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Searching for objects in scenes is a natural task for people and has been extensively studied by psychologists. In this paper we examine this task from a connectionist perspective. Computational complexity arguments suggest that parallel feed-forward networks cannot perform this task efficiently. One difficulty is that, in order to distinguish the target from distractors, a combination of features must be associated with a single object. Often called the binding problem, this requirement presents a serious hurdle for connectionist models of visual processing when multiple objects are present Psychophysical experiments suggest that people use covert visual attention to get around this problem. In this paper we describe a psychologically plausible system which uses a focus of attention mechanism to locate target objects. A strategy that combines top-down and bottom- up information is used to minimize search time. The behavior of the resulting system matches the reaction time behavior of people in several interesting tasks.

The findings of philosophers, linguists, and psychologists are conjoined here in an effort to develop a descriptive/analytic account of reasoning as it actually occurs in social settings. The primary focus of this paper is the reasoning that occurs in discussions of controversial social issues by groups of peers. Preliminary analysis indicates that rather sophisticated argument structures emerge from these informal settings, and that conversational interaction stimulates the development of arguments. Although participants did not always fully state their arguments, the investigators felt justified in attributing implicit premises because these were referred to later in the conversation as if they had been stated. In this respect at least, instead of merely assuming that subjects are good at reasoning, this study provides evidence for the claim. In this paper, a system for coding elements of reasoning and a method for displaying the interactive structure of reasoning in conversations are developed. A long-term goal is to use the information gained from this study to help understand and correct two problems that arise in the teaching of reasoning: the difficulty many students have with acquiring the principles of reasoning in standard logic courses and the difficulty of transferring whatever reasoning skills are acquired in the classroom to new situations.

Approach, Technologies and Goals This paper describes a set of educational tools designed to support central cognitive skills such as argument analysis and construction, cooperative negotation, collaborative writing and scientific inquiry. In building these tools we drew upon multi-media technologies, interactive video and object-oriented programming techniques. Our approach however, was not motivated by the technologies but rather by a desire to embed our educational objectives in situations which were intrinsically interesting to the students. This approach, alluded to in the title of the paper, came out of our intention to have the student feel good about her personal interests, and further to have her feel good about her intellectual competence as a vehicle for furthering her interests.

This paper discusses the problem of how to implement many-to-many, or multi-associative, mappings within connectionist models. Traditional symbolic approaches wield explicit representation of all alternatives via stored links, or implicitly through enumerative algorithms. Classical pattern association models ignore the issue of generating multiple outputs for a single input pattern, and while recent research on recurrent networks is promising, the field has not clearly focused upon multi-associativity as a goal. In this paper, we define multiassociative memory M M , and several possible variants, and discuss its utility in general cognitive modeling. W e extend sequential cascaded networks (Pollack 1987, 1990a) to fit the task, and perform several initial experiments which demonstrate the feasibility of the concept.

In earlier papers we presented a distributed model of action selection in an autonomous intelligent agent (Maes, 1989a, 1989b, 1991a, 1991b). An interesting feature of this algorithm is that it provides a handful of parameters that can be used to tune the action selection behavior of the algorithm. They make it possible, for example, to trade off goal-orientedness for data-orientedness, speed for quality, bias (inertia) for adaptivity, and so on. In this paper we report on an experiment we did in automating the tuning and run-time adaptation of these parameters. The same action selection model is used on a meta-level to select actions that alter the values of the parameters, so as to achieve the action selection behavior that is appropriate for the environment and task at hand.

While recent studies have demonstrated various ways that transfer might be achieved in a domain, the measures used to assess transfer rarely stray from lime and error data. This paper examines transfer in the complex skill of computer programming in order to explore more flexible and sensitive methods of assessing transfer. In the experiment, subjects wrote both a P A S C A L and a LISP version of two programming problems. Although a simple accuracy measure provides evidence for knowledge transfer between the two programming languages, measures based on analyses of the task domain (i.e., partialcredit accuracy, strategy use) provide much stronger evidence. Curiously, these measures target different subjects as exhibiting transfer, suggesting that more than one type of knowledge may be available for transfer.

