Desires and Motivation: The Computational Rule, the Underlying Neural Circuitry, and the Relevant Clinical Disorders

TL;DR

This paper introduces the RGDM model, a neural network-based framework that captures desire-motivation dynamics, predicts personality types, and offers insights into psychiatric disorders.

Contribution

It develops a novel recurrent neural network model for desire-motivation processes and links it to personality and clinical disorder predictions.

Findings

RGDM model captures desire-motivation dynamics.

Predicts eight personality types based on three dimensions.

Identifies three new depressive disorder phenotypes.

Abstract

As organism is a dissipative system. The process from multi desires to exclusive motivation is of great importance among all sensory-action loops. In this paper we argued that a proper Desire-Motivation model should be a continuous dynamic mapping from the dynamic desire vector to the sparse motivation vector. Meanwhile, it should at least have specific stability and adjustability of motivation intensity. Besides, the neuroscience evidences suggest that the Desire-Motivation model should have dynamic information acquisition and should be a recurrent neural network. A five-equation model is built based on the above arguments, namely the Recurrent Gating Desire-Motivation (RGDM) model. Additionally, a heuristic speculation based on the RGDM model about corresponding brain regions is carried out. It believes that the tonic and phasic firing of ventral tegmental area dopamine neurons should execute the respective and collective feedback functions of recurrent processing. The analysis about the RGMD model shows the expectations about individual personality from three dimensions, namely stability, intensity, and motivation decision speed. These three dimensions can be combined and create eight different personalities, which is correspondent to Jung's personality structure theorem. Furthermore, the RGDM model can be used to predict three different brand-new types of depressive disorder with different phenotypes. Moreover, it can also explain several other psychiatry disorders from new perspectives.