Explicit Reasoning Research Articles

Pertinence Generation in Radiological Diagnosis: Spreading Activation and the Nature of Expertise

An empirical study of human expert reasoning processes is presented. Its purpose is to test a model of how a human expert's cognitive system learns to detect, and does detect, pertinent data and hypotheses. This process is called pertinence generation. The model is based on the phenomenon of spreading activation within semantic networks. Twenty‐two radiologists were asked to produce diagnoses from two very difficult X‐ray films. As the model predicted, pertinence increased with experience and with semantic network integration. However, the experts whose daily work involved explicit reasoning were able, in addition, to go beyond and to generate more pertinence. The results suggest that two qualitatively different kinds of expertise, basic and super, should be distinguished. A reinterpretation of the results of Lesgold et al. (1988) is proposed, suggesting that apparent nonmonotonicities in performance are not representative of common radiological expertise acquisition but result from the inclusion of basic and super expertise on the same curve.

Teaching for Understanding: A Study of Students' Preinstruction Theories of Matter and a Comparison of the Effectiveness of Two Approaches to Teaching About Matter and Density

Thirty 8th-grade students were given an interview and a written test before and after a 10-week curriculum unit concerning matter, mass, volume, and density. The instruments probed qualitative understandings of matter; ability to differentiate weight and density using qualitative reasoning; formal, quantitative understandings of mass, weight, volume, and density; and ability to integrate both qualitative and quantitative reasoning about density. In Part 1 of the study, we examined the organization of student ideas prior to instruction. We found evidence to support our idea that students' qualitative conceptions of matter and density were organized in commonsense theories of matter that constrained their understanding of density: Students who believed that all material objects have weight, no matter how small or light the object, were much more likely to have made a beginning differentiation between weight and density than those who did not. We also showed that a qualitative understanding of density emerged prior to a formal, quantitative understanding of density, although most students were able to engage in explicit proportional reasoning about another, more familiar quantity (i.e., sweetness). In Part 2, we compared the effectiveness of two teaching approaches. One approach to teaching about matter and density-the standard Introductory Physical Science (IPS) curriculum-emphasized formal definitions, measurement, and explicit quantitative reasoning. Because it asked students to work formally with complex concepts before assuring that relevant qualitative understanding was in place, we designed a modified IPS curriculum that addressed this problem by encouraging students to make their initial assumptions about matter explicit and open to debate. The modified curriculum also used visual models and qualitative reasoning to help students bridge the gap between their starting conceptions and formal, quantitative definitions. We found that both curricula were effective in promoting a good quantitative understanding of mass, volume, and density. The modified cumculum, however, was more effective at restructuring students' qualitative conceptions and at promoting an integrated understanding of density. Thus, we conclude that science curricula should integrate both qualitative and quantitative reasoning to be effective at promoting conceptual change.

The Negative Information Problem in Mechanical Diagnostics

Condition-based maintenance (CBM) is an emerging technology, which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drivetrains, and industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors, including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real-time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes that might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models, which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by their absence are indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.

Relational Reasoning about Contexts

The syntactic nature of operational reasoning requires techniques to deal with term contexts, especially for reasoning about recursion. In this paper we study applicative bisimulation and a variant of Sands’ improvement theory for a small call-by-value functional language. We explore an indirect, relational approach for reasoning about contexts. It is inspired by Howe’s precise method for proving congruence of simulation orderings and by Pitts’ extension thereof for proving applicative bisimulation up to context. We illustrate this approach with proofs of the unwinding theorem and syntactic continuity and, more importantly, we establish analogues of Sangiorgi’s bisimulation up to context for applicative bisimulation and for improvement. Using these powerful bisimulation up to context techniques, we give concise operational proofs of recursion induction, the improvement theorem, and syntactic minimal invariance. Previous operational proofs of these results involve complex, explicit reasoning about contexts.

Hands up all those in favour of ethics

A recent media controversy is used to help discriminate between immediate and often impulsive reactions to ethical problems and those of a more critical or evaluative nature. Decision-making models are reviewed as a way of considering responses more carefully and using more explicit reasoning. The Canadian Code of Ethics for Psychologists is outlined and compared to that of the Psychological Society ofIreland. The former offers a clear and practical structure, even including guidelines for those situations when ethical principles are in conflict. A worked through example is reproduced to give a sense of how this code can operate in practice. It is argued that this style of code is useful for practice, for teaching ethics, as well as assisting in the adjudication of complaints against psychologists.

