Bayesian models of object perception

2015

Perception, 2019

The general lines of Bayesian modeling (BM) in the study of perception are outlined here. The main thesis argued here is that BM works well only in the so-called secondary processes of perception, and in particular in cases of imperfect discriminability between stimuli, or when a judgment is required, or in cases of multistability. In cases of "primary processes," on the other hand, it is often arbitrary and anyway superfluous, as with the laws of Gestalt. However, it is pointed out that in these latter cases, simpler and more well-established methodologies already exist, such as signal detection theory and individual choice theory. The frequent recourse to arbitrary values of a priori probabilities is also open to question.

1. The single cue case. Example: perceiving slant from texture.

Dedication We dedicate this book to the memory of David Knill (1961-2014). All three of us have learned a good part of what we know about Bayesian modeling of perception and action from him. As a caring and patient mentor and as an excellent teacher, he also made studying this topic a lot more enjoyable for all of us. The field of Bayesian modeling of perception and action would not be where it is without him and this book would probably never have been written. This book has been a long time in the making, and we are indebted to many people. We first came up with the idea in June 2009, when-together with Alan Stocker and Jonathan Pillow-we taught a computational neuroscience course at the Instituto Gulbenkian de Ciência in Oeiras, Portugal. At the time, in an impressive display of unbridled optimism, K.K. predicted that we would be done by December 2009. A short 13 years later, we have the book in hand. The delay has come with benefits, though: over the years, we have used chapter drafts and the book's ideas to teach Bayesian modeling to hundreds of undergraduate students, graduate students, and postdocs in our courses at

Wiley interdisciplinary reviews. Computational statistics

Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. This paper provides a review of the most relevant models developed in recent years. We divide methods according to the objective of the analysis. We start from spatio-temporal models for fMRI data that detect task-related activation patterns. We then address the very important problem of estimating brain connectivity. We also touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We conclude with a ...

Medical Imaging 2000: Image Processing, 2000

ABSTRACT

Information Sciences and …

Hierarchical generative models and Bayesian belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. The complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing models are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction.

Perception as Bayesian Inference

International Journal of Computer Vision, 1990

2005

ABSTRACT A fundamental question in visual neuroscience is: Why are the receptive fields and response properties of visual neurons as they are? A modern approach to this problem emphasizes the importance of adaptation to ecologically valid input. In this paper, we will review work on modelling statistical regularities in ecologically valid visual input (“natural images”) and the obtained functional explanation of the properties of visual neurons.