A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

TL;DR

This review explores neuroscience and cognitive psychology insights across multiple levels of brain function to inspire advancements in artificial general intelligence, emphasizing the integration of abstract reasoning and causal understanding.

Contribution

It provides a comprehensive overview of biological and cognitive models that could inform the development of more human-like AI systems.

Findings

Neuroscience insights can inform AI development.

Cognitive models highlight key reasoning capabilities.

Multi-level brain function exploration offers new AI inspiration.

Abstract

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.