Abstract
A behavioural paradigm for learning arbitrary visuo-motor associations established that human observers learn to associate visual objects with their corresponding motor responses faster if the objects follow a temporal rule rather than if they were presented in a random order. Here, we use a simple recurrent network with a back propagation training algorithm adapted to a reinforcement learning scheme. Our simulations fit quantitatively as well as qualitatively to the behavioural results, endorsing the role of temporal context in associative learning scenarios.
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Acknowledgments
The authors acknowledge the support provided by the federal state Sachsen-Anhalt with the Graduiertenförderung (LGFG scholarship).
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Glüge, S., Hamid, O.H. & Wendemuth, A. A Simple Recurrent Network for Implicit Learning of Temporal Sequences. Cogn Comput 2, 265–271 (2010). https://doi.org/10.1007/s12559-010-9066-z
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DOI: https://doi.org/10.1007/s12559-010-9066-z