From tools to theories: A heuristic of discovery in cognitive psychology.

Abstract

The study of scientific discovery-where do new ideas come from?-has long been denigrated by philosophers as irrelevant to analyzing the growth of scientific knowledge. In particular, little is known about how cognitive theories are discovered, and neither the classical accounts of discovery as either probabilistic induction (e.g., Reichenbach, 1938) or lucky guesses (e.g., Popper, 1959), nor the stock anecdotes about sudden "eureka" moments deepen the insight into discovery. A heuristics approach is taken in this review, where heuristics are understood as strategies of discovery less general than a supposed unique logic of discovery but more general than lucky guesses. This article deals with how scientists' tools shape theories of mind, in particular with how methods of statistical inference have turned into metaphors of mind. The tools-to-theories heuristic explains the emergence of a broad range of cognitive theories, from the cognitive revolution of the 1960s up to the present, and it can be used to detect both limitations and new lines of development in current cognitive theories that investigate the mind as an "intuitive statistician."

Domain-specific reasoning: Social contracts, cheating, and perspective change

“…The answers proposed were contentindependent formal theories. For the most part, good reasoning was reduced to propositional logic and a simplistic version of Bayesianism (see Gigerenzer, 1991aGigerenzer, , 1991b. (2) How shall we understand the role of content and structure in reasoning?…”

On welfare frameworks and catastrophic climate risks

“…The widespread focus on the CRRA utility function is perhaps an instance of the 'tools-totheories' heuristic (Gigerenzer, 1991), in which an analytical tool -the CRRA utility function -is elevated to the status of a theory, in this case of normative welfare analysis. If we believe the axioms of expected utility theory, as elucidated by von Neumann & Morgenstern (1944), and especially Savage (1954), we should evaluate uncertain prospects with expected utility functionals.…”

Does Cognitive Science Need Kernels?

“…Using the tools-to-theories heuristic (Gigerenzer, 1991) the computational problem that the subject tries to solve can be formalized in the same way that it is formalized in machine learning.…”