Surrogate reasoning is reasoning whose task is partially taken over by operations on external aids, such as sentences, diagrams, physical models, mathematical models, and computers. Drawing on the basic concepts in situation theory, we present a semi-formal model of surrogate reasoning. We claim that the relative advantages and disadvantages of different forms of surrogate reasoning can be explained with reference to the ways in which the default constraints on surrogates intervene in the processes of reasoning. We define and examine two prominent patterns of such constraint intervention (dubbed “free rides” and “overdetermined alternatives”). We also introduce the notion of “constraint projection” and try to capture the general framework in which different forms of constraint intervention take place in surrogate reasoning.
External resources sometimes change human performance drastically. Previous researches have explained this effect of external resources as a reduction of memory load or calculation load. D. A. Norman criticises this approach as “System View,” and proposes “Personal View” as an alternative. This latter approach assumes that external resources transform the original task. Based on this idea, Norman and his colleague have developed representational analysis to capture the characteristics of external resources. However, representational analysis ignores cognitive agents. When external resources transform a task, knowledge and skills required to solve the task are also changed. If a cognitive agent doesn't possess these knowledge and skills, transformed task doesn't become easier compared with original one. This means that difficulty of the transformed task is determined not only by the characteristics of the task captured by representational analysis, but also by the knowledge and skills possessed by a cognitive agent. In addition, transformation of task sometimes requires transformation of cognitive agents. From this point of view, three issues are discussed. First, changing task definition is an important ability. Second, change of task in learning situation changes what skill is acquired. Third, change of cognitive agent is influenced by culture.
Although expert-novice differences in various domains have so far been attributed to domain-specific schemata or perceptual-chunks, few past research has addressed the issue of schema acquisition itself. We address this issue in the domain of geometry proof problem-solving. Past literatures on geometry pointed out the significance of the diagrammatic features of problems as the basis of problem-solving memories and as a cue for abstract planning in constructing a proof. Based on this insight, we propose a new perceptual-chunking technique in which the learner chunks such “diagram elements visually grouped together” into a schema, using recognition propagation rules as chunking criteria. The rules represent how human solvers see the diagram elements and geometrical features. We implemented this chunking mechanism on a computer program, PCLEARN, and did some computational experiments to see how the learned chunks contribute to problem-solving improvement. Further, we designed a psychological experiment to examine how human subjects tend to parse the whole diagrams into parts after solving a set of geometry proof problems. Its result shows that the PCLEARN chunking technique can learn what human learner would learn much better than the conventional learners.
A study of two logic courses employing different modalities of information presentation (Stenning, Cox, & Oberlander, 1995) demonstrated improvements of general reasoning ability as measured by Graduate Record Exam (GRE) type analytical ability reasoning pre- and post-course tests, as well as interactions between students' pre-course aptitudes and modality of teaching. This paper investigates the reasoning processes involved in the students' solutions of one sub-scale of the GRE problems from that study by analysing their ‘work-scratchings’ on analytical reasoning (AR) items. These data are used to examine changes in what representations students select; their association with correct and incorrect solutions; the changes in selection brought about by teaching different kinds of students in different kinds of courses; the association between these changes and improvements in solution performance; and the relation between intuitive teaching recommendations and a theoretically motivated taxonomy of representations. Stenning & Oberlander (1995) present a theory of the cognitive differences between graphical and sentential representations which ascribes major cognitive properties of graphics to weakness of expressiveness. We apply this theory to the GRE AR problems and derive principled predictions of some constraints on the appropriateness of representations for problems. Analysis of the students' spontaneous representation selections shows that representational strategies do change differentially as a result of different teaching methods; the kinds of representation proposed by intuitive teaching recommendations as embodied in ‘crammers’ are globally correlated with success at solution; the theoretically based predictions of appropriate representations based on weakness of expression make rather better predictions that can be related to individual differences between students known to be important predictors of performance. These results are argued to have important practical pedagogical implications.
This paper proposes an experimental system named IST for facilitation of users' problem solving by providing image-schemas. First, it is ascertained based on a psychological experiment that image-schemas can promote user's analogical problem solving owing to their plasticity. The experimental result shows that image-schemas facilitating user's problem solving visualize the following two aspects abstractly; one is the objects and their relations appearing in the initial state of a target problem, and the other is the operators and their effects when applied. The IST system is constructed on the basis of the above result, taking the so-called radiation problem as an example.
We have analyzed the process by which people abandon their search in belief that they cannot find the necessary information. We are particularly interested in the reasons why they think that they have failed finding the target information. It is usually believed that information retrieval on computer databases is quite different from that in human networks, but there are some resemblances in the reasons why search is abandoned. There are several interesting observations in these processes. One is that even though people fail in getting the information they need, they sometimes do not realize their failure. We are building a model of human information retrieval in social settings using the analogy of computer database retrieval.