Title
Spatial Memory of Paths Using Circular Probability Distributions: Theoretical Properties, Navigation Strategies and Orientation Cue Combination.
Abstract
We propose a mathematical model of the Path Integration (PI) process. Its core assumption is that orientations of a path are summarized by circular probability distributions. We compare our model with classical, deterministic models of PI and find that, although they are indistinguishable in terms of information encoded, the probabilistic model is more parsimonious when considering navigation strategies. We show how sensory events can enrich the probability distributions memorized, resulting in a continuum of navigation strategies, from PI to stimulus-triggered response. We analyze the combination of circular probability distributions (e.g., multicue fusion), and demonstrate that, contrary to the linear case, adding orientation cues does not always increase reliability of estimates. We discuss experimental predictions entailed by our model.
Year
DOI
Venue
2013
10.1080/13875868.2012.756490
SPATIAL COGNITION AND COMPUTATION
Keywords
Field
DocType
Bayesian modeling,spatial memory,von Mises probability distribution,path integration,cue combination
Convolution of probability distributions,Data mining,Path integration,Bayesian inference,Computer science,Algorithm,Probability distribution,Statistical model,Artificial intelligence
Journal
Volume
Issue
ISSN
13.0
3
1387-5868
Citations 
PageRank 
References 
0
0.34
13
Authors
3
Name
Order
Citations
PageRank
Julien Diard16210.72
Pierre Bessière242586.40
Alain Berthoz327433.20