An analysis of bimanual actions in natural feeding of semi-wild chimpanzees

Objective The objective of the current study was to investigate the lateral dominance for a bimanually coordinated natural feeding behavior in semi-wild chimpanzees. Materials and Methods We investigated strychnos spp. fruit consumption behaviors in semi-wild chimpanzees as an ecologically comparable feeding behavior to those found in cerebral lateralization studies of non-primate species. Video recordings of thirty-three chimpanzees were assessed while they consumed hard-shelled strychnos fruits. We explored statistical and descriptive measures of hand dominance to highlight lateralized patterns. Results Statistical evaluation of feeding bouts revealed a group-level right-handed bias for bimanual coordinated feeding actions, however few individuals were statistically lateralized. Descriptive analyses revealed that the majority of individuals were lateralized and possessed a right-handed bias for strychnos feeding behavior. Discussion The results provide empirical evidence in supports of an early evolutionary delineation of function for the right and left hemispheres. The present findings suggest that great apes express an intermediate stage along the phylogenetic trajectory of human manual lateralization.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.