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The allure of Bayesian statistics: How can it unlock the mysteries of our perception?
With the rapid development of neuroscience, the concept of Bayesian statistics has attracted more and more attention in analyzing the function of the brain. Researchers are trying to answer a fundamental question: How the brain reasons and makes decisions in situations filled with uncertainty. This is not only a mathematical challenge, but also a key to uncovering the processes behind our perception and behavior.
Source
The historical roots of this research field can be traced back to multiple disciplines such as machine learning, experimental psychology, and Bayesian statistics. As early as the 1860s, Hermotz began studying how the human brain extracts perceptual information from sensory data. The basic idea is that the nervous system needs to organize sensory data into accurate internal models in order to understand the external world. Bayesian probability theory was developed by many important contributors, such as Laplace and Thomas Bayes, who have made great contributions to this. In 1988, Edwin Jens proposed a framework for using Bayesian probability to model psychological processes, which led to Bayesian statistical frameworks being considered to have the potential to provide deep insights into the functioning of the nervous system. This concept has been further developed in research on unsupervised learning, particularly in analytic synthesis methods.
Psychophysics
The results of many psychophysical experiments have been interpreted in terms of Bayesian models of perception. Many aspects of human perception and motor behavior can be modeled using Bayesian statistics. This approach emphasizes behavioral outcomes as the final manifestation of neural information processing and is known as the use of Bayesian decision theory to model sensory and motor decisions.
Representative works in this area include Landy, Jacobs, Jordan, Knill, Kording, Wolpert and Goldreich, etc.
Neural coding
Many theoretical studies explore how neural systems implement Bayesian algorithms. Typical work in this area includes the research of Pouget, Zemel, Deneve, Latham, Hinton and Dayan. George and Hawkins published an article establishing a cortical information processing model called hierarchical temporal memory, which is based on a Bayesian Markov chain network. They further mapped this mathematical model onto existing knowledge of cortical architecture, showing how neurons recognize patterns through hierarchical Bayesian inference.
Electrophysiology
Some recent electrophysiological studies have focused on the representation of probability in the nervous system. The work of Shadlen and Schultz is an important representative in this field.
Predictive coding
Predictive coding is a biologically sound scheme for inferring the cause of sensory input based on minimizing prediction errors. These schemes are formally related to Kalman filtering and other Bayesian update schemes.
Free energy
In the 1990s, researchers like Geoff Heaton and Carl Friston began to study the concept of free energy, a computable and feasible way to measure the relationship between real-world characteristics and neural network models. The differences between the captured feature representations. Carl Friston has recently attempted a unification within a framework in which Bayesian brains derive from a general principle of free energy minimization. Within this framework, action and perception are seen as the result of suppressed free energy, leading to perceptual reasoning and a more subjective Bayesian view of the brain.
Friston said: "The free energy considered here represents the limit of any degree of surprise in exchange with the environment, the expectation of which is encoded by the state or configuration."
The latest development in this field of research is succinctly presented in a 2008 New Scientist article, describing a unified theory of brain function. Friston made the following statement about the explanatory power of this theory:
"This model of brain function can explain the anatomical and physiological characteristics of multiple brain systems."
Although the scientific community's exploration in this field continues, it is undeniable that Bayesian statistics provides a new window for uncovering the mysteries of the human brain. In this challenging research field, how many unknown depths do you think our perception still has to explore?
