
By Robert M French, Elizabeth Thomas
This e-book introduces a number of connectionist types of cognition and behaviour. the key parts coated are high-level cognition, language, categorization and visible notion, and sensory and attentional processing. all the articles hide unpublished examine paintings. the major contribution of this booklet is that it focuses solely at the advances in connectionist modeling in psychology. The papers are particularly brief, and have been explicitly written to be obtainable to either connectionist modelers and experimental psychologists.
Read or Download From Associations to Rules: Connectionist Models of Behavior and Cognition: Proceedings of the Tenth Neural Computation and Psychology Workshop (Progress ... Processing) (Progress in Neural Processing) PDF
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Extra resources for From Associations to Rules: Connectionist Models of Behavior and Cognition: Proceedings of the Tenth Neural Computation and Psychology Workshop (Progress ... Processing) (Progress in Neural Processing)
Example text
Proc. 61, 37 (2003). 2. L. G. Allan, Bul. Psychon. Soc. 15, 147 (1980). 3. L. G. Allan and H. M. Jenkins, Learn. and Motiv. 14, 381 (1983). 4. L. G. Allan, S. Siegel and J. M. Tangen, Learn. & Behav. 33, 250 (2005). 5. L. B. Alloy and L. Y. Abramson, J. of Exp. : Gen. 108, 441 (1979). 6. C. M. Bishop, (1995). Neural Networks for Pattern Recognition. Oxford: Oxford University Press. 7. P. W. Cheng and L. R. Novick, Psych. Rev. 99, 365 (1992). 8. M. J. C. Crump, S. D. Hannah, L. G. Allan and L. K.
This pattern is readily explained in randomchanging context model in that the encoded context of items presented in close temporal distance is similar that items with larger distance. With the assumption of retrieval of encoded context, conditional response probabilities (CRP) will reflect this contextual overlap. Kahana (1996) also showed that the CRPfunctions are asymmetric and biased in the forward direction. In other words given item n the next item to be retrieved is more likely to be n+1 than n-1 even though both are more likely than n+2 or n-2.
Therefore, item n+1 gets encoded with a contextual state that is a combination of its retrieved pre-experimental context, some pre-experimental context of item n and so on. Importantly, item n-1 has not been encoded with any contextual features from n’s pre-experimental context. In other words, the forward asymmetry is already present in the encoded context. During the retrieval phase one has only to assume that the pre-experimental context is retrieved after the retrieval of the item. The combined context will then favor item n+1 given retrieval of item n.
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