Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

The Activation of Hypotheses during Abductive Reasoning

Abstract: The Activation of Hypotheses during Abductive Reasoning Martin R. K. Baumann (martin.baumann@phil.tu-chemnitz.de) Katja Mehlhorn (katja.mehlhorn@phil.tu-chemnitz.de) Franziska Bocklisch (franziska.bocklisch@phil.tu-chemnitz.de) Chemnitz University of Technology, Department of Psychology, Wilhelm-Raabe-Str. 43, 09107 Chemnitz, Germany observations. Following the above example, in most cases a patient complains not only about one symptom, such as a headache, but about a set of observations that could be a headache, sickness, and fever. Each of these symptoms can be caused by many different diseases. The physician’s task is to find the best explanation for the whole symptoms set. And, despite the complexity of the problem, the physician solves the problem in most cases quickly and accurately. How is this accomplished? Johnson and Krems (2001) suggested on the basis of their results on abductive reasoning that people use initial observations to construct a preliminary explanation for these observations. Succeeding observations are sequentially comprehended and integrated to generate a single current explanation for all observations seen so far. If an observation can be comprehended in different ways, that is, if there exist alternative elementary explanations for this new observation, the current explanation is used to decide between these alternatives. Only those elementary explanations for the new observations are considered as relevant that are compatible with the current explanation. Thus, the current explanation acts as an explanatory context for the comprehension and explanation of new observations. It reduces the complexity of the abductive reasoning problem as not all possible elementary explanations for a new observation are considered as relevant but only those that are compatible with the current explanation. Whereas Johnson and Krems’ model focuses on deliberate reasoning processes to describe the use of the current explanatory context, we assume that automatic comprehension processes based on spreading activation and constraint satisfaction also play a key role. It has been argued recently that both deliberate and automatic processes are involved in many reasoning tasks (Sloman, 1996) such as impression formation (Thagard & Kunda, 1998), hypothesis evaluation (Johnson, Zhang, & Wang, 1997), and medical diagnosis (Arocha & Patel, 1995). Thagard and Kunda explain how spreading activation processes can explain the effect of social stereotypes on the interpretation of behavior. Johnson, Zhang, and Wang show how automatic processes can provide information for the evaluation of hypotheses that is used subsequently in more deliberate processes to revise existing or generate new hypotheses in an abductive reasoning task. In our view these automatic processes also serve the function of making those elementary explanations of new observations highly available to the reasoner that have a high probability of being the relevant explanations in the Abstract Abductive reasoning, that is, finding an explanation for a set of observations, can be understood as a process of sequentially understanding and integrating new observations into a mental model about the current situation (Johnson & Krems, 2001; Josephson & Josephson, 1994). Whereas Johnson and Krems’ model focuses on conscious deliberate processes, it has been argued that automatic implicit processes also play an important role in abductive reasoning (e.g. Johnson, Zhang, & Wang, 1997). Adopting Kintsch`s (1998) construction-integration theory, we assume that automatic activation processes regulate the availability of possible explanations during the reasoning process. In our experiment, participants solved an artificial diagnosis task while the activation of explanatory hypotheses was measured. We found that explanatory hypotheses relevant in the current context for explaining a set of observations are kept in a more active state in memory than irrelevant or rejected hypotheses. Keywords: abductive reasoning; causal reasoning; automatic processes; explanations; activation. Introduction Generating a hypothesis to explain one or more observations is an essential part of many real world tasks. This kind of reasoning is called abductive reasoning (Josephson & Josephson, 1994). It is a vital subprocess, for example, in scientific discovery, medical diagnosis, software debugging, social attribution processes, and discourse comprehension. While explaining a given set of observations, the reasoner has often to decide between different alternative explanations to find the best explanation for the observations. We assume that both deliberate reasoning processes and automatic comprehension processes contribute to the generation of hypotheses (Johnson, Zhang, & Wang, 1997; Sloman, 1996). The goal of this paper is to examine how automatic comprehension processes constrain the consideration of hypotheses to the most plausible ones in the given context by making these hypotheses highly available to the reasoner and reducing the availability of implausible ones. Constructing an explanatory hypothesis can be a quite complex task. First, in many cases there is more than one possible explanation for a given observation. For example, headache is a common symptom of many diseases and is associated with many different causes. Second, the task is often not to explain one observation but a set of observations where each observation can be explained with more than one explanation. In such a case, a combination of elementary hypotheses has to be found that best explains all

Logic structure of clinical judgment and its relation to medical and psychiatric semiology.

Abstract: Background: The logical nature of clinical judgment has been conceptualized in different ways, but a clear connection between the features of clinical judgment and those of semiology is still lacking. Methods: The characteristics of clinical judgment, medical semiology, and psychiatric semiology are described. Connections between them are drawn. Results: Clinical judgment is described as an abductive inference. Abductive inferences are especially useful to balance universal and singular information. In psychiatric semiology, due to some specific features, a careful balance between the information present in descriptive definitions and the information absent from the definition but present in singular symptoms is needed. The main types of out-of-definition information are reviewed. Conclusions: The implications of the results for diagnosis and research are drawn.

