Use of Current Explanations in Multicausal Abductive Reasoning Use of Current Explanations in Multicausal Abductive Reasoning
نویسندگان
چکیده
In multicausal abductive tasks a person must explain some findings by assembling a composite hypothesis that consists of one or more elementary hypotheses. If there are n elementary hypotheses, there can be up to 2 composite hypotheses. To constrain the search for hypotheses to explain a new observation, people sometimes use their current explanation—the previous evidence and their present composite hypothesis of that evidence; however, it is unclear when and how the current explanation is used. In addition, although a person’s current explanation can narrow the search for a hypothesis, it can also blind the problem solver to alternative, possibly better, explanations. This paper describes a model of multicausal abductive reasoning that makes two predictions regarding the use of the current explanation. The first prediction is that the current explanation is not used to explain new evidence if there is a simple (i.e., non-disjunctive, concrete) hypothesis to account for that evidence. The second prediction is that the current explanation is used when attempting to discriminate among several alternative hypotheses for new evidence. These hypotheses were tested in three experiments. The results are consistent with the second prediction: the current explanation is used when discriminating among alternative hypotheses. However, the first prediction—that the current explanation is not used when a simple hypothesis can account for new data—received only limited support. Participants used the current explanation to constrain their interpretation of new data in 46.5% of all trials. This suggests that context-independent strategies compete with context-dependent ones—an interpretation that is consistent with recent work on strategy selection during problem solving.
منابع مشابه
A Hybrid Learning Model of Abductive Reasoning
Multicausal abductive tasks appear to have deliberate and implicit components: people generate and modify explanations using a series of recognizable steps, but these steps appear to be guided by an implicit hypothesis evaluation process. This paper proposes a hybrid symbolic-connectionist learning architecture for multicausal abduction. The architecture tightly integrates a symbolic Soar model...
متن کاملPii: S0364-0213(01)00059-3
In multicausal abductive tasks a person must explain some findings by assembling a composite hypothesis that consists of one or more elementary hypotheses. If there are n elementary hypotheses, there can be up to 2 composite hypotheses. To constrain the search for hypotheses to explain a new observation, people sometimes use their current explanation—the previous evidence and their present comp...
متن کاملAbduction, Experience, and Goals: a Model of Everyday Abductive Explanation* Abduction, Experience, and Goals: a Model of Everyday Abductive Explanation
Many abductive understanding systems generate explanations by a backwards chaining process that is neutral both to the explainer's previous experience in similar situations and to why the explainer is attempting to explain. This article examines the relationship of such models to an approach that uses case-based reasoning to generate explanations. In this case-based model, the generation of abd...
متن کاملAbductive Reasoning and Automated Analysis of Feature Models: How are they connected?
In the automated analysis feature models (AAFM), many operations have been defined to extract relevant information to be used on decision making. Most of the proposals rely on logics to give solution to different operations. This extraction of knowledge using logics is known as deductive reasoning. One of the most useful operations are explanations that provide the reasons why some other operat...
متن کامل