Hypothetico-nomological aspects of medical diagnosis Part I: General structure of the diagnostic process and its hypothesis-directed stage

In medical diagnostic examination three main stages may be distinguished: (a) initial exploration, (b) hypothesis-directed investigation, and (c) final diagnosis making. The purpose of this work is to study some methodological problems concerning the second of the above stages of the diagnosis and to prepare a background for a mathematical model [30] of this process. In diagnostic problem solving, the reasoning proceeds along the main lines traced by some initial suggestions and passes through various intermediate elements which are connected with one another forming ramifying chains and nets of inferences and hypotheses. Such a complex mental construction is based on laws which form medical knowledge and reflect various regularities and relations, causal, structural, functional, and others. The main components of diagnostic reasoning may be divided into several classes according to their function and content: leading hypotheses, working hypotheses, main diagnostic hypotheses, statements accepted as certain, intermediary and reserve hypotheses, therapeutic suggestions of immediate consequence. In an example of diagnostic problem solving these types of propositions are defined and analysed. In diagnostic reasoning, as in every other process of rational problem solving, explanation of the observed symptoms and signs and testing of the explaining hypotheses play a predominant role. These procedures form successive, frequently numerous and diversified steps and stages of the reasoning, leading to the construction of a mental model of the patient's state. Some problems relative to the scheme of explanation, especially to that which is based on causal laws, are discussed.     

国外关于问题解决的研究及其教学意义

提高学生解决问题能力已经成为现代教育的一个重要目标。问题解决就是由一定情境引起的,按照一定的目标,应用各种认知活动、技能等,经过一系列思维操作,使问题得以解决的过程。本文综述了问题解决的过程模型,影响问题解决的心理因素,并寻找其教学意义,以期给当前我国教育与教学改革提供参考。     

Mathematical model building in the solution of mechanics problems: Human protocols and the MECHO trace

This paper describes model building and manipulation in the solution of problems in mechanics. An automatic problem solver, MECHO, solving problems in several areas of mechanics, employs (1) a knowledge base representing the semantic content of the particular problem area, (2) a means-ends search strategy similar to GPS to produce sets of simultaneous equations and (3) a “focusing” technique, based on the data within the knowledge base, to guide the GSP-like search through possible equation instantiations. Sets of predicate logic statements are employed to describe this model building activity. These clauses are used to give information content to the knowledge base and provide both basis and guidance for the goal driven search. It is hypothesized that humon subjects solving mechanics problems employ similar model building techniques. Protocols of several subjects are presented and comparisons are drawn with the traces of the automated problem solver. Adjustments are made to the program to provide a better fit of traces to protocols. Some implications are presented for using a rule based model for describing human problem solving performance in solving mechanics problems.     

A Visual Representation to Quantitate,Diagnose, and Improve Creativity in Insight Problem Solving

A new visual representation for insight problems permits 22 new quantitative measures; which leads to a detailed diagnosis of a person's (or team's) creative weaknesses; which then leads to prescribing targeted, effective counter‐techniques for each weakness. Currently, only two measures are consistently used for insight problem solving: the number of problems solved and the time to solve the problems. These coarse measurements do not reveal the intricate dynamics of solving insight problems. Furthermore, four commonly used creativity measures (i.e., fluency, originality, flexibility, and elaboration) are often not applied to insight problems. This new visualization permits the easy application of all four creativity measures. I challenge creativity researchers to help determine which of the 22 proposed quantitative measures are the most diagnostic for insight problem solving in isolation and, in a weighted linear combination, which might yield an effective quotient (i.e., overall measure) of insight problem solving ability.     

Linking the demands–control–support model to innovation: The moderating role of personal initiative on the generation and implementation of ideas

The demands–control–support model indicates that workers can use job control and social support for problem solving. We examined whether personal initiative moderated relationships between, on the one hand, job control used for problem solving and social support used for problem solving and, on the other hand, ideas generation and implementation. We operationalized job control used for problem solving as ‘changing aspects of work activities to solve problems’. We operationalized social support used for problem solving as ‘discussing problems to solve problems’. Using an experience sampling methodology, participants provided data for up to four times a day for up to five working days (N= 89). The extent to which people ‘changed aspects of their work activities to solve problems’ was associated with higher levels of ideas generation for people with high personal initiative. The extent to which people ‘discussed problems to solve problems’ was associated with higher levels of ideas implementation for people with high personal initiative.     

Cognitive processes in well-defined and ill-defined problem solving

We investigated the relationship between two kinds of problem solving using Kitchener's model of hierarchical cognitive processing. We predicted that performance on well-defined problems (i. e. those with a single, guaranteed solution) would be independent of ill-defined problems (i. e. those with multiple, non-guaranteed solutions). We also predicted that self-reported epistemic beliefs (i. e. assumptions about the nature and acquisition of knowledge) would be related to ill-defined, but not well-defined, solutions. Results confirmed these predictions. We concluded that well-defined and ill-defined problems require separate cognitive processes and that epistemic beliefs play an important role in ill-defined problem solving. These findings supported Kitchener's three-level model of problem solving.     

