| Paper: | PS-1B.11 | ||
| Session: | Poster Session 1B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | The relational structure of a reinforcement learning task is represented and generalised in the entorhinal cortex | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1193-0 | ||
| Authors: | Alon Baram, Timothy Muller, Hamed Nili, University of Oxford, United Kingdom; Mona Garvert, Max-Planck-Institute for Human Cognitive and Brain Sciences, Germany; Tim Behrens, University of Oxford, United Kingdom | ||
| Abstract: | The ability to appropriately generalise previously acquired knowledge to novel situations is a hallmark of human intelligence. A possible neural solution to this problem is to devote pools of neurons to represent the relations between entities in the environment explicitly, in a manner that is divorced from the entities themselves. Such an explicit representation can generalise to novel situations with the same relational structure. Grid cells, originally found in the entorhinal cortex, have been proposed as such an explicit representation of the relations between different locations in spatial space. However, the neural representations underlying the generalisation of relational structures in abstract tasks remain poorly understood. Here we use fMRI in humans to show that the entorhinal cortex explicitly represents the relations between reward-predicting stimuli in a reinforcement learning task with different underlying correlation structures between the reward probabilities associated with different stimuli. Our results demonstrate that the same brain regions, perhaps with the same mechanisms, represent the relational structure of the task in both spatial and abstract decision-making tasks. This suggests that the brain uses a common coding framework for the structure of tasks across a wide range of domains. | ||