We present a theoretical analysis of students' implicit understanding of the concept of variables and functions, and present a cognitive model of this understanding based on the idea that reasoning involves a successful interaction between psychological agents and the things and other people in a situation. In the first part of the paper, we provide evidence that middle-and high-school students demonstrate implicit understanding of functional relations among quantities when they reason about a physical model of linear functions. Implicit understanding is knowledge of concepts or principles that enables and constrains performance, but is not articulate. In the second part of the pa^r, we describe several theoretical properties of our computational model: a.) activities are modeled as interactions between a parson and a situation; b.) reasoning is modeled as a form of activity that produces new information; and c.) understanding is modeled as attunement to the constraints of conceptual activities.

This paper describes D^DALUS, a system that uses a variant of means-ends analysis to generate plans and uses an incremental learning algorithm to acquire probabilistic search heuristics from problem solutions. W e summarize DjEDALUS' approach to search, knowledge, organization, and learning, and examine its behavior on multi-column subtraction. W e then evaluate the system in terms of its consistency with known results on human problem solving, comparing it to other psychological models of learntng and planning.

In this paper, I combine the ideas of attention from cognitive psychology with concept formation in machine learning. M y claim is that the use of attention can lead to a more efficient learning system, without sacrificing accuracy. Attention leads to a savings in efficiency because it focuses only on the relevant attributes, retrieves less information from the environment, and is therefore less costly than a system that uses every piece of information available. I present a working dgorithm for attention, built onto the Classit concept formation system, and describe results from three domains.'

After many years of neglect, the topic of mental imagery has recently emerged as an active area of debate. One aspect of this ongoing debate is whether an image is represented as a description or a depiction of its components. This paper is not so concerned with how mental images are stored, as with what machine representations will provide a basis for imagery as a problem solving paradigm in artificial intelligence. In fact, we argue that a knowledge representation scheme that combines the ability to reason about both descriptions and depictions of images best facilitates the efficient implementation of the processes involved in imagery.

This paper explores the ways in which resource limitations influence the nature of perceptual and cognitive processes. A framework is developed that allows early visual processing to be analyzed in terms of these limitations. In this approach, there is no one "best" system for any visual process. Rather, a spectrum of systems exists, differing in the particular trade-offs made between performance and resource requirements.

This paper presents experimental evidence for a model of human language processing in which ambiguity resolution is delayed when there is a conflict between semantic contextual bias and the syntactically preferred interpretation. If there is no conflict, an immediate decision is made. Decision is not delayed indefinitely; the length of the delay is limited by available processing resources.

This paper describes a formal framework for perceptual categorization that can account for the salient qualitative predicates human observers are willing to ascribe to a closed class of objects, and consequently the simple groupings they can induce from small sets of examples. The framework hinges on the idea of a generative process that produces a given set of objects, expressed as a sequence of group-theoretic operations on a primitive element, thus ascribing algebraic structure to perceptual organization in a manner similar to Leyton (1984). Putatively, perceivers always seek to interpret any stimulus as a formally generic result of some sequence of operations; that is, they interpret each object as a typical product of some generative process. The principle formal structure is a "mode lattice," which a) exhaustively lists the qualitative shape predicates for the class of shapes, and b) defines the inferential preference hierarchy among them. The mechanics are worked out in detail for the class of triangles, for which the predicted qualitative features include such familiar geometric categories as "scalene," "isosceles," and "right," as well as more "perceptual" ones like "tall" and "short." Within the theory it is possible as well to define "legal" vs. "illegal" category contrasts; a number of examples suggest that our perceptual interpretations tend to regularize the latter to the former.

Most work in learnability theory assumes that both the environment (the data to be learned) and the learning mechanism are static. In the case of children, however, this is an unrealistic assumption. First-language learning occurs, for example, at precisely that point in time when children undergo significant developmental changes. In this paper I describe the results of simulations in which network models are unable to learn a complex grammar when both the network and the input remain unchanging. However, when either the input is presented incrementally, or—more realistically—the network begins with limited memory that gradually increases, the network is able to learn the grammar. Seen in this light, the early limitations in a learner may play both a positive and critical role, and make it possible to master a body of knowledge which could not be learned in the mature system.

