Metric information in cognitive maps: Euclidean embedding of non-Euclidean environments
Fig 5
(a-c): Dataset “route-finding and shortcuts”, (d-f): Dataset “rips and folds”. (a) Shortcut predictions of the non-metric labeled graph (dotted lines) and average subject estimates (solid lines), plotted on ground truth coordinates. The gray vertices show how the graph would continue on routes through wormholes. (b) Shortcut predictions of the embedded graph, lines as in (a). The subject estimates were rotated to match the local orientation of the originating maze arm. (c) Distribution of the prediction error. The difference between the models is not significant, i.e., they predict the data equally well. (d) Example shortcut predictions (dotted lines) and subject estimates (solid lines) for three of the 24 object pairs in the “rips and folds” dataset. (e) Shortcut predictions of the embedded graph for the same object pairs as in (d). (f) Distribution of the prediction error. The difference between the models is also not significant on this dataset.