This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Cleeremans, Destrebecqz, &

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The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rulebased reasoning and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modelling.

The assessment of whether a statement is consistent with what has gone before is ubiquitous in discourse comprehension. One theory of the process is that individuals search for a mental model of a situation in which all the statements in the discourse are true. In the case of spatial descriptions, individuals should prefer to construct models that retain the information in the description. Hence, they should use strategies that retain information in an efficient way. If the descriptions are consistent with multiple models then they are likely to run into difficulties. We report two experiments in which the participants judged the consistency of spatial descriptions. The participants made more errors when later assertions in the description conflicted with the preferred models of earlier assertions. The results shed light on the

Proactive information sharing is a challenging issue faced by intelligence agencies in effectively making critical decisions under time pressure in areas related to homeland security. Motivated by psychological studies on human teams, a team-oriented agent architecture -- CAST (Collaborative Agents for Simulating Teamwork), was implemented to allow agents in a team to anticipate the information needs of teammates and help them with their information needs proactively, effectively, and timely. In this paper, we extend CAST with a decision-making module . Through two sets of experiments in a simulated battlefield, we evaluate the effectiveness of the decision theoretic proactive communication strategy in improving team performance, and the effectiveness of information fusion as an approach to alleviating the information overload problem faced by distributed decision makers.

Relational knowledge is a hallmark of human cognition and the subject of a vast body of research. In this paper we argue that existing accounts of relations are inadequate because they have little to say abouthowrelations arise in the rst place and because they tend to be limited to particular sorts of relational tasks. We present a new approach to the learning and representation of relations, an approach that makes use of what we call micro-relation units (MRUs). Each MRU represents a relation between features of di erent objects rather than between objects themselves. We show howthis approach o ers an account of the grounding of relations, and we describe a neural-network implementation of the MRU framework and show howit enables a variety of relational tasks to be performed by the same system.

Up until now there has been no agreement on what a mental model of a physical system is and how to infer the mental model a person has. This paper describes research aimed at solving these problems by proposing that a Mental Model is a dynamic representation created in WM by combining information stored in LTM (the Conceptual Model of the system) and characteristics extracted from the environment. Three experiments tested hypotheses derived from this proposal. Implications for research on Mental Model are discussed. Mental Models and Working Memory 3 3 The role of Working Memory on measuring Mental Models of physical systems When a person learns to interact with a system it means she/he acquires knowledge about its operation and about the structural relationships between its components. Researchers have called this knowledge the 'Mental Model' of the system (Moran, 1981). The existence of Mental Models, and their importance during the interaction with the system, has been demonstr...

Inferences about spatial, temporal, and other relations are ubiquitous. This article presents a novel model-based theory of such reasoning. The theory depends on 5 principles. (a) The structure of mental models is iconic as far as possible. (b) The logical consequences of relations emerge from models constructed from the meanings of the relations and from knowledge. (c) Individuals tend to construct only a single, typical model. (d) They spontaneously develop their own strategies for relational reasoning. (e) Regardless of strategy, the difficulty of an inference depends on the process of integration of the information from separate premises, the number of entities that have to be integrated to form a model, and the depth of the relation. The article describes computer implementations of the theory and presents experimental results corroborating its main principles.

Standard feedforward and recurrent networks cannot support strong systematicity when constituents are presented as local input/output vectors (Phillips, 1998). To explain systematicity connectionists must either: (1) develop alternative models; or (2) justify the assumption of similar (non-local) constituent representations prior to the learning task. I show that the second commonly presumed option cannot account for systematicity, in general. This option, termed first-order connectionism, relies upon established spatial relationships between common-class constituents to account for systematic generalization: Inferences (functions) learned over, e.g., cats extend systematically to dogs by virtue of both being nouns with similar internal representations so that the function learned to make inferences employing one simultaneously has the capacity to make inferences employing the other. But, humans generalize beyond common-class constituents. Cross-category generalization (e.g., inferences that require treating mango as a colour, rather than a fruit) makes having had the necessary common context to learn similar constituent representations highly unlikely. At best, the constituent similarity proposal encodes for one binary relationship between any two constituents, at any one time. It cannot account for inferences, such as transverse patterning that require identifying and applying one of many possible binary constituent relationships that is contingent on a third constituent (i.e., ternary relationship). Connectionists are, therefore, left with the first option which amounts to developing models with the symbol-like capacity to explicitly represent constituent relations independent of constituent contents, such as in tensor-related models. However, rather just simply impl...

Two studies investigated the relationship between working memory capacity (WMC), adult age, and the resolution of conflict between familiarity and recollection in short-term recognition tasks. Experiment 1 showed a specific deficit of young adults with low WMC in rejecting intrusion probes (i.e., highly familiar probes) in a modified Sternberg task, which was similar to the deficit found in old adults in a parallel experiment (K. Oberauer, 2001). Experiment 2 generalized these results to 3 recognition paradigms (modified Sternberg, local recognition, and n back tasks). Old adults showed disproportional performance deficits on intrusion probes only in terms of reaction times, whereas young adults with low WMC showed them only in terms of errors. The generality of the effect across paradigms is more compatible with a deficit in content–context bindings subserving recollection than with a deficit in inhibition of irrelevant information in working memory. Structural equation models showed that WMC is related to the efficiency of recollection but not of familiarity.