Analogy in Legal Reasoning

Abstract: Analyses of legal reasoning, whatever the axes they grind, inevitably deal with analogy. But they have, so far at least, been disappointing in their failure to develop a convincing general account of validity for such arguments. Some are content to classify various types, noting with respect to each type where difficulties regarding validity are located. A few seem to imply that the necessity for analogical argument entails, ipso facto, that legal argument lacks adequate criteria of validity. Of course, the whole matter of the validity of nondemonstrative inferences is murky. But I want to argue that a misleadingly incomplete account of the nature of analogy has unnecessarily complicated matters-indeed, that it has occasionally led away from fruitful accounts of judicial decision making. The difficulty lies in a fixation on what I shall call \"static analogies,\" which, as it turns out, are only one component of useful analogical arguments, and probably not even the most important component at that. What I shall call \"dynamic analogies\" are the crucial component, and a component which carries on its face the outline of validity conditions for analogical arguments in general.

Describing Young Children's Deductive Reasoning.

Abstract: This paper reports results related to the development of a consistent descriptive language for research on mathematical reasoning. Ways of reasoning deductively are highlighted, using examples drawn from observations of young students. One-step deductions versus multi-step deductions, known versus hypothetical premises, and single versus multiple premises, are used to distinguish different ways of reasoning. (Author) Reproductions supplied by EDRS are the best that can be made from the original document. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. DESCRIBING YOUNG CHILDREN'S DEDUCTIVE REASONING David A Reid Acadia University This paper reports results related to the development Of a consistent descriptive language for research on mathematical reasoning Ways of reasoning deductively are highlighted using examples drawn from observations of young students. Onestep deductions versus multi-step deductions, known versus hypothetical premises, and single versus multiple premises, are used to distinguish different ways of reasoning. This paper reports results related to the development of a consistent descriptive language for research on mathematical reasoning. These results arose out of a long term research project (the PRISM project [1]) aimed at elaborating and clarifying previous models and terminology for describing reasoning. The model now in use for this research project describes reasoning across five dimensions: need, target, kind of reasoning, formulation and formality; and has been used to describe reasoning of students of all ages (Reid 1995a,b, 1997, 1998, in press). In this paper one dimension, ways of reasoning, will be highlighted, using results drawn from observations of students aged about seven years. THE MODEL The PRISM project took as its beginning point a model for reasoning outlined by Reid (1995a, 1996b). It includes four dimensions for describing reasoning. Need includes the needs to explain and to verify mathematical statements and to explore to discover. new statements. This dimension of the model was inspired by the work of Bell (1976) and de Villiers (1990). Kind of reasoning includes reasoning deductively, inductively and by analogy, and was inspired by the work of Polya (1954/1990). Formulation refers to the degree of awareness the reasoner has of their own reasoning. Formality refers to the degree to which the expression of the reasoning conforms to the requirements of mathematical style. The work of Lakatos (1978) and Blum & Kirsch (1991) inspired this dimension. One of the refinements of this model that has resulted from the PRISM project is the addition of a fifth dimension, target, that describe who the reasoning is for: a teacher, a peer or oneself This model of reasoning is compatible with those being developed by others. For example, Sowder and Hard (1998) have outlined a model describing what they call "proof schemes". In terms of the PRISM model it offers additional detail concerning kinds of reasoning but is limited to those kinds of reasoning related specifically to the need to verify.

Argumentation, stories and generalizations: a comment

Abstract: The underlying theory of the software program described in ‘Sense-Making Software for Criminal Investigation’ (Bex et al., 2007) complements modified Wigmorean analysis (MWA). Both adopt a qualitative rather than a quantitative approach. MWA is broadly compatible with the kind of logic involved, including abductive inference to the best explanation and the idea of defeasible argumentation. Both approaches are mainly valuable as aids to thinking, especially constructing and evaluating arguments, rather than as methods of presenting them in order to persuade. Both approaches can be applied at different stages of criminal investigation (and more broadly of legal processes), but the specific device of Wigmore charts (one part of MWA) is more useful in hypothesis testing and discarding than in hypothesis formation, which typically requires imaginative reasoning. The Anchored Narratives of Crombag et al. and MWA have similar theoretical assumptions, except that MWA gives a radically different account about the relationship between stories, generalizations and argument. The proposed program has considerable promise, but before it can be of positive practical value in police investigation, more attention needs to be given not only to the obvious dangers of using stories and generalizations in this context but also about what positive guidance can be given to mitigate these dangers. There is, however, an unresolved tension between the simplifying tendencies of formalized computer programming and the tendency of MWA to emphasize the complexities of practical inferential reasoning and argumentation in legal contexts.