How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched. Prologue "How much do we know at any time? Much more, or so I believe, than we know we know!" —Agatha Christie, The Moving Finger A typical American seventh grader knows the meaning of

A theory of analogical mapping between source and target analogs based upon Interacting structural, semantic, and pragmatic constraints is proposed here. The structural constraint of isomorphism encourages mappings that maximize the consistency of relational corresondences between the elements of the two analogs. The constraint of semantic similarity supports mapping hypotheses to the degree that mapped predicates have similar meanings. The constraint of prog-mafic central/! / favors mappings involving elements the analogist believes to be Important in order to achieve the purpose for which the analogy Is being used. The theory is implemented in a computer program called ACME (Analogical Constraint Mapping Engine), which represents constraints by means of a network of supporting and competing hypotheses regarding what elements to map. A coop-erative algorithm for parallel constraint satisfaction identifies mapping hypotheses that collectively represent the overall mapping that best fits the interacting constraints. ACME has been applied to a wide range of examples that include problem analogies, analogical arguments, explanatory analogies, story analogies, formal analogies, and metaphors. ACME is sensitive to semantic and pragmatic Information if it Is available,.and yet able to compute mappings between formally Isomorphic analogs without any similar or identical elements. The theory Is able to account for empirical findings regarding the impact of consistency and similarity on human processing of analogies.

Abstract: Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the results about the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuron-like elements represent a large body of systematic knowledge and perform a range of inferences with such speed? We describe a computational model that is a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables, and perform a class of inferences in a few hundred msec. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model achieves this by i) representing dynamic bindings as the synchronous firing of appropriate nodes, ii) rules as interconnection patterns

Latent Semantic Analysis is used as a technique for measuring the coherence of texts. By comparing the vectors for two adjoining segments of text in a highdimensional semantic space, the method provides a characterization of the degree of semantic relatedness between the segments. We illustrate the approach for predicting coherence through re-analyzing sets of texts from two studies that manipulated the coherence of texts and assessed readers' comprehension. The results indicate that the method is able to predict the effect of text coherence on comprehension and is more effective than simple term-term overlap measures. In this manner, LSA can be applied as an automated method that produces coherence predictions similar to propositional modeling. We describe additional studies investigating the application of LSA to analyzing discourse structure and examine the potential of LSA as a psychological model of coherence effects in text comprehension.

We describe a computational model of how analogs are retrieved from memory using simultaneous satisfaction of a set of semantic, structural, and pragmatic constraints. The model is based on psychological evidence suggesting that human memory retrieval tends to favor analogs that have several kinds of correspondences with the structure that prompts retrieval: semantic similarity, isomorphism, and pragmatic relevance. We describe ARCS, a program that demonstrates how these constraints can be used to select relevant analogs by forming a network of hypotheses and attempting to satisfy the constraints simultaneously. ARCS has been tested on several data bases that display both its psychological plausibility and computational power.

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This article describes learning with media as a complementary process within which representations are constructed and procedures performed, sometimes by the learner and sometimes by the medium. It reviews research on learning with books, television, computers, and multimedia environments. These media are distinguished by cognitively relevant characteristics of their technologies, symbol systems, and processing capabilities. Studies are examined that illustrate how these characteristics, and instructional designs that employ them, interact with learner and task characteristics to influence the structure of mental representations and cognitive processes. Of specific interest is the effect of media characteristics on the structure, formation, and modification of mental models. Implications for research and practice are discussed Do media influence learning? The research reviewed in this article suggests that capabilities of a particular medium, in conjunction with methods that take advantage of

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

model meaning as the relations among abstract symbols that are arbitrarily related to what they signify. These symbols are ungrounded in that they are not tied to perceptual experience or action. Because the symbols are ungrounded, they cannot, in principle, capture the meaning of novel situations. In contrast, participants in three experiments found it trivially easy to discriminate between descriptions of sensible novel situations (e.g., using a newspaper to protect one’s face from the wind) and nonsense novel situations (e.g., using a matchbook to protect one’s face from the wind). These results support the Indexical Hypothesis that the meaning of a sentence is constructed by (a) indexing words and phrases to real objects or perceptual, analog symbols; (b) deriving affordances from the objects and symbols; and (c) meshing the affordances under the guidance of syntax. © 2000 Academic Press Key Words: meaning; language; embodiment; computational models; Latent Semantic Analysis; Hyperspace Analogue to Language. Meaning is the most important problem in cognitive psychology. Meaning controls memory and perception. Meaning is the goal of communication. Meaning underlies social activities and culture: To a great degree, what distinguishes human cultures are the meanings they give to natural phenomena, artifacts, and human relations. Yet, rather than being a hotbed of theoretical and empirical investigation, meaning in cognitive psychology has been coopted by a particular approach: Meaning arises

We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? The connectionist model Shruti attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds. Relational structures (frames, schemas) are represented in Shruti by clusters of cells, and inference in Shruti corresponds to a transient propagation of rhythmic activity over such cell-clusters wherein dynamic bindings are represented by the synchronous firing of appropriate cells. Shruti encodes mappings across relational structures using high-efficacy links that enable the propagation of rhythmic activity, and it encodes items in long-term memory as coincidence and conincidence-error detector circuits that become active in response to the occurrence (or non-occurrence) of appropriate coincidences in the on going flux of rhythmic activity.