Emotions, diffusive emotional control and the motivational problem for autonomous cognitive systems

TL;DR

This paper explores how emotions and diffusive emotional control can address the motivational problem in autonomous cognitive systems, proposing a neural network model inspired by biological emotional processes.

Contribution

It introduces a novel approach linking emotions, meta-learning, and action strategies through a dense associative network for artificial intelligence.

Findings

Emotions are linked to neuromodulators that facilitate meta-learning.

Universal emotional drives can enhance autonomous decision-making.

The proposed model demonstrates potential for adaptive behavior in AI systems.

Abstract

All self-active living beings need to solve the motivational problem: The question what to do at any moment of their live. For humans and non-human animals at least two distinct layers of motivational drives are known, the primary needs for survival and the emotional drives leading to a wide range of sophisticated strategies, such as explorative learning and socializing. Part of the emotional layer of drives has universal facets, being beneficial in an extended range of environmental settings. Emotions are triggered in the brain by the release of neuromodulators, which are, at the same time, the agents for meta-learning. This intrinsic relation between emotions, meta-learning and universal action strategies suggests a central importance for emotional control for the design of artificial intelligences and synthetic cognitive systems. An implementation of this concept is proposed in terms of a dense and homogeneous associative network (dHan).