This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.

... paradigms of serial recall, recognition memory, free recall, and implicit memory. List memory performance in ACT-R is determined by the level of activation of declarative chunks which encode that items occur in the list. This level of activation is in turn determined by amount of rehearsal, delay, and associative fan from a list node. This theory accounts for accuracy and latency profiles in backward and forward serial recall, set size effects in the Sternberg paradigm, length–strength effects in recognition memory, the Tulving–Wiseman function, serial position, length and practice effects in free recall, and lexical priming in implicit memory paradigms. This wide variety of effects is predicted with minimal parameter variation. It is argued that the strength of the ACT-R theory is that it offers a completely specified processing architecture that serves to integrate many existing models in the literature.

This article proposes a theoretical framework for external representation based problem solving. The Tic-Tac-Toe and its isomorphs are used to illustrate the procedures of the framework as a methodology and test the predictions of the framework as a functional model. Experimental results show that the behavior in the Tic-Tac-Toe is determined by the directly available information in external and internal representations in terms of perceptual and cognitive biases, regardless of whether the biases are consistent with, inconsistent with, or irrelevant to the task. It is shown that external representations are not merely inputs and stimuli to the internal mind and that they have much more important functions than mere memory aids. A representational determinism is suggested--the form of a representation determines

Systematic errors in performance are an important aspect of human behavior that have not received adequate explanation. One such systematic error is termed post-completion error; a typical example is leaving one’s card in the automatic teller after withdrawing cash. This type of error seems to occur when people have an extra step to perform in a procedure after the main goal has been satisfied. The fact that people frequently make this type of error, but do not make this error every time, may best be explained by considering the working memory load at the time the step is to be performed: the error is made when the load on working memory is high, but will not be made when the load is low. A model of performance in the task was constructed using Just and Carpenter’s (1992) CAPS that predicted that high working memory load should be associated with post-completion errors. Two experiments confirmed that such errors can be produced in a laboratory as well as a naturalistic setting, and that the conditions under which the CAPS model makes the error are consistent with the conditions under which the errors occur in

We propose a novel approach to intelligent tutoring in which feedback messages are associated with constraints on correct problem solution. The knowledge state of the student is represented by the constraints that he or she does and does not violate during problem solving. Constraint-based tutoring has been implemented in SQL-Tutor, an intelligent tutoring system for teaching the database query language SQL. Empirical evaluation shows that (a) students find the system easy to use, and (b) they do better on a subsequent classroom examination than peers without experience with the system. Furthermore, learning curves are smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the constraint construct.

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In this paper we review the literature relating to the psychological/educational study of programming. We identify general trends comparing novice and expert programmers, programming knowledge and strategies, program generation and comprehension, and objectoriented versus procedural programming. (We do not cover research relating specifically to other programming styles.) The main focus of the review is on novice programming and topics relating to novice teaching and learning. Various problems experienced by novices are identified, including issues relating to basic program design, to algorithmic complexity in certain language features, to the ‘‘fragility’ ’ of novice knowledge, and so on. We summarise this material and suggest some practical implications for teachers. We suggest that a key issue that emerges is the distinction between effective and ineffective novices. What characterises effective novices? Is it possible to identify the specific deficits of ineffective novices and help them to become effective learners of programming? 1.

Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

Many intellectual disciplines contribute to the field of cognitive science: psychology, linguistics, anthropology, and artificial intelligence, to name just a few. Cognitive science itself

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