This article presents a comprehensive survey of research concerning interactions between associative learning and attention in humans. Four main findings are described. First, attention is biased toward stimuli that predict their consequences reliably (). This finding is consistent with the approach taken by Mackintosh (1975) in his attentional model of associative learning in nonhuman animals. Second, the strength of this attentional bias is modulated by the value of the outcome (). That is, predictors of high-value outcomes receive especially high levels of attention. Third, the related but opposing idea that may result in increased attention to stimuli (Pearce & Hall, 1980), receives less support. This suggests that hybrid models of associative learning, incorporating the mechanisms of both the Mackintosh and Pearce-Hall theories, may not be required to explain data from human participants. Rather, a simpler model, in which attention to stimuli is determined by how strongly they are associated with significant outcomes, goes a long way to account for the data on human attentional learning. The last main finding, and an exciting area for future research and theorizing, is that and modulate both deliberate attentional focus, and more automatic attentional capture. The automatic influence of learning on attention does not appear to fit the traditional view of attention as being either or. Rather, it suggests a new kind of “derived” attention.

Abstract& Prediction error (''surprise'') affects the rate of learning: We learn more rapidly about cues for which we initially make incorrect predictions than cues for which our initial predictions are correct. The current studies employ electrophysiological measures to reveal early attentional differentiation of events that differ in their previous involvement in errors of predictive judgment. Error-related events attract more attention, as evidenced by features of event-related scalp potentials previously implicated in selective visual attention (selection negativity, augmented anterior N1). The earliest differences detected occurred around 120 msec after stimulus onset, and distributed source localization (LORETA) indicated that the inferior temporal regions were one source of the earliest differences. In addition, stimuli associated with the production of prediction errors show higher dwell times in an eyetracking procedure. Our data support the view that early attentional processes play a role in human associative learning. &

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link: AbstractCategorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of divergent, non-complementary models with little consensus on the relative adequacy of these accounts.Progress on assessing relative adequacy of formal categorization models against these criteria has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered, and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).Keywords: categorization; cluster; exemplar; model selection; modeling; prototype. EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 2The study of categorization is a fascinating endeavor. The process of constructing and using categories underpins our capacity to encode and apply information in the world in an efficient and competent manner but also, ultimately, our ability to think in terms of abstract kind of categorization models should we aim to develop? The lack of consensus regarding such key issues has resulted in categorization research being carried out in increasingly independent strands and this has been inhibiting overall progress in the field. Nosofsky, Gluck, Palmeri, McKinley and Glauthier (1994) wrote, "Recent years have seen an avalanche of newly proposed models of category learning and representation. As such models grow increasingly more sophisticated, there is a need to develop increasingly more rigorous testing grounds so that one may choose among them" (p. 352). Almost 20 years later, progress towards this goal remains limited.In the current article, we first provide a definition of the term formal model, consider the principal advantages of formal modeling over other forms of theorizing, briefly summarize some of the leading formal models of categorization, and assess progress to date on the empirical evaluation and comparison of these models. We then set out the approaches EMPIRICAL EVALUATION OF CATEGORIZATION MODELS 3 we believe are most likely to lead to progress in the future. We organize our conclusions in terms of a set of criteria for assessing the relative adequacy of models, and a list of dependent and independent variables that any adequate formal model of categorization should be expected to...

It has been demonstrated that when people free classify stimuli presented simultaneously in an array, they have a preference to categorize by a single dimension. However, when people are encouraged to categorize items sequentially, they sort by "family resemblance," grouping by overall similarity. The present studies extended this research, producing 3 main findings. First, the sequential procedure introduced by G. Regehr and L. R. Brooks (1995) does not always produce a preference for family resemblance sorts. Second, sort strategy in a sequential procedure is sensitive to subtle variations in stimulus properties. Third, spatially separable stimuli evoked more family resemblance sons than stimuli of greater spatial integration. It is suggested that the family resemblance sorting observed is due to an analytic strategy.

The processes of overall similarity sorting were investigated in 5 free classification experiments. Experiments 1 and 2 demonstrated that increasing time pressure can reduce the likelihood of overall similarity categorization. Experiment 3 showed that a concurrent load also reduced overall similarity sorting. These findings suggest that overall similarity sorting can be a time-consuming analytic process. Such results appear contrary to the idea that overall similarity is a nonanalytic process (e.g., T. B. Ward, 1983) but are in line with F. N. Milton and A. J. Wills's (2004) dimensional summation hypothesis and with the stochastic sampling assumptions of the extended generalized context model (K. Lamberts, 2000). Experiments 4 and 5 demonstrated that the relationship between stimulus presentation time and overall similarity sorting is nonmonotonic, and the shape of the function is consistent with the idea that the three aforementioned processes operate over different parts of the time course.

