation of eye movements in category learning, we acknowledge at the outset that there are a number of reasons to expect something other than a simple one-to-one mapping between eye movements and the construct of “selective attention” as operationalized by categorization models. One reason of course is that eye fixations may be influenced by low-level perceptual characteristics of stimuli which do not necessarily have any bearing on how those items are classified. Another is that participants may attempt to learn more about the categories than just how to classify correctly (e.g., they might try to learn how to predict features given a category label rather than just vice versa). Because effects such as these are not part of category learning per se, they are beyond the purview of models such as ALCOVE or RULEX as currently formulated. Additionally, it is important to note that eye movements most directly measure a learner’s selective attention to spatial locations (on a computer screen), a construct which is theoretically distinct from their selective attention to stimulus dimensions (see, e.g., Logan, 2004, for a discussion). Nevertheless, the application of eyetracking to category learning is new, and thus we believe that for now our (perhaps overly simplistic) predictions provide a useful initial framework for the evaluation of eye movements in the Shepard et al. (1961) category structures. In the general discussion, we will reevaluate the relationship between eye movements and selective attention to stimulus dimensions in light of our experimental results.