CogPonder: Towards a Computational Framework of General Cognitive Control

Current computational models of cognitive control exhibit notable limitations. In machine learning, artificial agents are now capable of performing complex tasks but often ignore critical constraints such as resource limitations and how long it takes for the agent to make decisions and act. Conversely, cognitive control models in psychology are limited in their ability to tackle complex tasks (e.g., play video games) or generalize across a battery of simple cognitive tests. Here we introduce CogPonder, a flexible, differentiable, cognitive control framework that is inspired by the Test-Operate-Test-Exit (TOTE) architecture in psychology and the PonderNet framework in machine learning. CogPonder functionally decouples the act of control from the controlled processes by introducing a controller that acts as a wrapper around any end-to-end deep learning model and decides when to terminate processing and output a response, thus producing both a response and response time. Our experiments show that CogPonder effectively learns from data to generate behavior that closely resembles human responses and response times in two classic cognitive tasks. This work demonstrates the value of this new computational framework and offers promising new research prospects for both psychological and computer sciences.