Article

From mere coincidences to meaningful discoveries

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Cognition (Impact Factor: 3.63). 06/2007; 103(2):180-226. DOI: 10.1016/j.cognition.2006.03.004
Source: PubMed

ABSTRACT People's reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on their functional role in processes of causal discovery and theory revision. We present a formal definition of coincidences in the context of a Bayesian framework for causal induction: a coincidence is an event that provides support for an alternative to a currently favored causal theory, but not necessarily enough support to accept that alternative in light of its low prior probability. We test the qualitative and quantitative predictions of this account through a series of experiments that examine the transition from coincidence to evidence, the correspondence between the strength of coincidences and the statistical support for causal structure, and the relationship between causes and coincidences. Our results indicate that people can accurately assess the strength of coincidences, suggesting that irrational conclusions drawn from coincidences are the consequence of overestimation of the plausibility of novel causal forces. We discuss the implications of our account for understanding the role of coincidences in theory change.

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    • "If the complexity-based model proposed here is correct, why would probabilistic or Bayesian accounts have partial predictive power? And why are coincidences systematically accompanied by a subjective feeling of low probability (Griffiths & Tenenbaum, 2007)? The relation between descriptive complexity and probability has always been noticed (Solomonoff, 1997), but its usual formulation as p = 2 –C(D) is unsatisfactory for our purpose. "
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    • "Bayesian models of cognitive inference are increasingly prominent is several areas of cognitive psychology, including animal and human learning (Courville et al. 2006, Tenenbaum et al. 2006, Steyvers et al. 2003, Griffiths and Tenenbaum 2008), visual perception and motor control (Yuille and Kersten 1006, Kording and Wolpert 2006),semantic memory and language processing (Steyvers et al. 2006, Chater and Manning 2006, Xu and Tenenbaum in press), and social cognition (Baker et al. 2007). For a recent overview of Bayesian models of cognition, see Griffiths et al. (2008). "
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