Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks

Working memory (WM) is thought to rely on sophisticated frontostriatal mechanisms for selective gating, supporting updating and readout of information to and from distinct memory "addresses" (neural populations). According to computational models, capacity limitations in WM arise due to challenges in "role addressability": learning to correctly bind items in memory to their respective roles and assigning credit to the corresponding gating operations. However, these conclusions are based on particular assumptions about biological neural networks, and it is unclear whether the principles generalize to other architectures. To address this question, we examine whether similar gating mechanisms emerge in Transformer neural network architectures, which have demonstrated success on tasks requiring executive function-the ability to represent, coordinate, and manage multiple subtasks-yet lack intentionally built-in gating mechanisms. We analyze the mechanisms that emerge within Transformers trained on human WM tasks explicitly designed to place demands on gating. We find that the Transformer's attention mechanism develops role-addressable input and output gating, but only when trained on task distributions that benefit from frontostriatal-like gating mechanisms. Moreover, these gating strategies support enhanced generalization and variable binding and increase the models' effective capacity to store and access multiple items in memory, resembling the constraints found in frontostriatal models. These results suggest that gating mechanisms serve a fundamental computational role in managing role addressability and binding and highlight opportunities for future research on computational similarities between modern artificial intelligence architectures and models of the human brain.