Computational Investigations of Learning and Synchronization in Cognitive Control

Complex cognition requires binding together of stimulus, action, and other features, across different time scales. Several implementations of such binding have been proposed in the literature, most prominently synaptic binding (learning) and synchronization. Biologically plausible accounts of how these different types of binding interact in the human brain are still lacking. To this end, we adopt a computational approach to investigate the impact of learning and synchronization on both behavioral (reaction time, error rate) and neural (θ power) measures. We train four models varying in their ability to learn and synchronize for an extended period of time on three seminal action control paradigms varying in difficulty. Learning, but not synchronization, proved essential for behavioral improvement. Synchronization however boosts performance of difficult tasks, avoiding the computational pitfalls of catastrophic interference. At the neural level, θ power decreases with practice but increases with task difficulty. Our simulation results bring new insights in how different types of binding interact in different types of tasks, and how this is translated in both behavioral and neural metrics.