OOPS is a reactive planner which integrates sensory perception and action selection. A principal feature of the OOPS architecture is the use and discovery of cheap, diagnostic features to indicate opportunities, which are then verified in more expensive computations. This diagnostic relationship is established analytically and refined using tools from decision theory. The use and refinement of diagnostic features depends upon assumptions of conditional independence. However, in the case of a multiplanning agent (one which simultaneously pursues several goals), while conditional independence holds true for features vis-avis individual opportunities, plans may interact in the world, and conditional independence may not hold. In this paper we discuss how the knowledge needed to avoid detrimental action interactions can be incorporated into OOPS's inexpensive diagnostic computations, with benefits for robustness, performance, and learning.

When one looks at transcripts of people answering questions or carrying on dialogues about everyday matters, their comments are filled with plausible inferences -- inferences that are not certain, but that make sense. Often, in forming these inferences, generalizations are made that are equally uncertain, but are nevertheless useful as a guide to their reasoning. This paper describes some extensions to our earlier description of a core theory of plausible reasoning (Collins and Michalski, 1989), based in large part on a recent protocol study. The primary focus is on the inductive inference patterns people use to form plausible generalizations, weakly held beliefs based on few examples. W e also show how the model was extended to deal with plausible inferences involving continuous quantities and inequalities.

We examine how acting in dynamic, complex, not entirely predictable environments affects the indexing, storage and reuieval of cases in a memory-based system. W e discuss how a hierarchical goal sUiicture can be exploited to provide indices for searching and storage when planning and acting in everyday environments under time pressure. The tradeoffs between the costs and utility associated with attempting to prevent repeating a failure or missing an opportunity are briefly examined. Considering these tradeoffs leads to distinguishing between when failures can be allowed to recur and when they should be anticipated and avoided. The amount of effort expended when handling failures differs for the two situations, but in both cases a hierarchical goal structure can be used to choose effective indices efficiently. This paper describes the approach taken in our EXPEDITER^ system and briefly compares it to other approaches.

This paper describes a model of belief systems called belief relativity (BR), which addresses the relationships and structure of knowledge held by multiple interacting agents. This paradigm uses belief reference frames (b-frames) as the main unit of belief spaces, within which an agent's beliefs are stored. B R is concerned with how beliefs are created and revised, how they influence each other within or between b-frames, and how one searches for b-frames that are useful (e.g., that remove contradictions). B R also deals with degrees of belief, propagated along the influences that relate beliefs and b-frames to each other. B R attempts to combine the best features of these idejis into a unified, synergistic framework.

This paper introduces Button Theory whose two principle goals are, first, to provide a taxonomy of the ways that students might usefully interact with and control a computerbased teacher, and second, to provide a natural mechanism by which they may exercise that control. W e have developed a small but comprehensive set of messages that students would find it useful to convey to a teacher during a tutorial interaction, and have associated each message with a button presented iconically on the computer screen W e describe our experience with the use of Button Theory in a prototype computer-based teaching system, and demonstrate how, even with rather simple mechanisms, this framework enables surprisingly rich interactions.

This paper presents a method, generalization to interval, that can encode images into symbolic expressions. This method generalizes over instances of spatial patterns, and outputs a constraint program that can be used declaratively as a learned concept about spatial patterns, and procedural as a method for reasoning about spatial relations. Thus our method transforms numeric spatial patterns to symbolic declarative/procedural representations. We have implemented generalization to interval with Acorn,^ a system that acquires knowledge about spatial relations by observing 2-D raster images. We have applied this system to some layout problems to demonstrate the ability of the system and the flexibility of constraint programs for knowledge representation.

