Encoding geometric and non-geometric information: a study with evolved agents.
Vertebrate species use geometric information and non-geometric or featural cues to orient. Under some circumstances, when both geometric and non-geometric information are available, the geometric information overwhelms non-geometric cues (geometric primacy). In other cases, we observe the inverse tendency or the successful integration of both cues. In past years, modular explanations have been proposed for the geometric primacy: geometric and non-geometric information are processed separately, with the geometry module playing a dominant role. The modularity issue is related to the recent debate on the encoding of geometric information: is it innate or does it depend on environmental experience? In order to get insight into the mechanisms that cause the wide variety of behaviors observed in nature, we used Artificial Life experiments. We demonstrated that agents trained mainly with a single class of information oriented efficiently when they were exposed to one class of information (geometric or non-geometric). When they were tested in environments that contained both classes of information, they displayed a primacy for the information that they had experienced more during their training phase. Encoding and processing geometric and non-geometric information was run in a single cognitive neuro-representation. These findings represent a theoretical proof that the exposure frequency to different spatial information during a learning/adaptive history could produce agents with no modular neuro-cognitive systems that are able to process different types of spatial information and display various orientation behaviors (geometric primacy, non-geometric primacy, no primacy at all).
Ponticorvo
Michela
M
Laboratory of Artificial and Natural Cognition, Department of Relational Sciences, University of Naples "Federico II", Via Porta di Massa 1, 80133 Naples, Italy. michela.ponticorvo@unina.it
Miglino
Orazio
O
D000223:Adaptation, Psychological; D000818:Animals; D001185:Artificial Intelligence; D005075:Biological Evolution; D003198:Computer Simulation; D003463:Cues; D016571:Neural Networks, Computer; D021641:Recognition, Psychology; D013028:Space Perception; D013037:Spatial Behavior