Animated human agents with motion planning capability for 3D‐space postural goals

AbstractIn this paper, we present a rule‐based heuristic method of motion planning for an animated human agent with massively redundant degrees of freedom. It constructs motion plans to achieve 3D‐space goals of control points on the body, e.g. a hand, while avoiding collisions. Like the artificial potential field approach, the method performs motion decisions in 3D world space rather than in joint space. To handle the massively redundant degrees of freedom, we use a qualitative kinematic model, which specifies motions of body parts and dependencies among them, without specifying the exact distance parameters. This model helps the body select appropriate primitive motions for given goals of control points more globally than does the gradient vector of an artificial potential field of the body. The method simulates (in imagination) the suggested plan to find whether some body parts hit objects, and how much they penetrate the objects. Based on this simulated collision information, the method suggests intermediate goals of the collision body parts. A subplan to achieve these intermediate goals is again postulated by using the qualitative kinematic model. This explicit reasoning helps alleviate the basic cause of local minima in the potential field approach, namely, conflicts between attractive potential fields due to goal positions of control points and repulsive potential fields due to obstacles.

Guiding conceptual design through behavioral reasoning

This paper presents a model for conceptual design based on an explicit behavioral reasoning step to guide the design process. Rather than mapping directly from function to form, we treat conceptual design as a two-step process, first transforming functional requirements to a behavioral description and then matching physical artifacts to this behavior. We believe that behavior, in terms of physical principles and phenomena, provides a natural bridge between functional requirements and physical artifacts. Behavioral reasoning breaks preconceived links between functions and artifacts, allowing for innovative solutions to be found. A new representation calledbehavior graphs (derived from bond graphs) has been developed to facilitate behavioral reasoning. This paper discusses behavior graphs and their use in a design synthesis model that generates systems of pre-defined embodiments (e.g., motor, spring, valve) to meet functional requirements given in terms of input and output parameters (e.g., force, pressure, displacement, voltage). An experimental computer program implementing this model is discussed and illustrative examples presented.

The computational account of belief

Fodor and others who think that scientific, computational psychology will vindicate commonsense belief-desire psychology have maintained that belief can be identified with the explicit storage of a token with appropriate content. I review and develop problems for the explicit storage view and show that a more plausible account identifies belief with the disposition to use a token with appropriate content in explicit reasoning and planning processes and as a basis for action. I argue that this type of inner disposition account will also apply to most other common sense attitudes. The result is a realism about commonsense belief-desire psychology that is more modest than Fodor's: While such inner dispositions probably do exist, these states will probably not be the main focus of scientific psychological theories.

Reasoning with Bounded Rationality

Oaksford and Chater (1992) have criticized an earlier discussion of theories of human reasoning (Evans, 1991) for omission of the concept of `bounded rationality'. They argue that all existing theories of deductive competence would become computationally intractable when applied to complex real-world problems. In reply, it is accepted that we cannot use explicit deductive reasoning based on either inference rules or mental models in the face of complex problems. However, it is suggested first of all that we do engage in explicit reasoning with relatively simple but important real-world problems, and that the mental models theory provides a plausible account of how this is achieved. Complex real-world problem see are knowledge-based and are thus probably dealt with by the induction of schemas or by connectionist mechanisms which we know to be very efficient in the human brain. Thus much real-world reasoning will be implicit and inductive, rather than explicit and deductive.

A general possible worlds framework for reasoning about knowledge and belief

In this paper non-normal worlds semantics is presented as a basic, general, and unifying approach to epistemic logic. The semantical framework of non-normal worlds is compared to the model theories of several logics for knowledge and belief that were recently developed in Artificial Intelligence (AI). It is shown that every model for implicit and explicit belief (Levesque), for awareness, general awareness, and local reasoning (Fagin and Halpern), and for awareness and principles (van der Hoek and Meyer) induces a non-normal worlds model validating precisely the same formulas (of the language in question).

Automatic program debugging for intelligent tutoring systems

Program debugging is an important part of the domain expertise required for intelligent tutoring systems that teach programming languages. This article explores the process by which student programs can be automatically debugged in order to increase the instructional capabilities of these systems. The research presented provides a methodology and implementation for the diagnosis and correction of nontrivial recursive programs. In this approach, recursive programs are debugged by repairing induction proofs in the Boyer‐Moore logic. The induction proofs constructed and debugged assert the computational équivalence of student programs to correct exemplar solutions. Exemplar solutions not only specify correct implementations but also provide correct code to replace buggy student code. Bugs in student code are repaired with heuristics that attempt to minimize the scope of repair.The automated debugging of student code is greatly complicated by the tremendous variability that arises in student solutions to nontrivial tasks. This variability can be coped with, and debugging performance improved, by explicit reasoning about computational semantics during the debugging process. This article supports these claims by discussing the design, implementation, and evaluation of Talus, an automatic debugger for LISP programs, and by examining related work in automated program debugging.Talus relies on its abilities to reason about computational semantics to perform algorithm recognition, infer code teleology, and to automatically detect and correct nonsyntactic errors in student programs written in a restricted, but nontrivial, subset of LISP. Solutions can vary significantly in algorithm, functional decomposition, role of variables, data flow, control flow, values returned by functions, LISP primitives used, and identifiers used. Solutions can consist of multiple functions, each containing multiple bugs. Empiricial evaluation demonstrates that Talus achieves high performance in debugging widely varying student solutions to challenging tasks.

