Clark Chinn

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

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

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.