The origin of cognitive modules for face processing: A computational evolutionary perspective

Abstract

Despite extensive research, mechanisms underlying the emergence of cognitive modules remains elusive due to the complex interplay of genetic, developmental, and environmental factors. Computational modeling, however, provides a means of exploring their origins by simulating manipulations on these factors. In this study, we aimed to investigate the emergence of cognitive modules by developing the Dual-Task Meta-Learning Partitioned (DAMP) model, whose plastic architecture facilitates automatic structure optimization through a genetic algorithm that simulates natural selection by iteratively selecting for efficient learning fitness. We found that a specialized module for face identification robustly emerged in the DAMP model. Critically, the emergence of cognitive modules was not exclusive to faces in individual-level identification tasks. Rather, modular structures formed across all tested object categories in both categorization and identification tasks within our model. Interestingly, the formation of these modules was strongly influenced by the structural constraint of sparse connectivity within the network, suggesting that modularity may arise as an adaptation strategy to cope with the limitations imposed by sparse connections in biological neural networks. These findings provide a new evolutionary perspective on the development of cognitive modules in the human brain, highlighting the pivotal role of neural network structural properties in shaping cognitive functionality.