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• W2320408901 abstract "A recent associative model (Miller, N.Y., & Shettleworth, S.J., 2007. Learning about environmental geometry: An associative model. Journal of Experimental Psychology: Animal Behavior Processes B, 33, 191-212) is an influential mathematical account of how agents behave when reorienting to previously learned locations in spatial arenas. However, it is mathematically and empirically flawed. The current article explores these flaws, including its inability to properly predict geometric superconditioning. We trace the flaws to the model's mathematical structure and how it handles inhibition. We then propose an operant artificial neural network model that solves these problems with inhibition and can correctly model both reorientation and superconditioning." @default.
• W2320408901 created "2016-06-24" @default.
• W2320408901 creator A5053304894 @default.
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• W2320408901 date "2013-01-01" @default.
• W2320408901 modified "2023-09-26" @default.
• W2320408901 title "Differentiating models of associative learning: Reorientation, superconditioning, and the role of inhibition." @default.
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• W2320408901 doi "https://doi.org/10.1037/a0032174" @default.
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