The traditional approach to dynamic inferencing is to represent knowledge in a symbolic hierarchy, find the most specific information in the hierarchy that relates to the input, and apply the attached inferences. This approach provides for inheritance and parallel retrieval but at the expense of very complex learning and access mechanisms. Parallel Distributed Processing (PDP) systems have recently emerged as an alternative. PDP systems use a very simple processing mechanism, but can only eiccess high-level knowledge sequentially and require an enormous amount of training time. This paper presents Parallel Distributed Semantic (PDS) Networks, an approeich that integrates the best features of symbolic and PDP systems by storing the content of symbolic hierarchies in ensembles of P D P networks, connecting the networks in the manner of a semantic network, and using Propagation Filters to determine how information is passed between networks. Simulation results are presented which indicate that P D S Networks and Propagation Filters are able to perform pattern completion from partial input, generate dynamic inferences, and propagate role bindings.

This paper describes INC2, an incremental category formation system which implements the concepts of family resemblance, contrast-model-based similarity, and context-sensitive, distributed probabilistic representation. The system is evaluated in terms of both the structure of categories/hierarchies it generates and its categorization (prediction) accuracy in both noise-free and noisy domains. Performance is shown to be comparable to both humans and existing leaming-from-example systems, even though the system is not provided with any category membership information during the category formation stage.

Li Montreal, nurses respond to 9-1-1 emergency calls for medical help, backed up by physicians when needed. In this context, they have to make rapid decisions based on limited and sometimes unreliable information. The purpose of this study was to describe the decision-making processes used by nurses in telephone triage and to examine the relations among these processes in relation to nurses' characteristics and performance. The study was conducted in real emergency conditions. The sample included 34 nurses and 50 calls. Each call was transcribed and subjected to performance evaluation and content analysis. This paper focuses on the cognitive analyses of two protocols associated with different outcomes. The results show that nurses' decision-making in triage situations are often based on surface features (patterns of symptoms) rather than the underlying pathophysiology, particularly in high urgency cases. High performance was related to decisions based on the evaluation of the whole emergency situation. The contribution of training and the effects of experience on triage performance are discussed.

Distance estimation has been used extensively in the investigation of cognitive maps, yet it is not well understood as a cognitive process in its own right and, as a result, has been viewed as a simple read-out from a spatial representation. In contrast, this paper considers distance estimation to be a complex mental process in which heuristics guide the choice of strategies. Specifically, verbal protocols were collected on a distance estimation task for 20 undergraduates using a variety of city pairs in U.S. and Canada. On the basis of these data, distance estimation is shown to be a constructive process, using a relatively limited number of heuristics, such as addition, hedges and ratios. The choice of heuristics and the time to make a judgment are shown to be related to variables such as the familiarity of locations and the distance to be judged. The advantage of viewing distance estimation as a constructive process rather than a passive readout off an intemal map is argued.

Two experiments were conducted to investigate knowledge transfer from learned programming languages to learning new ones. The first experiment concerned transfer from knowing LISP to learning PROLOG; the results showed that subjects who knew LISP had significant advantages over subjects who did not. Moreover, among the subjects who knew LISP those who knew LISP better seemed to learn PROLOG faster. The second experiment studied transfer from knowing either PASCAL or PROLOG to learning LISP; attention was specifically focused on transfer of knowledge of writing recursive and iterative programs in these languages. The results indicated that PROLOG programmers, who were usually more knowledgeable on recursion, were more ready to learn the recursive part of the LISP language. Some general theoretical discussion about knowledge transfer among programming languages is also presented in the paper.

How do skilled map-readers use topographic maps to figure out where in the world they are? Our research addresses this question by studying the problem solving of experienced map-readers as they solve localization Where am I? - problems. Localization relies upon judgments of similarity and difference between the contour information of the map and the topographic information in the terrain. In this paper we discuss experiments that focus on how map-readers use attributes and structural relations to support judgments of similarity and difference. In our field and laboratory experiments, experienced map-readers implicitly define attributes to be detailed descriptors of individual topographic features. They use structural relations that link two or more topographic features as predicates. The time-course of their problem solving suggests that attributes and relations are psychologically distinct. Attributes like slope, e.g., "steep (hill)", support only initial judgments of difference. Relations like "(this hill) falls steeply down into (a valley)" are more powerful, supporting both judgments of difference and judgments of similarity. Judgments based on relations are used to test hypotheses about location. Experienced map readers exploit the distinction between attributes and relations as they solve localization problems efficiently.