We report the first electrophysiological investigation of the inverse base-rate effect (IBRE), a robust non-rational bias in predictive learning. In the IBRE, participants learn that one pair of symptoms (AB) predicts a frequently occurring disease, whilst an overlapping pair of symptoms (AC) predicts a rarely occurring disease. Participants subsequently infer that BC predicts the rare disease, a non-rational decision made in opposition to the underlying base rates of the two diseases. Error-driven attention theories of learning state that the IBRE occurs because C attracts more attention than B. On the basis of this account we predicted and observed the occurrence of brain potentials associated with visual attention: a posterior Selection Negativity, and a concurrent anterior Selection Positivity, for C vs. B in a post-training test phase. Error-driven attention theories further predict no Selection Negativity, Selection Positivity or IBRE, for control symptoms matched on frequency to B and C, but for which there was no shared symptom (A) during training. These predictions were also confirmed, and this confirmation discounts alternative explanations of the IBRE based on the relative novelty of B and C. Further, we observed higher response accuracy for B alone than for C alone; this dissociation of response accuracy (B>C) from attentional allocation (C>B) discounts the possibility that the observed attentional difference was caused by the difference in response accuracy.

Our thanks go to Angus B. Inkster for his help coding the verbal reports.TRAINING TYPE AND CATEGORIZATION. AbstractThe effects of two different types of training on rule-based and information-integration category learning were investigated in two experiments. In observational training, a category label is presented, followed by an example of that category and the participant's response. In feedback training, the stimulus is presented, the participant assigns it to a category and then receives feedback about the accuracy of that decision. Ashby, Maddox, and Bohil (2002) reported that feedback training was superior to observational training when learning information-integration category structures, but that training type had little effect on the acquisition of rule-based category structures. These results were argued to support the COVIS dual-process account of category learning. However, a number of non-essential differences between their rule-based and information-integration conditions complicate interpretation of these findings. Experiment 1 controlled, between category structures, for participant error rates, category separation, and the number of stimulus dimensions relevant to the categorization. Under these more controlled conditions, rule-based and information-integration category structures both benefitted from feedback training to a similar degree. Experiment 2 maintained this difference in training type when learning a rule-based category that had otherwise been matched, in terms of category overlap and overall performance, with the rule-based categories used in Ashby et al.These results indicate that differences in dimensionality between the category structures in Ashby et al. is a more likely explanation for the interaction between training type and category structure than the dual-system explanation they offered.KEYWORDS: COVIS, categorization, implicit, explicit, feedback.TRAINING TYPE AND CATEGORIZATION. 3Ashby and Maddox (2011) stated that many researchers now assume multiple systems are involved in category learning. To the extent that this claim is accurate, it is down in no small part to the behavioral dissociations reported by Ashby, Maddox and colleagues. These studies tend to find a differential effect of a manipulation on the learning of two types of category structure: rule-based and information-integration. argue that these dissociations are predicted by one particular dual-system model of category learning, COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, Turken, & Waldron, 1998;Ashby, Paul, & Maddox, 2011), which assumes the existence of two competing systems of category learning. The strength of the case for COVIS is, of course, not a function of the number of dissociations that have been reported, but rather of the number that prove to be reliable and valid. Indeed, there is a growing body of work that casts doubt on the validity or interpretation of a high proportion of these dissociations (e.g. Dunn, Newell, & Kalish, 2012;Newell, Dunn, & Kalish, 2010;...

The effect of concurrent load on generalization performance in human contingency learning was examined in 2 experiments that employed the combined positive and negative patterning procedure of Shanks and Darby (1998). In Experiment 1, we tested 32 undergraduates and found that participants who were trained and tested under full attention showed generalization consistent with the application of an opposites rule (i.e., single cues signal the opposite outcome to their compound), whereas participants trained and tested under a concurrent cognitive load showed generalization consistent with surface similarity. In Experiment 2, we replicated the effect with 148 undergraduates and provided evidence that it was the presence of concurrent load during training, rather than during testing, that was critical. Implications for associative, inferential, and dual-process accounts of human learning are discussed.