The neural and cognitive architecture for learning from a small sample

TL;DR

This paper proposes a model where higher cognitive functions interact with reinforcement learning to reduce search space complexity, enabling efficient learning from few examples, similar to human intelligence.

Contribution

It introduces a novel framework combining cognitive functions with reinforcement learning to improve learning efficiency from small samples.

Findings

Model reduces search space complexity

Enhances learning efficiency from few exemplars

Bridges cognitive science and AI learning methods

Abstract

Artificial intelligence algorithms are capable of fantastic exploits, yet they are still grossly inefficient compared with the brain's ability to learn from few exemplars or solve problems that have not been explicitly defined. What is the secret that the evolution of human intelligence has unlocked? Generalization is one answer, but there is more to it. The brain does not directly solve difficult problems, it is able to recast them into new and more tractable problems. Here we propose a model whereby higher cognitive functions profoundly interact with reinforcement learning to drastically reduce the degrees of freedom of the search space, simplifying complex problems and fostering more efficient learning.