The contributions of convergent thinking,divergent thinking,and schizotypy to solving insight and non-insight problems

The ability to generate diverse ideas (divergent thinking) is valuable in solving creative problems (e.g., insight problems); yet, however advantageous, this ability is insufficient to solve the problem alone and requires the ability to logically deduce an assessment of correctness of each solution (convergent thinking). Positive schizotypy may help isolate the aspects of divergent thinking prevalent in insight problem solving. Participants were presented with a measure of schizotypy (Oxford-Liverpool Inventory of Feelings and Experiences), divergent and convergent thinking tasks, insight problems, and non-insight problems. We found no evidence for a relationship between schizotypy and insight problem solving. Relationships between divergent thinking and insight problem solving were also surprisingly weak; however, measures of convergent thinking had a stronger relationship with problem solving. These results suggest that convergent thinking is more important than divergent thinking in problem solving.     

A computational model for solving problems from the Raven’s Progressive Matrices intelligence test using iconic visual representations

We describe a computational model for solving problems from Raven’s Progressive Matrices (RPM), a family of standardized intelligence tests. Existing computational models for solving RPM problems generally reason over amodal propositional representations of test inputs. However, there is considerable evidence that humans can also apply imagery-based reasoning strategies to RPM problems, in which processes rooted in perception operate over modal representations of test inputs. In this paper, we present the “affine model,” a computational model that simulates modal reasoning by using iconic visual representations together with affine and set transformations over these representations to solve a given RPM problem. Various configurations of the affine model successfully solve between 33 and 38 of the 60 problems on the Standard Progressive Matrices, which matches levels of performance for typically developing 9- to 11-year-old children. This suggests that, for at least a sizeable subset of RPM problems, it is not always necessary to extract amodal symbols in order to arrive at the correct answer, and iconic visual representations constitute a sufficient form of representation to successfully solve these problems. We intend for the affine model to serve as a complementary computational account to existing propositional models, which together may provide an integrated, dual-process account of human problem solving on the RPM.     

PROBLEM‐SOLVING SCHEMATA FOR TEXT TYPES: A COMPARISON OF NARRATIVE AND EXPOSITORY TEXT STRUCTURES

In this paper we argue that comprehension is a problem solving activity and that different problem solving schemata exist for different types of texts, both narrative and expository. When processing the text, the reader uses these problem solving schemata to construct meaning of the context and to satisfy personal reading goals. For instance, comprehension of a text, such as a short story, a folk tale, or even a passage from a science book, can be viewed as a problem solving activity. Bransford and Stein's (1984) IDEAL model identifies the problem solver as one who (I) identifies problems, (D) defines and represents problems, (E) explores possible strategies for solving the problem, (A) acts on those strategies, and (L) looks back and evaluates the effects of those strategies. This problem solving model aptly describes what readers as problem solvers do when comprehending a text. The reader as a problem solver identifies and defines specific reading goals. To understand the content of the passage, the reader as a problem solver uses domain‐specific knowledge about the topic, prior experience in reading that type of text to explore a multitude of strategies. While reading the text, the reader uses those strategies to construct meaning about the text. After reading a passage, the proficient reader as a problem solver evaluates what has been read and checks to see if one's reading goals have been met.     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

A model for diagnostic problem solving

A formal model for diagnostic problem solving is proposed which comprises a goal block and an operational block in short-term memory, together with an environment information block and a psychoinformation block in long-term memory.     

Theory of constructions and set in problem solving

Hierarchically organized knowledge about actions has been postulated to explain planning in problem solving. Perdix, a simulation of problem solving in geometry with schematic planning knowledge, is described. Perdix’s planning knowledge enables it to augment the problem space it is given by constructing auxiliary lines. The planning system also provides a mechanism that can result in problem solving set. Results of three experiments involving set and constructions seem consistent with the kinds of knowledge structures hypothesized in the model. Protocols given during solution of 11 geometry problems showed general agreement with the explanation of constructions and set based on planning knowledge, but they also indicated processes of human problem solving not represented in the model. Finally, the explanation of constructions is discussed in relation to the general question of ill-structured problems and creativity, and the explanation of set is discussed in relation to other phenomena in the problem solving literature, including functional fixedness.     

