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Dynamics of Reward Based Decision Making: A Computational Study

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9886))

Abstract

We consider a biologically plausible model of the basal ganglia that is able to learn a probabilistic two armed bandit task using reinforcement learning. This model is able to choose the best option and to reach optimal performances after only a few trials. However, we show in this study that the influence of exogenous factors such as stimuli salience and/or timing seems to prevail over optimal decision making, hence questioning the very definition of action-selection. What are the ecological conditions for optimal action selection?

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References

  1. Chelazzi, L., Perlato, A., Santandrea, E., Della Libera, C.: Rewards teach visual selective attention. Vision. Res. 85, 58–72 (2013)

    Article  Google Scholar 

  2. Gurney, K., Prescott, T.J., Redgrave, P.: A computational model of action selection in the basal ganglia. I. A new functional anatomy. Biol. Cybern. 84(6), 401–410 (2001)

    Article  MATH  Google Scholar 

  3. Gurney, K., Prescott, T.J., Redgrave, P.: A computational model of action selection in the basal ganglia. II. Analysis and simulation of behaviour. Biol. Cybern. 84(6), 411–423 (2001)

    Article  MATH  Google Scholar 

  4. Guthrie, M., Leblois, A., Garenne, A., Boraud, T.: Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study. J. Neurophysiol. 109(12), 3025–3040 (2013)

    Article  Google Scholar 

  5. Leblois, A., Boraud, T., Meissner, W., Bergman, H., Hansel, D.: Competition between feedback loops underlies normal and pathological dynamics in the basal ganglia. J. Neurosci. 26, 3567–3583 (2006)

    Article  Google Scholar 

  6. Mogami, T., Tanaka, K.: Reward association affects neuronal responses to visual stimuli in macaque te and perirhinal cortices. J. Neurosci. 26(25), 6761–6770 (2006)

    Article  Google Scholar 

  7. Mormann, M.M., Navalpakkam, V., Koch, C., Rangel, A.: Relative visual saliency differences induce sizable bias in consumer choice. J. Consum. Psychol. 22(1), 67–74 (2012)

    Article  Google Scholar 

  8. Pasquereau, B., Nadjar, A., Arkadir, D., Bezard, E., Goillandeau, M., Bioulac, B., Gross, C.E., Boraud, T.: Shaping of motor responses by incentive values through the basal ganglia. J. Neurosci. 27(5), 1176–1183 (2007)

    Article  Google Scholar 

  9. Pooresmaeili, A., Bach, D.R., Dolan, R.J.: The effect of visual salience on memory-based choices. J. Neurophysiol. 111(3), 481–487 (2014)

    Article  Google Scholar 

  10. Redgrave, P., Prescott, T.J., Gurney, K.: The basal ganglia: a vertebrate solution to the selection problem? Neuroscience 89(4), 1009–1023 (1999)

    Article  Google Scholar 

  11. Schmidt, R., Leventhal, D.K., Mallet, N., Chen, F., Berke, J.D.: Canceling actions involves a race between basal ganglia pathways. Nat. Neurosci. 16(8), 1118–1124 (2013)

    Article  Google Scholar 

  12. Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  13. Topalidou, M., Rougier, N.: [re] interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study. ReScience 1(1) (2015)

    Google Scholar 

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Acknowledgements

The authors would like to acknowledge the funds received from Centre Franco-Indien pour la Promotion de la Recherche Avance (CEFIPRA) under the project DST-INRIA 2013-02/Basal Ganglia.

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Correspondence to Bhargav Teja Nallapu .

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© 2016 Springer International Publishing Switzerland

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Nallapu, B.T., Rougier, N.P. (2016). Dynamics of Reward Based Decision Making: A Computational Study. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_38

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

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