The Activation of Hypotheses during Abductive Reasoning

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