Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (Kruschke, 1990a, 1990b, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Experiment 1, which partially replicated and extended the probabilistic classification learning paradigm of Gluck and Bower (1988a), demonstrated the importance of an error-driven learning rule. Experiment 2, which extended the classification learning paradigm of Medin and Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments. One of the major current models for explaining performance in arbitrary category learning paradigms is the context model proposed by Medin and Schaffer (1978) and elaborated by Estes (1986a) and Nosofsky (1984, 1986). According to the context model, people represent categories by storing individual exemplars in memory and make classification decisions on the basis of similarity comparisons with the stored exemplars. The context model has proved to be successful at predicting quantitative details of classification performance in a wide variety of experimental settings and has compared favorably with a variety of alternative models, including prototype, independent-feature, and certain logical-rule based models (see Medin & Florian, in press, and Nosofsky, in press-a, in press-b, for reviews). However, some shortcomings of the context model were recently demonstrated in series of probabilistic classification learning experiments conducted by Gluck and Bower (1988a)

Problem solvers often have multiple operators available to them but must select just one to apply. We present three experiments that demonstrate that solvers use at least two sources of information to make operator selections in the building sticks task (BST): information from their past history of using the operators and information from the current context of the problem. Specifically, problem solvers are more likely to use an operator the more successful it has been in the past and the closer it takes the current state to the goal state. These two effects, respectively, represent the learning and performance processes that influence solvers ’ operator selections. A computational model of BST problem solving, developed within the ACT-R theory (Anderson, 1993), provides the unifying framework in which both types of processes can be integrated to predict solvers ’ selection tendencies. � 1996 Academic Press, Inc. Most problems can be approached in multiple ways but solved by only a few. Problem solving can be viewed, then, as finding one of the few paths that leads from a problem’s initial state to its goal state through some space of possible intermediate states (Newell & Simon, 1972). In this framework,

Abstract not found

Abstract not found

this paper and the writing of the paper were supported, in part, by grants to Dominic W. Massaro from the Public Health Service (PHS R01 NS 20314), the National Science Foundation (BNS 8812728), a James McKeen Cattell Fellowship, and the graduate division of the University of California, Santa Cruz. Cohen & Massaro On the Similarity of Categorization Models 2

P against several alternative models such as a weighted averaging model (WTAV), which is an inefficient algorithm for combining the auditory and visual sources. For (2) The WTAV predicts that two sources can never be more informative than one. In direct contrasts, the FLMP has consistently and significantly outperformed the WTAV w a w v w w wa w v i j ( ) ( ) . / / da = + + = + - a v ( )( ) . / / da = + - - 1 1 1 Copyright 2001 Psychonomic Society, Inc. The research was supported by grants from the National Institute of Deafness and Other Communicative Disorders (PHS R01DC00236), the National Science Foundation (Challenge Grant CDA-9726363), Intel Corporation,and the University of California Digital Media Innovation Program. D.W.M. is highly appreciative of the encouraging support of Dan Friedman and Bill Rowe. We thank William Batchelder, James Cutting, In Jae Myung,Mark Pitt, and John Wixted for their constructive comments on an earlier version of the paper. Correspon

Abstract not found

The integration of information from different sensors, cues, or modalities lies at the very heart of perception. We are studying adaptive phenomena in visual cue integration. To this end, we have designed a visual tracking task, where subjects track a target object among distractors and try to identify the target after an occlusion. Objects are defined by three different attributes (color, shape, size) which change randomly within a single trial. When the attributes differ in their reliability (two change frequently, one is stable), our results show that subjects dynamically adapt their processing. The results are consistent with the hypothesis that subjects rapidly re-weight the information provided by the different cues by emphasizing the information from the stable cue. This effect seems to be automatic, ie not requiring subjects' awareness of the differential reliabilities of the cues. The hypothesized re-weighting seems to take place in about 1 s. Our results suggest that cue integration can exhibit adaptive phenomena on a very fast time scale. We propose a probabilistic model with temporal dynamics that accounts for the observed effect.

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, "rational", model to its, "non-rational", predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.

One of the implicit assumptions of multi-modal interfaces is that human-computer interaction is significantly facilitated by providing multiple input and output modalities. Surprisingly, however, there is very little theoretical and empirical research testing this assumption in terms of the presentation of multimodal displays to the user. The goal of this paper is provide both a theoretical and empirical framework for addressing this important issue. Two contrasting models of human information processing are formulated and contrasted in experimental tests. According to integration models, multiple sensory influences are continuously combined during categorization, leading to perceptual experience and action. The Fuzzy Logical Model of Perception (FLMP) assumes that processing occurs in three successive but overlapping stages: evaluation, integration, and decision (Massaro, 1998). According to nonintegration models, any perceptual experience and action results from only a single sensory influence. These models are tested in expanded factorial designs in which two input modalities are varied independently of one another in a factorial design and each modality is also presented alone. Results from a variety of experiments on speech, emotion, and gesture support the predictions of the FLMP. Baldi, an embodied conversational agent, is described and implications for applications of multimodal interfaces are discussed.