Abstract
A multi-dimensional stimulus can elicit a range of responses depending on which dimension or combination of dimensions is considered. Such selection can be implicit, providing a fast and automatic selection, or explicit, providing a slower but contextualized selection. Both forms are important but do not derive from the same processes. Implicit selection results generally from a slow and progressive learning that leads to a simple response (concrete/first-order) while explicit selection derives from a deliberative process that allows to have more complex and structured response (abstract/second-order). The prefrontal cortex (PFC) is believed to provide the ability to contextualize concrete rules that leads to the acquisition of abstract rules even though the exact mechanisms are still largely unknown. The question we address in this paper is precisely about the acquisition, the representation and the selection of such abstract rules. Using two models from the literature (PBWM and HER), we explain that they both provide a partial but differentiated answer such that their unification offers a complete picture.
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References
Alexander, W.H., Brown, J.W.: Hierarchical error representation: a computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Comput. 27(11), 2354–2410 (2015)
Alexander, W.H., Brown, J.W.: Frontal cortex function as derived from hierarchical predictive coding. Sci. Rep. 8(1), 3843 (2018). https://doi.org/10.1038/s41598-018-21407-9
Badre, D.: Cognitive control, hierarchy, and the rostro-caudal organization of the frontal lobes. Trends Cogn. Sci. 12(5), 193–200 (2008)
Badre, D., Kayser, A.S., D’Esposito, M.: Frontal cortex and the discovery of abstract action rules. Neuron 66(2), 315–326 (2010)
Daw, N.D., Niv, Y., Dayan, P.: Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8(12), 1704–1711 (2005). https://doi.org/10.1038/nn1560
Domenech, P., Koechlin, E.: Executive control and decision-making in the prefrontal cortex. Curr. Opin. Behav. Sci. 1, 101–106 (2015). https://doi.org/10.1016/j.cobeha.2014.10.007
Frank, M.J., Badre, D.: Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cereb. Cortex 22(3), 509–526 (2012)
Koechlin, E., Ody, C., Kouneiher, F.: The architecture of cognitive control in the human prefrontal cortex. Science 302(5648), 1181–1185 (2003)
Miller, E.K., Cohen, J.D.: An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24(1), 167–202 (2001)
O’Reilly, R.C., Frank, M.J.: Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 18(2), 283–328 (2006)
Tenenbaum, J.B., Kemp, C., Griffiths, T.L., Goodman, N.D.: How to grow a mind: statistics, structure, and abstraction. Science 331(6022), 1279–1285 (2011)
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Dagar, S., Alexandre, F., Rougier, N. (2022). From Concrete to Abstract Rules: A Computational Sketch. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds) Brain Informatics. BI 2022. Lecture Notes in Computer Science(), vol 13406. Springer, Cham. https://doi.org/10.1007/978-3-031-15037-1_2
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DOI: https://doi.org/10.1007/978-3-031-15037-1_2
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