In cooperative information-seeking environments, we have observed that the dialogues have the following characteristics: (1) they contain sufficient relevant information, (2) they are coherent, and (3) they are well-structured. In this paper, we describe a mechanism for plan inference which takes advantage of these observed features to reduce the number of alternate interpretations of a user's statements. This reduction is achieved as follows: initially, we take advantage of the relevant information trait by using guiding principles and meta predicates to constrain the number of possible interpretations of a single statement. Discourse coherence considerations are then applied to integrate subsequent statements and drop incoherent interpretations. The retained interpretations are evaluated using a measure of information content, which is used to prefer the interpretations that have more relevant information. The entire mechanism is based on an approach that takes advantage of the well-structured nature of information-seeking dialogues to arrive at the intended interpretation as efficiently as possible.

We present a process model of decision making mediated by goals and relationships. The model is implemented in the V O T E computer program which simulates Congressional roll call voting. In this paper, we focus on VOTE's decision strategies, which are based on the need not only to arrive at a vote, but also to produce an explanation for each decision. W e describe severed typical strategies, as well as an indirect strategy, Deeper Analysis, that is invoked when the normal strategies fail to arrive at a decision.

This paper presents the result that a computer program can mimic the acquisition by children of a sdected set of grammatical morphemes. Roger Brown [Brown, 1973] studied the acquisition of 14 morphemes, and showed how a set of partial order relations describes this aspect of child language learning. W e show that these relations can be given a computational basis. They follow directly from a class of Boolean learning algorithms which have three simple constraints in the manner in which they consider hypotheses. I will call these three constraints the C A M constraints. C A M constraint 1 is to increase the length of the conjuncts one term at a time. The second C A M constraint is to consider all hypotheses of the same length simultaneously. Finally, C A M constraint 3 is to collect all single-term hypotheses involving noun features into a single conjunction prior to Boolean learning.

This paper describes a model for reading instructions. The basic framework is that an agent engages in an activity and resorts to using instructions only when "all else fails". That is, by reading the instructions during the period of engagement, the meaning of the instructions can be clarified by feedback from the world. This model has been implemented in a computer program, IIMP. IIMP is the instruction reading component of FLOABN (Alterman et al., 1991), an integrated architecture whose domain is reasoning about the usage of mechanical and electronic devices.

In earlier work, we proposed a memory model that would facilitate the detection of opportunities to satisfy suspended goals. In this opportunistic memory model, suspended goals are indexed under feature sets that are predictive of the presence of the opportunity, and which are likely to be encountered in the normal course of future activity. The functional benefit of such encoding depends crucially on the particular vocabulary of features used, the costs of their detection, and the overlap of features relevant to the pursuit of different goals. In this paper we investigate the feature vocabulary implied by recent work on visual search [Treisman, 1985, Tsotsos, 1990], and its use in indexing goals suspended due to the lack of a particular object.

Over the past few years there has been a growing interest in the notion of using causal explanations in both learning ([Dejong and Mooney, 1986] [Mitchell et a/., 1986]) and planning ([Hammond, 1989], [Hammond, 1987] and [Simmonsand Davis, 1987]). The study of how complex causal explanations can be used in learning has turned into something of a cottage industry in AI; however, little attention has paid to how explanations may be constructed. In this paper, we will examine some of the current proposals concerning the process of explanation, augment them with a few ideas of our own, and suggest a new, more strategic level of knowledge about explanations that can be used to guide the explanation process. In particular, we are interested in the problems involved with integrating rule-based methods of explanation construction with memory-based approaches.

In connectionist networks, newly-learned information can completely destroy previouslylearned information unless the network is continually retrained on the old information. This behavior, known as catastrophic forgetting, is unacceptable both for practical purposes and as a model of mind. This paper advances the claim that catastrophic forgetting is a direct consequence of the overlap of the system's distributed representations and can be reduced by reducing this overlap. A simple algorithm is presented that allows a standard feedforward backpropagation network to develop semidistributed representations, thereby significantly reducing the problem of catastrophic forgetting.

