Technical Program

Paper Detail

Paper: PS-2B.51
Session: Poster Session 2B
Location: H Fläche 1.OG
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Simple Associative Learning Accounts for the Complex Dynamics of Operant Extinction
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: José R. Donoso, Zhiyin Lederer, Julian Packheiser, Roland Pusch, Thomas Walther, Onür Güntürkün, Sen Cheng, Ruhr-Universität Bochum, Germany
Abstract: Extinction Learning (EL), the ability of changing a previously acquired behaviour as a result of altered reinforcement contingencies, is essential for a successful adaptation to a changing environment. EL is specific to the context in which it occurred, as evidenced by the so-called renewal of the extinguished behaviour after returning to the original context where conditioning took place (ABA renewal). Here, we analyse single learning curves obtained from pigeons performing an operant conditioning task within an ABA paradigm. The curves exhibit a stunning diversity both across subjects and within a subject across sessions. We find that a computational model with simple sensorimotor associations and a winner-takes-all decision process can surprisingly account for most of the peculiar features in the behaviour. Our model suggests that the complexity of behaviour stems from the history of context and rewarded responses within and across sessions. In conclusion, our work demonstrates how studying the dynamics of learning can reveal previously unappreciated nuances in the behaviour and how even simple models can generate complex, apparently purposeful behaviour.