A Hierarchical Computational Model Inspired by the Behavioral Control in the Primate Brain

• Main Authors: Dongqing Shi, Jerald Kralik, Haiyan Mi
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Cognitive hierarchical architecture; computational model; reinforcement learning; local and global planning
• Online Access: https://ieeexplore.ieee.org/document/9207882/

The basic cognitive architecture of our brain is still unknown. However, scientists have found evidence for existence of distinct behavioral control systems shared by humans and nonhumans. Inspired by the problem solving systems of the behavioral control in the primate brain, a hierarchical computational model is presented. We focus on the integrative performance of brain substructures, each of which is represented by a problem solver that is further modeled by a certain algorithm. Different levels of brain substructures, as well as the corresponding algorithms, are hierarchically organized both in structure and in function, including how and when higher-order solvers control lower-order ones. Different problem solvers share a same slice of working memory. This novelty is claimed since most of existing brain models emphasize on the neural network structure even though the neuron dynamics of brain is still very controversial. And we compare its performance to three other computational models in the face of a challenging foraging problem. Agents are examined in foraging environment with different sizes, and/or transparent barriers. The experimental results show that our model performed the best outright in most scenarios. Further, the results discover that the virtues of our primate brain lie not only in the heights of thinking it can reach, but also in its range and versatility.