Abstract
Taking inspiration from neural principles of decision-making is of particular interest to help improve adaptivity of artificial systems. Research at the crossroads of neuroscience and artificial intelligence in the last decade has helped understanding how the brain organizes reinforcement learning (RL) processes (the adaptation of decisions based on feedback from the environment). The current challenge is now to understand how the brain flexibly regulates parameters of RL such as the exploration rate based on the task structure, which is called meta-learning [1] Doya, 2002). Here, we propose a computational mechanism of exploration regulation based on real neurophysiological and behavioral data recorded in monkey prefrontal cortex during a visuo-motor task involving a clear distinction between exploratory and exploitative actions. We first fit trial-by-trial choices made by the monkeys with an analytical reinforcement learning model. We find that the model which has the highest likelihood of predicting monkeys’ choices reveals different exploration rates at different task phases. In addition, the optimized model has a very high learning rate, and a reset of action values associated to a cue used in the task to signal condition changes. Beyond classical RL mechanisms, these results suggest that the monkey brain extracted task regularities to tune learning parameters in a task-appropriate way. We finally use these principles to develop a neural network model extending a previous cortico-striatal loop model. In our prefrontal cortex component, prediction error signals are extracted to produce feedback categorization signals. The latter are used to boost exploration after errors, and to attenuate it during exploitation, ensuring a lock on the currently rewarded choice. This model performs the task like monkeys, and provides a set of experimental predictions to be tested by future neurophysiological recordings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Doya, K.: Metalearning and neuromodulation. Neural Netw. 15(4-6), 495–506 (2002)
Barraclough, D., Conroy, M., Lee, D.: Prefrontal cortex and decision making in a mixed-strategy game. Nat. Neurosci. 7(4), 404–410 (2004)
Procyk, E., Tanaka, Y., Joseph, J.: Anterior cingulate activity during routine and non-routine sequential behaviors in macaques. Nat. Neurosci. 3(5), 502–508 (2000)
Aston-Jones, G., Cohen, J.: Adaptive gain and the role of the locus coeruleus-norepinephrine system in optimal performance. J. Comp. Neurol. 493(1), 99–110 (2005)
Brown, J., Braver, T.: Learned predictions of error likelihood in the anterior cingulate cortex. Science 307, 1118–1121 (2005)
Dosenbach, N.U., Visscher, K.M., Palmer, E.D., Miezin, F.M., Wenger, K.K., Kang, H.C., Burgund, E.D., Grimes, A.L., Schlaggar, B.L., Peterson, S.E.: A core system for the implementation of task sets. Neuron 50, 799–812 (2006)
Matsumoto, M., Matsumoto, K., Abe, H., Tanaka, K.: Medial prefrontal cell activity signaling prediction errors of action values. Nat. Neurosci. 10, 647–656 (2007)
Quilodran, R., Rothe, M., Procyk, E.: Behavioral shifts and action valuation in the anterior cingulate cortex. Neuron 57(2), 314–325 (2008)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Dominey, P., Arbib, M., Joseph, J.: A model of corticostriatal plasticity for learning oculomotor associations and sequences. Journal of Cognitive Neuroscience 7(3), 311–336 (1995)
Khamassi, M., Martinet, L., Guillot, A.: Combining self-organizing maps with mixture of epxerts: Application to an Actor-Critic model of reinforcement learning in the basal ganglia. In: Proceedings of the 9th International Conference on the Simulation of Adaptive Behavior (SAB), Rome, Italy, pp. 394–405. Springer, Heidelberg (2006)
Schultz, W., Dayan, P., Montague, P.: A neural substrate of prediction and reward. Science 275(5306), 1593–1599 (1997)
Gurney, K., Prescott, T., Redgrave, P.: A computational model of action selection in the basal ganglia. I. A new functional anatomy. Biol. Cybern. 84(6), 401–410 (2001)
Girard, B., Cuzin, V., Guillot, A., Gurney, K., Prescott, T.: A basal ganglia inspired model of action selection evaluated in a robotic survival task. Journal of Integrative Neuroscience 2(2), 179–200 (2003)
Procyk, E., Goldman-Rakic, P.: Modulation of dorsolateral prefrontal delay activity during self-organized behavior. J. Neurosci. 26(44), 11313–11323 (2006)
Dehaene, S., Changeux, J.: A neuronal model of a global workspace in effortful cognitive tasks. Proc. Natl. Acad. Sci. USA 95, 14529–14534 (1998)
Cohen, J., Aston-Jones, G., Gilzenut, S.: A systems-level perspective on attention and cognitive control. In: Posner, M. (ed.) Cognitive Neuroscience of Attention, pp. 71–90. Guilford Publications, New York (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khamassi, M., Quilodran, R., Enel, P., Procyk, E., Dominey, P.F. (2010). A Computational Model of Integration between Reinforcement Learning and Task Monitoring in the Prefrontal Cortex. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, JA., Mouret, JB. (eds) From Animals to Animats 11. SAB 2010. Lecture Notes in Computer Science(), vol 6226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15193-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-15193-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15192-7
Online ISBN: 978-3-642-15193-4
eBook Packages: Computer ScienceComputer Science (R0)