This paper argues that there are powerful similarities between cognitive development in children and scientific theory change. These similarities are best explained by postulating an underlying abstract set of rules and representations that underwrite both types of cognitive abilities. In fact, science may be successful largely because it exploits powerful and flexible cognitive devices that were designed by evolution to facilitate learning in young children. Both science and cognitive development involve abstract, coherent systems of entities and rules, theories. In both (...) cases, theories provide predictions, explanations, and interpretations. In both, theories change in characteristic ways in response to counterevidence. These ideas are illustrated by an account of children's developing understanding of the mind. (shrink)

This paper argues that there are powerful similarities between cognitive development in children and scientific theory change. These similarities are best explained by postulating an underlying abstract set of rules and representations that underwrite both types of cognitive abilities. In fact, science may be successful largely because it exploits powerful and flexible cognitive devices that were designed by evolution to facilitate learning in young children. Both science and cognitive development involve abstract, coherent systems of entities and rules, theories. In both (...) cases, theories provide predictions, explanations, and interpretations. In both, theories change in characteristic ways in response to counterevidence. These ideas are illustrated by an account of children's developing understanding of the mind. (shrink)

My concern is with the possible implications of research in developmental psychology for understanding the workings of modern science. I agree both with Gopnik's general naturalistic orientation and with her more specific claims about scientists as cognitive agents. Neither the formal structure of propositions nor the social structure of scientific communities provides sufficient resources for the understanding we seek. So I agree that the empirical study of human cognition is not only relevant, but necessary, for understanding how science works.

Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a background theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light (...) on the theoretical virtues of parsimoniousness, unification, and non ad hocness, on the dispute about Bayesianism, and on empiricism and scientific realism. * Both of us gratefully acknowledge support from the Graduate School at the University of Wisconsin-Madison, and NSF grant DIR-8822278 (M.F.) and NSF grant SBE-9212294 (E.S.). Special thanks go to A. W. F. Edwards.William Harper. Martin Leckey. Brian Skyrms, and especially Peter Turney for helpful comments on an earlier draft. (shrink)

The central problem with Bayesian philosophy of science is that it cannot take account of the relevance of simplicity and unification to confirmation, induction, and scientific inference. The standard Bayesian folklore about factoring simplicity into the priors, and convergence theorems as a way of grounding their objectivity are some of the myths that Earman's book does not address adequately. 1Review of John Earman: Bayes or Bust?, Cambridge, MA. MIT Press, 1992, £33.75cloth.

The advent of formal definitions of the simplicity of a theory has important implications for model selection. But what is the best way to define simplicity? Forster and Sober ([1994]) advocate the use of Akaike's Information Criterion (AIC), a non-Bayesian formalisation of the notion of simplicity. This forms an important part of their wider attack on Bayesianism in the philosophy of science. We defend a Bayesian alternative: the simplicity of a theory is to be characterised in terms of Wallace's Minimum (...) Message Length (MML). We show that AIC is inadequate for many statistical problems where MML performs well. Whereas MML is always defined, AIC can be undefined. Whereas MML is not known ever to be statistically inconsistent, AIC can be. Even when defined and consistent, AIC performs worse than MML on small sample sizes. MML is statistically invariant under 1-to-1 re-parametrisation, thus avoiding a common criticism of Bayesian approaches. We also show that MML provides answers to many of Forster's objections to Bayesianism. Hence an important part of the attack on Bayesianism fails. (shrink)

While endorsing Gopnik's proposal that studies of the emergence and modification of scientific theories and studies of cognitive development in children are mutually illuminating, we offer a different picture of the beginning points of cognitive development from Gopnik's picture of "theories all the way down." Human infants are endowed with several distinct core systems of knowledge which are theory-like in some, but not all, important ways. The existence of these core systems of knowledge has implications for the joint research program (...) between philosophers and psychologists that Gopnik advocates and we endorse. A few lessons already gained from this program of research are sketched. (shrink)