Model of human cognition

TL;DR

This paper introduces a neuro-theoretical framework that aims to explain human cognition and improve AI by making it more explainable, efficient, and biologically plausible, addressing key limitations of current large language models.

Contribution

It presents a novel neuro-theoretical model that offers insights into cognitive processes and a computational approach for developing explainable, generalizable AI systems.

Findings

Provides a theoretical understanding of decision-making and problem solving.

Offers a computationally efficient method for AI development.

Enhances explainability and biological plausibility of AI models.

Abstract

The development of large language models (LLMs) is limited by a lack of explainability, the absence of a unifying theory, and prohibitive operational costs. We propose a neuro-theoretical framework for the emergence of intelligence in systems that is both functionally robust and biologically plausible. The model provides theoretical insights into cognitive processes such as decision-making and problem solving, and a computationally efficient approach for the creation of explainable and generalizable artificial intelligence.