Tracks to a Medical Diagnosis: Expertise Differences in Visual Problem Solving

This study focuses on the visual problem‐solving process of clinical pathologists. Its aim is to find expertise‐related differences in the temporal arrangement of this process, with a special focus on the orientation phase. A theoretical model of the visual diagnostic process of medical specialists is extended with general problem‐solving theory. Participants were 13 experts, 12 intermediates, and 13 novices, who all diagnosed seven microscopic images. Their microscope movements and thinking aloud were recorded. To study temporal arrangement of the process, we applied a time‐grid to the data. The results reflected several aspects of general problem‐solving theory. Experts and intermediates showed a more extensive orientation phase and more refined schemata than novices. Intermediates also showed a control phase at the end of the diagnostic process. Novices showed a uniform process. These phases were reflected in microscope navigation and thinking aloud, which justifies the extension of the theoretical model.Copyright © 2016 John Wiley & Sons, Ltd.     

Cognitive Abilities Involved in Insight Problem Solving: An Individual Differences Model

This study investigated individual differences in cognitive abilities that contribute to solving insight problems. A model is proposed describing three types of cognitive ability that contribute independently to insight: convergent thinking, divergent thinking, and breaking frame. The model was tested in a large sample (N = 108) by regressing insight problem solving performance on measures of these three abilities. This analysis demonstrated that all three abilities predicted insight independently. Convergent thinking was further broken down into verbal intelligence and working memory, which also predicted insight independently of each other and of divergent thinking and breaking frame. Finally, when pitted against noninsight problem solving as a predictor in regression, only insight problem solving was uniquely associated with divergent thinking and breaking frame. The model is suggested as a potentially useful taxonomy for the study of ill-defined problems and cognitive abilities.     

代数应用题项目生成中的认知过程与任务特征分析

认知项目设计起源于理论驱动的测量模式,要求在详尽阐述测量建构理论的基础上,鉴别测验项目类型的任务特征,建立和标定与测量建构认知变量之间的对应关系,从而通过系统变换任务特征生成项目.本文以Greeno (1994)提出的代理者-情境交互作用(agent-situation interaction)的问题解决观为基础,对代数应用题解决的认知过程和任务蕴含性(affordance)进行了系统分析.具体而言,代数应用题被视为以文本形式呈现的,嵌套在现实情境中的某种量化结构.在代数应用题解决的转译、整合、计划、执行的认知过程中需要形成三种不同的表征,即文本基、情境模型和问题模型.其形成及解决过程受到个体问题图式知识的深刻影响.对应于不同表征方式,对代数应用题语法/语义结构、量化元素及结构、问题情境特征进行了系统分析和综合.该分析表明,为了保证代数应用题项目生成的科学性,当前研究的关键在于建立任务特征与问题解决认知变量之间的经验关系,并在问题领域整体水平上检验这种关系的可推广性.     

Replication of Psycholinguistic Experiments and the Resolution of Inconsistencies

Non-exact replications are regarded as effective tools of problem solving in psycholinguistic research because they lead to more plausible experimental results; however, they are also ineffective tools of problem solving because they trigger cumulative contradictions among different replications of an experiment. This paper intends to resolve this paradox by putting forward a metatheoretical model that clarifies the criteria with the help of which various aspects of the effectiveness of the problem solving process can be differentiated and evaluated. The key point is the reconstruction of the relationship between original experiments and their non-exact replications by introducing the concept of ‘experimental complex’ and analysing the problem solving strategies that the researchers apply. The workability of the proposed metatheoretical model is illustrated with the help of three case studies.     

A production system model for human problem solving

Summary A new production system model was developed for a class of transformation problems, based upon the results of an experiment which evaluated the psychological validity of a noticing order in problem solving. This model specifies how general problem solving heuristics interact with domain knowledge and how possible actions are assembled from externally presented information. The production system may conceptually be divided into three groups: move generation, move evaluation, and move execution productions. The simulation model was evaluated by a second experiment. With a common set of parameter values, good fits were obtained between the predicted and observed data. The significance of individual productions was assessed by running simulations with individual productions deleted. While previous simulations have described subjects' behavior on a single problem, the present model successfully predicted human problem solving in three structurally different problems. The proposed production system thus gives a detailed account of the procedural knowledge that subjects use in transformation problems.     

Problem Solving,Stress, and Coping in Adolescent Suicide Attempts

Twenty adolescents who had made suicide attempts were compared with 20 nonpsychiatric control subjects on measures of problem solving, stress, and coping. The suicidal group did not show evidence of “rigid” thinking or of deficits in the ability to generate solutions to standardized interpersonal problems. However, they did report recent histories of more severe life stress and had inaccurate appraisal of the extent to which stressful events could be controlled. Although suicidal patients were able to generate as many adaptive strategies as control subjects for coping with their own most severe recent real-life stressor, they actually used fewer. They were also more likely to identify maladaptive behaviors as ways of coping. These findings support a transactional model of adolescent suicidal behavior, whereby inaccuracies in the appraisal aspects of problem solving (but not in the solution-generation aspects) in the face of high life stress lead to a reduction in the use of adaptive efforts to cope.