Goldstone, R. L., Gopnik, A., Thagard, P., & Ullman, T. D. (2018). Models of human scientific discovery. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (pp. 29-30). Madison, Wisconsin: Cognitive Science Society.
The scientific understanding of scientific understanding has been a long-standing goal of cognitive science. A satisfying formal model of human scientific discovery would be a major intellectual achievement, requiring solutions to core problems in cognitive science: the creation and use of apt mental models, the prediction of the behavior of complex systems involving interactions between multiple classes of elements, high-level perception of noisy and multiply interpretable environments, and the active interrogation of a system through strategic interventions on it – namely, via experiments. Over the past decades there have been numerous attempts to build formal models that capture what Perkins (1981) calls some of the “mind’s best work” – scientific explanations for how the natural world works by systematic observation, prediction, and testing. Early work by Hebert Simon and his colleagues (Langley, Simon, Bradshaw, & Zytkow, 1987) developed production rule systems employing heuristics to tame extremely large conjoint search spaces of experiments to run and hypotheses to test. Qualitative physics approaches seek to understand physical phenomena by building non-numeric, relational models of the phenomena (Forbus, 1984). Some early connectionist models interpreted scientific explanation in terms of emerging patterns of strongly activated hypotheses that mutually support one another (Thagard, 1992).