Goal-Driven Cognition in the Brain: A Computational Framework

TL;DR

This paper proposes a computational framework emphasizing goal-driven cognition as primary in the brain, contrasting with traditional reinforcement learning models, and explains its implications for behavior and clinical disorders.

Contribution

It introduces a novel two-phase goal-driven model of cognition, integrating biological and computational insights, and offers testable predictions for brain function and disorders.

Findings

Goal-driven cognition is organized into goal selection and goal engagement phases.

The model explains phenomena in clinical disorders like depression and OCD.

Provides a computational basis for dopamine and limbic system functions.

Abstract

Current theoretical and computational models of dopamine-based reinforcement learning are largely rooted in the classical behaviorist tradition, and envision the organism as a purely reactive recipient of rewards and punishments, with resulting behavior that essentially reflects the sum of this reinforcement history. This framework is missing some fundamental features of the affective nervous system, most importantly, the central role of goals in driving and organizing behavior in a teleological manner. Even when goal-directed behaviors are considered in current frameworks, they are typically conceived of as arising in reaction to the environment, rather than being in place from the start. We hypothesize that goal-driven cognition is primary, and organized into two discrete phases: goal selection and goal engaged, which each have a substantially different effective value function. This dichotomy can potentially explain a wide range of phenomena, playing a central role in many clinical disorders, such as depression, OCD, ADHD, and PTSD, and providing a sensible account of the detailed biology and function of the dopamine system and larger limbic system, including critical ventral and medial prefrontal cortex. Computationally, reasoning backward from active goals to action selection is more tractable than projecting alternative action choices forward to compute possible outcomes. An explicit computational model of these brain areas and their function in this goal-driven framework is described, as are numerous testable predictions from this framework.