[Paper Review] Unveiling the mystery of visual information processing in human brain

Emanuel Diamant|ArXiv.org|Jul 2, 2008

Image Retrieval and Classification Techniques3 citations

TL;DR

This paper proposes a novel definition of visual information based on Algorithmic Information Theory (AIT) and Kolmogorov complexity, arguing that traditional neuroscience has overlooked the fundamental question of what 'information' actually means in visual processing. By redefining information as algorithmic complexity, the author challenges long-standing assumptions in brain research and calls for a paradigm shift to better model human visual perception objectively and systematically.

ABSTRACT

It is generally accepted that human vision is an extremely powerful information processing system that facilitates our interaction with the surrounding world. However, despite extended and extensive research efforts, which encompass many exploration fields, the underlying fundamentals and operational principles of visual information processing in human brain remain unknown. We still are unable to figure out where and how along the path from eyes to the cortex the sensory input perceived by the retina is converted into a meaningful object representation, which can be consciously manipulated by the brain. Studying the vast literature considering the various aspects of brain information processing, I was surprised to learn that the respected scholarly discussion is totally indifferent to the basic keynote question: "What is information?" in general or "What is visual information?" in particular. In the old days, it was assumed that any scientific research approach has first to define its basic departure points. Why was it overlooked in brain information processing research remains a conundrum. In this paper, I am trying to find a remedy for this bizarre situation. I propose an uncommon definition of "information", which can be derived from Kolmogorov's Complexity Theory and Chaitin's notion of Algorithmic Information. Embracing this new definition leads to an inevitable revision of traditional dogmas that shape the state of the art of brain information processing research. I hope this revision would better serve the challenging goal of human visual information processing modeling.

Motivation & Objective

• To address the critical omission in neuroscience of defining 'information' in visual processing.
• To challenge the prevailing assumption that visual perception is understood without clarifying the nature of information.
• To propose a new theoretical framework based on Algorithmic Information Theory (AIT) and Kolmogorov complexity.
• To reformulate the foundational principles of visual information processing in the human brain.
• To enable more rigorous, objective modeling of human visual perception by grounding it in a mathematically coherent definition of information.

Proposed method

• Proposes a redefinition of 'information' using Chaitin's and Kolmogorov's Algorithmic Information Theory.
• Defines visual information as the algorithmic complexity of a perceptual pattern, i.e., the length of the shortest program that can generate it.
• Applies this definition to neural processing pathways from retina to cortex, treating visual input as algorithmic objects.
• Argues that conscious object representation arises from the compression of sensory input into minimal algorithmic descriptions.
• Reframes existing neuroscientific models by requiring that any theory of visual processing must first define what constitutes information.
• Calls for a paradigm shift in brain research by insisting that all models of visual processing must be built on a clear, formal definition of information.

Experimental results

Research questions

• RQ1 What is the fundamental nature of visual information in the human brain?
• RQ2 Why has the neuroscience community failed to define 'information' in visual processing despite decades of research?
• RQ3 How can Algorithmic Information Theory provide a more rigorous foundation for modeling visual perception?
• RQ4 What are the implications of redefining information as algorithmic complexity for existing models of cortical processing?
• RQ5 How can a formal definition of information improve the objective modeling of human visual cognition?

Key findings

• The paper identifies a critical gap in neuroscience: the absence of a formal definition of 'information' in visual processing research.
• It argues that the lack of a clear definition undermines the scientific rigor of models in visual perception and cognition.
• By adopting algorithmic complexity as the definition of information, the paper provides a mathematically grounded alternative to traditional, vague notions of information.
• The proposed framework implies that visual perception involves the compression of sensory input into minimal algorithmic descriptions, aligning with principles of efficient coding.
• The author concludes that current models of visual processing are built on unexamined assumptions and must be revised to incorporate a formal definition of information.
• The paper positions its redefinition as essential for advancing objective, testable models of human visual information processing.