We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model’s complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.

Computational models have emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. In this paper we investigate the advantages of developing rigorous computational models of driver behavior in cognitive architectures — computational frameworks with underlying psychological theories that incorporate basic properties and limitations of the human system. In particular, we describe an integrated driver model developed in the ACT-R cognitive architecture and demonstrate how this model accounts for the steering profiles, lateral-position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. The model has implications both for theoretical accounts of complex dynamic tasks in the context of cognitive architectures and for practical applications in predicting and recognizing driver behavior and distraction.

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.

This article presents five principles of learning, derived from cognitive theory and supported by empirical results in cognitive psychology. To bridge the gap between theory and practice, each of these principles is transformed into a practical guideline and exemplified in a real teaching context. It is argued that this approach of putting cognitive theory into practice can offer several benefits to statistics education: a means for explaining and understanding why reform efforts work; a set of guidelines that can help instructors make well-informed design decisions when implementing these reforms; and a framework for generating new and effective instructional innovations

A model of memory retrieval is described. The model embodies 4 main claims: (a) temporal memory— traces of items are represented in memory partly in terms of their temporal distance from the present; (b) scale-similarity—similar mechanisms govern retrieval from memory over many different timescales; (c) local distinctiveness—performance on a range of memory tasks is determined by interference from near psychological neighbors; and (d) interference-based forgetting—all memory loss is due to interference and not trace decay. The model is applied to data on free recall and serial recall. The account emphasizes qualitative similarity in the retrieval principles involved in memory performance at all timescales, contrary to models that emphasize distinctions between short-term and long-term memory.

Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.

This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects’ incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.

studies have directly examined whether order of study itself influences retrieval efficacy. In contrast, many dozens of studies have examined this question in paired-associate learning, asking whether memory for simple pairs exhibits a forward asymmetry effect (i.e., better forward recall than backward recall). Surprisingly, such asymmetries are exceedingly hard to detect in pairedassociate tasks, with many studies producing nearly identical levels of forward and backward recall (see Ekstrand, The authors acknowledge support from National Institutes of Health grant MH55687. We are grateful to Kelly Addis for assisting in data collection and for helpful discussions on the analyses of Experiment 2. We also thank Marc Howard, Franklin Zaromb and Nelson Cowan for their helpful comments on an earlier version of this manuscript. Correspondence concerning this article should be addressed to Michael Kahana, Volen National Center for Complex Systems, MS 013, Brandeis University, Waltha

this article should be addressed to Shane T. Mueller (smueller@umich.edu) or David E. Meyer (demeyer@umich.edu), Cognition and Perception Program, Dept. of Psychology, University of Michigan, 525 East University, Ann Arbor, MI, 48109-1109, USA

The rich empirical puzzle of the Stroop effect has traditionally been approached with narrowly focused and somewhat atheoretical models. A recent exception is a simulation model based on the WEAVER++ language theory. The present model, WACT, combines components of WEAVER++ with the memory and control processes of the ACT-R cognitive theory. WACT accounts for the time course of inhibition from incongruent word distractors, facilitation from congruent word distractors, the lack of effect of color distractors, and the semantic gradient in inhibition. WACT goes beyond WEAVER++ to account for Stroop performance errors as well as latencies, and its implementation in a unified cognitive theory opens doors to broader coverage of Stroop phenomena than standalone models are likely to attain. Documented and executable code for WACT is available for inspection and comment at www.msu.edu/~ema/stroop.