A common feature of early speech is that children use case marking incorrectly. Several researchers have proposed that the child's mistakes are limited to the misuse of nominative case, and are corrected once the child acquires verbal morphology. In this paper I will show that this characterization of the problem is incorrect: children misuse all case forms, not just nominative case. In addition, I will show that the child's use of case is related to the acquisition of nominal morphology, not verbal. Case marking can be better understood as a result of the child learning the productive agreement processes of his language. This characterization accounts for the acquisition of case and the "waffling" which children exhibit, and does so within a unified theory of lexical and syntactic acquisition.

Intelligent agents interacting with their environments combine information from several sense modalities and indulge in tasks that have components of perception, reasoning, learning and planning. Traditional AI systems focus on a single component. This paper highlights the importance of the integrated perceive-reason-act-learn loop, and describes a system designed to capture this loop. As a first step, it learns about simple objects, their qualities, and the words that name and describe them. The visual-linguistic associations formed serve as a bias in acquiring further knowledge about actions, which in turn aids the system in satisfying its internal needs (e.g., hunger, thirst, sleep, curiosity). Learning mechanisms that extract, aggregate, generate, de-generate and generalize build a hierarchical network (that serves as internal models of the environment) with which the system perceives and reasons.

In this paper we report on the use of our operatorbased model of human covert visual attention [Wiesmeyer and Laird, 1990] to account for reaction times in counting tasks in which a stimulus is presented and left undisturbed until a response is made. Previous explanations have not employed &n attentionally-driven model. Our model, which is based on the Model Human Processor [Card et a/., 1983], is an early selection model in which an attentional "zoom lens" [Eriksen and Yeh, 1985] operates under the control of cognition in order to both locate features in visual space and improve the quality of featural information delivered to short-term memory by perception. W e have implemented our model and the control structures to simulate rapid counting tasks in the Soar cognitive architecture [Laird et al., 1987], which has been suggested as the basis for a unified theory of cognition [Newell, 1990]. Reaction times in the counting task are explained using operator traces that correspond to sequences of deliberate acts having durations in the 50 msec range.

Data from Neuropsychology have been widely used in order both to lest pre-existing cognitive theories and to develop new accounts. Indeed, several theorists have used dissociations, and in particular double dissociations, both in theory testing and in developing new theoretical accounts Double dissociations are indeed believed to be a key tool in revealing the gross structure or "modularity" of cognitive processes. In this paper, in the light of a case study in which a simple electrical system is systematically lesioned, we argue that double dissociation in an arbitrary modular system need not, and typically will not, reveal that modularity. These results suggest that the observation of a double dissociation implies little about the structure of the underlying system. W e finish arguing that the weakness of the methods described involves that neurobiological data have to be seriously taken into account in order to uncover the real structure of the cognitive system.

In this paper we examine the role played by working memory demands in determining problem difficulty during the solution of Tower of Hanoi Problem isomorphs. W e do so by describing a production system model that accounts for subjects' performance on these problems via a dynamic analysis of the memory k>ad imposed by the problem and of changes in that load during the problem solving episode. W e also present the results of detailed testing of the model against human subject data. The model uses a highly constrained working memory to account for a number of features of the problem solving behavior, including the dichotomous (exploratory and final path) nature of the problem solving, the relative difficulty of the problems, the particular moves made in each state of the problem space, and the temporal patterning of the final path moves.

