this article. This paper has also benefited greatly from constructive feedback from Gordon Logan, Mike Stadler, and our other reviewers. We thank Joanie Sanchez for her assistance in running Experiment 1. This research was supported by a Social Science Faculty Research Award from Yale University. Portions of this research were presented at the Annual Meeting of the Association for Research in Ophthalmology and Vision, Fort Lauderdale, FL, in May, 1997, and at the Annual Meeting of the Psychonomic Society, Philadelphia, PA, in November, 1997

This article presents a theory that inte~ates space-based and object-based approaches to visual attention. The theory puts together M. P. van Oeffelen and P. G. Vos's ( 1982, 1983) COntour DEtector (CODE) theory of perceptual grouping by proximity with C. Bundesen's (1990) theory of visual attention (TVA). CODE provides input to TVA, accounting for spatially based between-object selection, and TVA converts the input to output, accounting for feature- and category-based withinobject selection. CODE clusters nearby items into perceptual groups that are both perceptual objects and regions of space, thereby integrating object-based and space-based approaches to attention. The combined theory provides a quantitative account of the effects of grouping by proximity and dis~nce between items on reaction time and accuracy data in 7 empirical situations that shaped the current literature on visual spatial attention. For the last decade the attention literature has been embroiled in a debate over the nature of visual spatial attention that focuses on the "thing " that attention selects (e.g., Baylis &

Two connectionist models of attention in associative learning, previously used to model human category learning, are shown to have special cases that are essentially equivalent to N. J. Mackintosh's (1975, Psychological Review, 82, 276 298) classic model of attention in animal learning. The models unify formulas for associative weight change with formulas for attentional change, under a common goal of error reduction. Error-driven attentional shifting accelerates learning of new associations but also protects previously learned associations from retroactive interference. The models are fit to data from a recent experiment in human associative learning (J. K. Kruschke 6 N. J. Blair, 2000, Psychonomic Bulletin 6 Review, 7, 636 645), which shows that blocking of learning involves learned inattention. The approach also provides a novel and unifying theory of latent inhibition (the preexposure effect) in terms of blocking. The discussion summarizes how the approach accounts for a variety of other ``irrational' ' phenomena in associative learning, including base rate effects, perseveration of attention through relevance

Over the decades, computational models of human cognition have advanced from programs that produce output similar to that of human problem solvers to systems that mimic both the products and processes of human performance. In this paper, we describe a model that achieves the next step in this progression: predicting individual participants' performance across multiple tasks after estimating a single individual difference parameter. We demonstrate this capability in the context of a model of working memory, where the individual difference parameter for each participant represents working memory capacity. Specifically, our model is able to make zero-parameter predictions of individual participants' performance on a second task after separately fitting performance on a preliminary task. We argue that this level of predictive ability offers an important test of the theory underlying our model.

predictions of exemplar models and that supported prototype models. In the authors ’ view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs. deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al.’s experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs. A classic issue in cognitive psychology concerns the manner in which people represent categories in memory. According to prototype models (Homa, 1984; Posner & Keele, 1968; Reed, 1972), people represent categories by forming a summary representation that is a central tendency of all of the experienced members of a

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision-making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, adapt their judgment strategies from heuristic-based to instance-based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT’s learning mechanisms have been implemented in an ACT-R cognitive model. Through a series of experiments, this paper shows how the IBLT’s learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments.

When subjects switch between a pair of stimulus–response tasks, reaction time is slower on trial N if a different task was performed on trial N � 1. We present a parallel distributed processing (PDP) model that simulates this effect when subjects switch between word reading and color naming in response to Stroop stimuli. Reaction time on ‘‘switch trials’ ’ can be slowed by an extended response selection process which results from (a) persisting, inappropriate states of activation and inhibition of task-controlling representations; and (b) associative learning, which allows stimuli to evoke tasks sets with which they have recently been associated (as proposed by Allport & Wylie, 2000). The model provides a good fit to a large body of empirical data, including findings which have been seen as problematic for this explanation of switch costs, and shows similar behavior when the parameters are set to random values, supporting Allport and Wylie’s proposal. © 2001 Elsevier Science Key Words: task switching; task set; Stroop effect; parallel distributed processing; executive functions. Atkinson and Shiffrin (1968) proposed a distinction between relatively permanent cognitive structures, such as short- and long-term memory, and control processes which harness those fixed structures in order to attain specific goals. This distinction was elaborated in the following years (e.g.,

this article may be addressed to either Michael Kahana or Robert Sekuler, Volen National Center for Complex Systems, MS 013, Brandeis University, Waltham, MA 02254-9110. E-mail may be sent to kahana @brandeis.edu or sekuler@brandeis.edu plex multidimensional stimulus spaces (Nosofsky, 1992; Maddox & Ashby, 1996; Ashby & Perrin, 1988), with decision rules that can predict performance in a variety of classification paradigms (Nosofsky & Palmeri, 1998; Nosofsky & Alfonso-Reese, 1999; Maddox & Ashby, 1996). Although models of classification and models of visual discrimination share many assumptions about stimulus representation and subjects' decision rules, models of classification have been primarily developed to explain subjects' classification of combinations of simple geometric forms, whereas models of discrimination have been developed to explain subjects ' discrimination of elemental visual stimuli, including sinusoidal luminance gratings. Because such stimuli can be combined to synthesize more complex images such as textures and natural scenes, they represent a natural test-bed for assessing theories' power and generalizability

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Working memory resources are needed for processing and maintenance of information during cognitive tasks. Many models have been developed to capture the effects of limited working memory resources on performance. However, most of these models do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. We propose a computational model that accounts for differences in working memory capacity in terms of a quantity called source activation, which is used to maintain goal-relevant information in an available state. We apply this model to capture the working memory effects of individual subjects at a fine level of detail across two experiments. This, we argue, strengthens the interpretation of source activation as working memory capacity. 2001 Cognitive Science Society, Inc. All rights reserved.