Cognitive flexibility versus stability via activation-based and weight-based adaptations.

Humans are remarkably efficient at adapting to different contextual demands by exerting optimal levels of cognitive flexibility versus stability for switching between different tasks. However, empirical findings document that these control adaptation processes can occur across multiple timescales, suggesting that different computational mechanisms may be involved. Here, we developed a recurrent neural network model to simulate behavioral indices of cognitive flexibility versus stability and investigated its dynamics. Throughout four simulation studies, we revealed that control adaptations that benefit from recent control states (activation-based) can support fast adjustments in cognitive flexibility, whereas control adaptations that depend on changes in control settings (weight-based) evolve more slowly over learning. Interestingly, like humans, the model further learned to associate contextual features to different control settings and use this knowledge to shift along a flexibility-stability continuum when encountering these same contexts. We further verified model-specific predictions through an analysis of an existing human dataset (N = 102). In sum, our work integrates fast and slow control adaptations within a unified framework and advances our understanding on human adaptative behavior and its regulatory mechanisms through the lens of activation-based versus weight-based adaptations.