David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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The Rescorla–Wagner model has been a leading theory of animal causal induction for nearly 30 years, and human causal induction for the past 15 years. Recent theories (especially Psychol. Rev. 104 (1997) 367) have provided alternative explanations of how people draw causal conclusions from covariational data. However, theoretical attempts to compare the Rescorla–Wagner model with more recent models have been hampered by the fact that the Rescorla–Wagner model is an algorithmic theory, while the more recent theories are all computational. This paper provides a detailed derivation of the long-run behavior of the Rescorla– Wagner model under a wide range of parameters and experimental setups, so that the model can be compared with computational theories. It also shows that the model agrees with competing theories on a wider range of cases than had previously been thought. The paper concludes by showing how recently suggested modifications of the Rescorla–Wagner model impact the long-run behavior of the model
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Citations of this work BETA
Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik (2011). Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults. Cognitive Science 35 (8):1407-1455.
Chrisantha Fernando (2013). From Blickets to Synapses: Inferring Temporal Causal Networks by Observation. Cognitive Science 37 (8):1426-1470.
Alison Gopnik (2004). Children's Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers. Cognitive Science 28 (3):303-333.
Inbal Arnon & Michael Ramscar (2012). Granularity and the Acquisition of Grammatical Gender: How Order-of-Acquisition Affects What Gets Learned. Cognition 122 (3):292-305.
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