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Explaining Away in Weight Space
– Peter Dayan Sham, Peter Dayan, Sham Kakade
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3
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Semi-rational Models of Conditioning: The Case of Trial Order
– Nathaniel D. Daw, Aaron C. Courville, Peter Dayan
- 2007
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18
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The rat as particle filter
– Nathaniel D. Daw, Aaron C. Courville, Université De Montréal
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Surprising Predictions
– n.n.
- 1994
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Centre single caption. cf. [no comma]. RJ OCR scanned
– Klaus G. Melchers, Harald Lachnit, David R. Shanks
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Animal Learning & Behavior
– Nuria Ortega, Oskar Pineño
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6
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A comparison between elemental and compound training of cues in retrospective revaluation
– Martha Escobar, Oskar Pineño, Helena Matute
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3
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Backward blocking: The role of within-compound . . .
– Miguel A. Vadillo, Leyre Castro, Helena Matute, Edward A. Wasserman
- 2008
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Positive and negative mediation as a function of whether the absent cue was previously associated with the outcome
– Leyre Castro, Helena Matute
- 2010
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Outcome similarity modulates retroactive interference . . .
– Oskar Pineño, Helena Matute
- 2005
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95
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A theory of causal learning in children: Causal maps and Bayes nets
– Alison Gopnik, Clark Glymour, David M. Sobel, Laura E. Schulz, Tamar Kushnir, David Danks
- 2004
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16
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Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting
– John K. Kruschke
- 2006
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1
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Higher-order associative processing in hermissenda suggests multiple sites of neuronal modulation
– R F Rogers, L D Matzel, Neuronal Modulation, Ronald F. Rogers, Louis D. Matzell
- 1996
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5
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Timing and Partial Observability in the Dopamine System
– Nathaniel D. Daw, Aaron C. Courville, David S. Touretzky
- 2003
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18
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Acquisition and extinction in autoshaping
– Sham Kakade, Peter Dayan
- 2002
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Within-subjects Extinction and Renewal in Predictive Judgments
– M. Concepción, M. Concepcion Paredes-Olay, Juan M. Rosas
- 1999
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18
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Is there a geometric module for spatial orientation? Squaring theory and evidence
– Ken Cheng, Nora S. Newcombe
- 2005
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Foraging Through Prediction
– Peter Dayan, P Read Montague, Terrence J Sejnowski, P Read, Montague Terrence, J Sejnowski
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Reinforcement Learning
– Ar Ni Ng
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