Mutual Emotion-Cognition Dynamics

TL;DR

This paper introduces a new dynamical framework based on stable transient neural activity networks to better understand brain mental processes and their interactions, offering a formal and noise-resistant approach.

Contribution

It proposes a novel paradigm using transient dynamics to model brain activity, contrasting traditional methods, and provides a foundation for a quantitative theory of mental processes.

Findings

Transient neural networks are resistant to noise and stable.

The approach explains perception, cognition, and emotion as sequences of activity patterns.

Potential to develop artificial agents with human-like mental dynamics.

Abstract

We present a new paradigm in the study of brain mental dynamics on the basis of the stable transient activity neural networks observed in experiments. This new approach is in contrast to traditional system analysis usually adopted in cognitive modeling. Transient dynamics offers a sound formalism of the observed qualities of brain activity, while providing a rigorous set of analysis tools. Transients have two main features: First, they are resistant to noise, and reliable even in the face of small variations in initial conditions; the sequence of states visited by the system (its trajectory), is thus structurally stable. Second, the transients are input-specific, and thus convey information about what caused them in the first place. This new dynamical view manifests a rigorous explanation of how perception, cognition, emotion, and other mental processes evolve as a sequence of activity patterns in the brain, and, most importantly, how they interfere with each other. The ideas discussed and demonstrated here lead to the creation of a quantitative theory of the human mental activity and can be deployed on artificial agents.