This paper reports on the learning of spatial concepts in the £o project. The starting point Ls the identification of a visual primitive which appears to play a central role in the visually-based semantics for terms which express spatial relations between two objects. This primitive is simply the orientation of the imaginary ray connecting the two related objects where they are nearest each other. Given this, an important part of the learning consists of determining which other orientations this particular one should align with (e.g. it should align with upward vertical for "above"). These other orientations may be supplied by an objectcentered coordinate frame, as in English "in front of and Mixtec "cii", as well as by the upright coordinate frame. A central feature of the system design is the use of orientation-tuned Gaussian nodes which can learn their orientation and

The ambiguity inherent in natural language requires us to make many decisions about the meaning of what we hear or read. Yet most studies of natural language understanding have assumed that although language may be ambiguous, we always make the right choice when faced with a decision about ambiguity. Consequently, very little is said about how to recover from incorrect decisions. In this paper we look at two rare examples of investigations into recovering from erroneous decisions in resolving lexical ambiguity. After examining the corresponding theories, we find that what at first appear to be competing theories can in fact be resolved into a unified theory of lexical error recovery based upon a highly parallel architecture for language understanding.

Comparison is of two types, the implicit sort that is behind all categorization and the explicit sort by which two object representations are compared in short-term memory. Children learn early on both to categorize and to compare explicitly, but they only learn to use dimensions in these processes considerably later. In this paper we present a connectionist model which brings together categorization and comparison, focusing on the development of the use of dimensions. The model posits (1) a general comparison mechanism which is blind to the nature of its inputs and (2) the sharing of internal object and dimension representations by categorization and comparison processes. Trained on the two processes, the system learns to use dimension inputs as filters on its representations for objects; it is these filtered representations which are matched in comparison. The model provides an account of the tendency for early comparison along one dimension to be disrupted by similarities along other dimensions and of the process by which the child might overcome this deficiency.

In a previous study (Hickman & Laikin, 1990), we introduced a within-trial analogy mechanism called internal analogy that transfers both success and failure experiences between corresponding parts of the search tree for a single problem. In this paper, we describe powerful extensions to the learning procedure and their consequences on problem solving behavior First, we explain how our similarity metric can be naturally augmented to provide a more flexible partial match. To overcome the need for a static measure, however, we propose a mechanism that learns the appropriate level of partial match through feedback from previous analogical reasoning. Second, we show how this partial match mechanism controls the problem solver's search. Protocol dau from a subject working in a geometry theorem-proving domain provide support for the psychological fidelity of the extended internal analogy model.

This paper is part of an effort to extend research on mathematical problem solving beyond the traditional focus on formal procedures (both in the classroom and in problem solving research). W e are beginning to investigate students' inductive discovery-oriented strategies and the interaction between these and formal deductive strategies. In contrast to typical classroom problems in math and science which demand the application of a learned formal procedure (e.g., prove X), we gave students more open-ended problems (e.g., is X true?) for which the formal deductive procedure is useful, but other, possibly informal or inductive, strategies are also potentially useful. The normative approach for solving these problems, in fact, requires the use of both a deductive strategy, which is definitive only when X is true, and an inductive search for examples, which is definitive only when X is not universally true. When presented with these problems we found that geometry students have some limited facility to perform the deductive strategy (though, less so in this context than when they are directly asked to vwite a proof) and use a degenerate version of the inductive strategy. Instead of considering multiple examples and looking for a counter-example, students tend to read off the conclusion from the single example (or model) we provided.

It is widely held that there is a distinction between attentive and automatic cognitive processing. In research on attention using visual search tasks, the detection performance of human subjects in consistent mapping paradigms is generally regarded as indicating a shift, with practice, from serial, attentional, controlled processing to parallel, automatic processing, while detection performance in varied mapping paradigms is taken to indicate that processing remains under attentional control. This paper proposes a priority learning mechanism to model the effects of practice and the development of automaticity, in visual search tasks. A connectionist simulation model implements this learning algorithm. Five prominent features of visual search practice effects are simulated. These are: 1) in consistent mapping tasks, practice reduces processing time, particularly the slope of reaction times as a ftinction of the number of comparisons; 2) in varied mapping tasks, there is no change in the slope of the reaction time function; 3) both the consistent and varied effects can occur concurrently; 4) reversing the target and distractor sets produces strong interference effects; and 5) the benefits of practice are a function of the degree of consistency.

