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 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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Probabilistic Models of Cognition: Conceptual Foundations.Nick Chater & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
The Myth of Cognitive Decline: Non‐Linear Dynamics of Lifelong Learning.Michael Ramscar, Peter Hendrix, Cyrus Shaoul, Petar Milin & Harald Baayen - 2014 - Topics in Cognitive Science 6 (1):5-42.
Granularity and the Acquisition of Grammatical Gender: How Order-of-Acquisition Affects What Gets Learned.Inbal Arnon & Michael Ramscar - 2012 - Cognition 122 (3):292-305.
Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - Cognitive Science 35 (8):1407-1455.
From Blickets to Synapses: Inferring Temporal Causal Networks by Observation.Chrisantha Fernando - 2013 - Cognitive Science 37 (8):1426-1470.
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