Bayesian models: the structure of the world, uncertainty, behavior, and the brain

Abstract

Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.

Updating our Selves: Synthesizing Philosophical and Neurobiological Perspectives on Incorporating New Information into our Worldview

“…Indeed, perceptual biases such as optical illusions can be explained in terms of Bayesian inference [28,29]: our expectations, represented by priors, influence our perception of the outside world. While these data support the idea that the brain employs Bayesian inference, and there exists an emerging literature on the neural basis of decision-making based on sensory evidence and priors [30][31][32], less is known about how priors are updated based on new information -the issue most relevant to pro-attitude incorporation.…”

Stimulus uncertainty enhances long-term potentiation-like plasticity in human motor cortex

“…Bayesian models suggest the brain relies on both prior and current sensory information to create the best estimate of the current state of the world (Vilares & Kording, 2011). According to this framework, perceptual learning is described as a process of updating the prior distribution based on the current inferred posterior distribution (Knill & Pouget, 2004).…”

GIRL special issue introduction

“…One issue of the approach is the computational intractability of Bayesian rationality, which is usually dismissed by arguing that rational analysis rationalizes behavior on the assumption that human agents behave 'as if' they were perfect Bayesian agents, and nothing more. The success of this strategy hinges crucially on what 'as if' can be taken to mean, and this issue has been hotly debated in the recent years (Jones and Love 2011;Vilares and Kording 2011;Bowers and Davis 2012).…”