An analysis of the delta rule and the learning of statistical associations
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neurocomputing: foundations of research
A computational theory of adaptive behavior based on an evolutionary reinforcement mechanism
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Applying associative theory to need awareness for personalized reminder system
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
A Java simulator of Rescorla and Wagner's prediction error model and configural cue extensions
Computer Methods and Programs in Biomedicine
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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 algorthimic theory, while the model recent theories are all computational. This paper provides a detailed derivation of the long-run behaviour 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 modification of the Rescorla-Wagner model impact the long-run behavior of the model.