Introduction

Why did the oxygen theory of combustion supersede the phlogiston theory? Why is Darwin's theory of evolution by natural selection superior to creationism? How can a jury in a murder trial decide between conflicting views of what happened? This target article develops a theory of explanatory coherence that applies to the evaluation of competing hypotheses in cases such as these. The theory is implemented in a connectionist computer program with many interesting properties.

The problem of inference to explanatory hypotheses has a long history in philosophy and a much shorter one in psychology and artificial intelligence (AI). Scientists and philosophers have long considered the evaluation of theories on the basis of their explanatory power. In the late nineteenth century, Peirce discussed two forms of inference to explanatory hypotheses: hypothesis, which involved the acceptance of hypotheses, and abduction, which involved merely the initial formation of hypotheses (Peirce 1931–1958; Thagard 1988a). Researchers in artificial intelligence and some philosophers have used the term “abduction” to refer to both the formation and the evaluation of hypotheses. AI work on this kind of inference has concerned such diverse topics as medical diagnosis (Josephson et al. 1987; Pople 1977; Reggia et al. 1983) and natural language interpretation (Charniak and McDermott 1985; Hobbs et al. 1988). In philosophy, the acceptance of explanatory hypotheses is usually called inference to the best explanation (Harman 1973, 1986). In social psychology, attribution theory considers how people in everyday life form hypotheses to explain events (Fiske and Taylor 1984).

WHAT IS ABDUCTION?

In the 1890s, the great American philosopher C. S. Peirce (1931–1958) used the term “abduction” to refer to a kind of inference that involves the generation and evaluation of explanatory hypotheses. This term is much less familiar today than “deduction,” which applies to inference from premises to a conclusion that has to be true if the premises are true. And it is much less familiar than “induction,” which sometimes refers broadly to any kind of inference that introduces uncertainty, and sometimes refers narrowly to inference from examples to rules, which I will call “inductive generalization.” Abduction is clearly a kind of induction in the broad sense, in that the generation of explanatory hypotheses is fraught with uncertainty. For example, if the sky suddenly turns dark outside my window, I may hypothesize that there is a solar eclipse, but many other explanations are possible, such as the arrival of an intense storm or even a huge spaceship.

Despite its inherent riskiness, abductive inference is an essential part of human mental life. When scientists produce theories that explain their data, they are engaging in abductive inference. For example, psychological theories about mental representations and processing are the result of abductions spurred by the need to explain the results of psychological experiments. In everyday life, abductive inference is ubiquitous, for example when people generate hypotheses to explain the behavior of others, as when I infer that my son is in a bad mood to explain a curt response to a question.

This chapter discusses the cognitive contributions that emotions make to scientific inquiry, including the justification as well as the discovery of hypotheses. James Watson's description of how he and Francis Crick discovered the structure of DNA illustrates how positive and negative emotions contribute to scientific thinking. I conclude that emotions are an essential part of scientific cognition.

Introduction

Since Plato, most philosophers have drawn a sharp line between reason and emotion, assuming that emotions interfere with rationality and have nothing to contribute to good reasoning. In his dialogue the Phaedrus, Plato compared the rational part of the soul to a charioteer who must control his steeds, which correspond to the emotional parts of the soul (Plato, 1961, p. 499). Today, scientists are often taken as the paragons of rationality, and scientific thought is generally assumed to be independent of emotional thinking.

Current research in cognitive science is increasingly challenging the view that emotions and reason are antagonistic to each other, however. Evidence is accumulating in cognitive psychology and neuroscience that emotions and rational thinking are closely intertwined (see, for example: Damasio, 1994; Kahneman, 1999; Panksepp, 1999). My aim in this chapter is to extend that work and describe the role of the emotions in scientific thinking. If even scientific thinking is legitimately emotional, then the traditional division between reason and emotion becomes totally unsupportable.

My chapter begins with a historical case study.

The power of human intelligence depends on the growth of knowledge through experience, coupled with flexibility in accessing and exploiting prior knowledge to deal with novel situations. These global characteristics of intelligence must be reflected in theoretical models of the human cognitive system (Holland, Holyoak, Nisbett, & Thagard, 1986). The core of a cognitive architecture (i.e., a theory of the basic components of human cognition) consists of three subsystems: a problem-solving system, capable of drawing inferences to construct plans for attaining goals; a memory system, which can be searched in an efficient manner to identify information relevant to the current problem; and an inductive system, which generates new knowledge structures to be stored in memory so as to increase the subsequent effectiveness of the problem-solving system.

These three subsystems are, of course, highly interdependent; consequently, the best proving ground for theories of cognition will be the analysis of skills that reflect the interactions among problem solving, memory, and induction. One such skill is analogical problem solving – the use of a solution to a known source problem to develop a solution to a novel target problem. At the most general level, analogical problem solving involves three steps, each of which raises difficult theoretical problems (Holyoak, 1984, 1985). The first step involves accessing a plausibly useful analog in memory. It is particularly difficult to identify candidate analogs when they are concealed in a large memory system, and when the source and target were encountered in different contexts and have many salient dissimilarities. These theoretical issues are closely related to those raised in Schank's (1982) discussion of “reminding.”