## Bayesian models of PI and search

This post is written by Rob Vickerstaff.

In our new paper Tobias and I present a Bayesian-statistical model of an ant searching for its nest using path integration (PI) as its only navigation aid, and compare the resulting search patterns to real Cataglyphis fortis search patterns and to three simpler search strategies. The ant search patterns were collected by Tobias, and we decided to use only searches by so-called zero-vector ants (those just emerging from their nest whose PI system was therefore known to be reset) because for these we knew there was minimal positional error in the PI system when the search began.

Our Bayesian search model might be called “semi-optimal” in that the model assumes a perfect memory of where search effort has been expended, but does not employ a sophisticated planning algorithm. Instead the ant is assumed to think only one step ahead, and choose the greatest immediate probability of finding the nest based on its current Bayesian probability distribution function. This simple rule is surprisingly robust, and outperforms the three simpler search methods in efficiency, and is the most ant-like in appearance.

A key feature of the model is that it automatically adapts to changes in positional uncertainty, a feature it shares with the ants which produce a broader search pattern the longer the preceding excursion has been.

Vickerstaff RJ and Merkle T (2012) Path integration mediated systematic search: A Bayesian model. J Theor Biol 307: 1-19

http://dx.doi.org/10.1016/j.jtbi.2012.04.034