The Critical Incident Method, An Overlooked Way of Human Relations Training for Small Business

The paper presents an outline of Pigor's critical incident method for human relations training. The method parallels a real life process of decision making consisting of searching for information, formulating the problem, and deciding upon a course of action based upon explicit reasoning. The method also provides for making generalizations that will be useful in dealing with or preventing future human relations situations. It is pointed out that the critical incident method has advantages for small firms beyond those it shares with large firms. These consist of the special appropriateness for small management groups, the low cost because training sessions can be handled by members of the small organization, the relevance of the cases discussed which come from members of the organization, the flexibility of the format of the method, and finally the number of human relations goals that can be simultaneously developed by those in the same training session.

A Handbook of Human Performance

A ten-step approach for developing a comprehensive but concise, design-oriented handbook of human performance is proposed, with emphasis on the first two steps. The ten steps are: 1. Identify and define classes and subclasses of human performance. 2. Develop a concise format for abstracting information for each lowest level subclass. 3. Establish the need for a handbook. 4. Prepare an abstract for each literature source covering a lowest level subclass. 5. Develop a concise format, including design recommendations, for summarizing each lowest level subclass. 6. Prepare a summary, including design recommendations, for each lowest level subclass. 7. Collate summaries and abstracts. 8. Prepare table of contents and index. 9. Publish handbook. 10. Distribute handbook. For Step 1, a functional rather than traditional approach is presented. Classes of human performance are identified and defined in terms of basic functions suitable for all applications. Five major functions are identified, subdivided and defined. The five major functions are: R. Receiving information: Receiving all information, except receiving communicated information, which is a subclass of communicating, the second major function. Receiving information includes perception of all natural-environmental and artificially-displayed information (other than perception of directly - or indirectly - communicated information), and input loading and interacting, which also are considerations in communicating. C. Communicating: All exchanging of information between humans by a system or systems of symbols, signs and/or behavior. Communicating consists of emitting and receiving communicated information: oral or non-oral, direct or indirect, unaided or aided, and voluntary or involuntary. P. Processing information: Operating on and treating received information; basic handling of perceived information. Processing information includes storing and retrieving information (recognizing, recalling, reproducing), acquiring and using concepts (acquiring, symbolizing, defining), altering information (calculating and computing, logical and mathematical transforming, encoding and decoding), reasoning (intuiting, inductive and deductive explicit reasoning), imagining (anticipatory, creative, fanciful). M. Managing personal performance: Guiding and directing one's own performance. The “executive” function, which is concerned with carrying into effect and integrating the four other major functions. Managing personal performance includes valuing, making decisions, and initiating and sustaining personal performance. A. Acting: Carrying into effect; changing system physical states. The fifth major function covers producing physical effects. The means is exerting force within oneself and/or on other objects; the direct or indirect result or output is work. Acting includes direct acting (e.g., manually lifting an object) and indirect acting (e.g., operating the controls of an overhead crane lifting an object).

The Unaccountable Element in Science

I hope that it will become clear in the course of this paper what I mean by calling some elements of science unaccountable. Let me now say only that I shall speak of the contributions made to scientific thought by acts of personal judgment which cannot be replaced by the operation of explicit reasoning. I shall try to show that such tacit operations play a decisive part not only in the discovery, but in the very holding of scientific knowledge. I shall outline the structure of these acts and indicate to what extent this structure offers a justification for relying on such acts.

The Unaccountable Element in Science

I hope that it will become clear in the course of this paper what I mean by calling some elements of science unaccountable. Let me now say only that I shall speak of the contributions made to scientific thought by acts of personal judgment which cannot be replaced by the operation of explicit reasoning. I shall try to show that such tacit operations play a decisive part not only in the discovery, but in the very holding of scientific knowledge. I shall outline the structure of these acts and indicate to what extent this structure offers a justification for relying on such acts.