ఇంటర్నేషనల్ జర్నల్ ఆఫ్ స్కూల్ అండ్ కాగ్నిటివ్ సైకాలజీ
అందరికి ప్రవేశం

నైరూప్య

Bayesian Brain: Computation with Perception to Recognize 3D Objects

Kumar Sankar Ray*

We mimic the cognitive ability of Human perception, based on Bayesian hypothesis, to recognize view-based 3D objects. We consider approximate Bayesian (Empirical Bayesian) for perceptual inference for recognition. We essentially handle computation with perception. Recent development in neuroscience indicates human perception can be represented by Bayesian inference. Bayesian models can perform variety of perceptual task; thus we should have an instrumentalist view towards Bayesian models in the context of neuroscience. Bayesian models are very effective to secure both subjects’ perceptual activity and capture features of the human neural mechanism. Bayesian model can be used to study brain’s various perceptual tasks. In this design study to represent the perceptual task in Bayesian approach we consider beta distribution for computation of prior, like-lihood and posterior probability. Due to the computational simplicity we consider beta distribution as stated above. The basic aim of this article is to demonstrate that computation with perception is Bayesian inference and essentially we express the perception as an optimal hypothesis in terms of resulting belief obtained from sensory data (likelihood data). Recently Bayesian approach achieved tremendous success in the field of computer vision. Thus it leads to model human visual perception and allows an observer to perceive the world.