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. 2013 Dec 12;9(12):e1003383. doi: 10.1371/journal.pcbi.1003383

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© 2013 Penny et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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The task of model selection is for the agent to decide which environment it is in (hence the question mark in the above graphic). Top Left: North-east trajectory in maze 2, Top Right: North-east trajectory in maze 1. The mazes have different coloured east and west walls. The markers on the trajectories (A, B, C, D and E) denote locations corresponding to different time points ( and ). Bottom: The log likelihood ratio (of maze 1 versus maze 2), , as a function of the number of time points along the trajectory. At n = 1000, the LogLR is approximately 3. This allows the agent to infer, with 95% probability, that it is located in maze 1 rather than maze 2.

Inline graphic — The task of model selection is for the agent to decide which environment it is in (hence the question mark in the above graphic). Top Left: North-east trajectory in maze 2, Top Right: North-east trajectory in maze 1. The mazes have different coloured east and west walls. The markers on the trajectories (A, B, C, D and E) denote locations corresponding to different time points ( and ). Bottom: The log likelihood ratio (of maze 1 versus maze 2), , as a function of the number of time points along the trajectory. At n = 1000, the LogLR is approximately 3. This allows the agent to infer, with 95% probability, that it is located in maze 1 rather than maze 2.