Clark Chinn

Most philosophers of science now believe that scientific data are theory laden, i.e., the evaluation of data is influenced by prior theoretical beliefs. Although there is historical and psychological evidence that is consistent with the theory-laden position, experimental evidence is needed to directly test whether prior beliefs influence the evaluation of scientific data. In a fully counterbalanced design, one group of subjects received evidence that dinosaurs were cold-blooded, and another group of subjects received evidence that dinosaurs were warm-blooded. The subjects reported a strong belief in whichever theory they had read about. Then subjects were presented with a piece of data that supported one theory and contradicted the other theory. The identical piece of data was rated as more believable when it was consistent with the subject's theory than when it was inconsistent. These results provide clear support for the position that scientific data are theory laden.

This paper presents an analysis of the forms of response that scientists make when confronted with anomalous data. We postulate that there are seven ways in which an individual who currently holds a theory can respond to anomalous data: (1) ignore the data; (2) reject the data; (3) exclude the data from the domain of the current theory; (4) hold the data in abeyance; (5) reinterpret the data; (6) make peripheral changes to the current theory; or (7) change the theory. We analyze psychological experiments and cases from the history of science to support this proposal. Implications for the philosophy of science are discussed.

This paper presents a cognitive account of the process of evaluating scientific data. Our account assumes that when individuals evaluate data, they construct a mental model of a data-interpretation package, in which the data and theoretical interpretations of the data are integrated. We propose that individuals attempt to discount data by seeking alternative explanations for events within the mental model; data-interpretation packages are accepted when the individual cannot find alternative accounts for these events. Our analysis indicates that there are many levels at which data-interpretation packages can be accepted or denied.

In this paper we provide a psychological account of the nature and development of explanation. We propose that an explanation is an account that provides a conceptual framework for a phenomenon that leads to a feeling of understanding in the reader/hearer. The explanatory conceptual framework goes beyond the original phenomenon, integrates diverse aspects of the world, and shows how the original phenomenon follows from the framework. We propose that explanations in everyday life are judged on the criteria of empirical accuracy, scope, consistency, simplicity, and plausibility. We conclude that explanations in science are evaluated by the same criteria, plus those of precision, formalisms, and fruitfulness. We discuss several types of explanation that are used in everyday life – causal/mechanical, functional, and intentional. We present evidence to show that young children produce explanations that have the same essential form as those used by adults. We also provide evidence that children use the same evaluation criteria as adults, but may not apply those additional criteria for the evaluation of explanations that are used by scientists.