Place recognition from distant landmarks: human performance and maximum likelihood model

We present a simple behavioral experiment on human place recognition from a configuration of four visual landmarks. Participants were asked to navigate several paths, all involving a turn at one specific point, and while doing so incidentally learned the position of that turning point. In the test phase, they were asked to return to the turning point in a reduced environment leaving only the four landmarks visible. Results are compared to two versions of a maximum likelihood model of place recognition using either view-based or depth-based cues for place recognition. Only the depth-based model is in good qualitative agreement with the data. In particular, it reproduces landmark configuration-dependent effects of systematic bias and statistical error distribution as well as effects of approach direction. The model is based on a place code (depth and bearing of the landmarks at target location) and an egocentric working memory of surrounding space including current landmark position in a local, map-like representation. We argue that these elements are crucial for human place recognition.