Spatial relations and spatial language form an important part of everyday reasoning. This paper describes SPATR, a system which addresses the labeUing of components of objects and the interpretation of spatial relations between objects within the framework of adaptive planning. S P A T R implements a model of spatial reasoning, which mediates among language, memory, and perception. Using a case-based approach for reasoning from past experience, S P A T R makes use of spatial relationships corresponding to closed class terms, as well as a 3D, hierarchical representation of objects for retrieving relevant past experience.

Neuropsychological evidence has proved influential both in testing pre-existing cognitive theories and in developing new accounts. It has been argued that dissociations, and, in particular, double dissociation are particularly valuable in developing new theoretical accounts, since they may reveal the gross structure or "modularity" of cognitive processes. In this paper, we show that even fully distributed systems -i.e. systems with no modularity can give rise to double dissociations. W e give the example of a recurrent neural network which draws loops and spirals which shows a double dissociation between the two tasks when lesioned. This result suggests that the observation of a double dissociation implies little about the modularity of the underlying system. In the final section we argue that a dual task technique can give additional hints about the structure of the underlying system because the class of distributed systems we describe are not able, in general, to perform two tasks at the same time. Finally, we argue that neurobiology has to be taken into account in order to interpret purely behavioral data.

Any single algorithm for abduction requires specific kinds of knowledge and ignores other kinds of knowledge. A knowledge- based system that uses a single abductive method, is restricted to using the knowledge required by that method. This makes the system britde, because the single Fixed method can only respond appropriately in a limited range of situations and can only make use of a subset of the potentially relevant knowledge. In this paper, we describe a framework from which abductive strategies can be opportunistically constructed to reflect the problem being solved and the knowledge available to solve the problem. W e also describe ABDSoar, a Soar-based implementation

Macro-operators and chunks have long been used to model the acquisition and refinement of procedural knowledge. However, it is clear that human learners use more sophisticated techniques to encode more powerful operators than simple linear macro-operators: specifically, linear macro-operators cannot represent arbitrary repetitions of operators. This paper presents a process-model for the acquisition of iterative macrooperators, which are an efficient representation of repeating operators. W e show that inducing iterative macro-operators from empirical problem-solving traces provides dramatically better efficiency results than simple linear macro-operators. This domain-independent learning mechanism is integrated into the FERMI problemsolver, giving more evidence that humans have a similar learning capability.

This paper investigates the process of hypothesis generation and the coordination of hypothesis and evidence in medical diagnostic tasks. Two issues are addressed: the generation of hypothesis and the directionality of reasoning. Two problems whose initial presentation suggested an initial hypothesis were presented to subjects with different degrees of expertise in clinical medicine. When faced with contradictory evidence against the initial hypothesis, 1) early novices either modified the initial hypothesis, or ignored, or reinterpreted the cues in the problem to fit the hypothesis; 2) intermediate novices generated concurrent hypotheses to account for different sets of data; and 3) advanced novices generated several initial hypotheses and subsequently narrowed the hypothesis space by generating a single coherent diagnostic hypothesis. All subjects, used a mixture of forward reasoning and backward reasoning. A more forward-directed reasoning was related to diagnostic accuracy. These results on diagnostic reasoning are discussed in relation to findings on scientific reasoning.

With the recent ability to construct fault tolerant computer models using connectionist approaches, researchers are n o w able to investigate the effects of damage to these models. This has great appeal for cognitive science as it provides a further way to verify or falsify a computer model. Existing studies employ a concept of network "lesioning" that fails to have explanatory adequacy for neurobiology. While using anatomically plausible architectures for cognitive models, they nonetheless use biologically implausible methods for simulating neurological damage to these networks. This paper examines the different objects of computational networks and their analogical neurobiological counterparts, and suggests a taxonomy of connectionist network lesion methods. Finally, an existing visual system model is used as a testbed to study the differential effects of focal and diffuse lesions. Tlie exj)erimcnts with focal damage versus diffuse damage suggest that while the effects of focal brain injury m a y be due to the particular computations performed in some brain area, the effects of diffuse brain injury or degeneration may cause cognitive defici ts because of the inherent nature of the brain as a distributed computational device, and not through differential local effects