- Preprint Article
- 10.7490/f1000research.1110679.1
Likelihood estimation of places in local environments
- Oct 02, 2015
- F1000Research
- Stephan Lancier + 1 more +1
Place recognition is based on long-term memory codes providing local position information. We propose a Maximum-Likelihood model of place recognition taking into account stored and perceived landmark distances and bearings. Stored landmark distance is assumed to be based on triangulation and is therefore veridical. Landmark distance perceived during homing are assumed to be hyperbolically compressed (Gilinsky, 1951). We evaluate the model with experimental data which was collected from place recognition experiments in a virtual reality setup. Three groups of participants learned a goal location within three different configurations of four distinguishable landmarks (parallelogram, irregular with large distance variation between landmarks, irregular with homogeneous distances). In the following test phase the participants navigated to the goal location, but the environment was now covered by “ground fog” removing all environmental information except the landmarks themselves. Error ellipses were elongated towards the most distant landmark and, in the irregular conditions, showed a systematic bias in the same direction. The model reproduces the ellipse orientations if we assume that distance measurements are less noisy for near distances (from about 30 m) than bearing measurements; it also reproduces the systematic biases due to the hyperbolic compression of perceived distances (Gilinsky’s A = 90 m).
Read more