Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories.

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.

The abilities to either flexibly adjust behavior according to changing demands (cognitive flexibility) or to maintain it in the face of potential distractors (cognitive stability) are critical for adaptive behavior in many situations. Recently, a novel human paradigm has found individual differences of cognitive flexibility and stability to be related to common prefrontal networks. The aims of the present study were, first, to translate this paradigm from humans to mice and, second, to test conceptual predictions of a computational model of prefrontal working memory mechanisms, the Dual State Theory, which assumes an antagonistic relation between cognitive flexibility and stability. Mice were trained in a touchscreen-paradigm to discriminate visual cues. The task involved “ongoing” and cued “switch” trials. In addition distractor cues were interspersed to test the ability to resist distraction, and an ambiguous condition assessed the spontaneous switching between two possible responses without explicit cues. While response times did not differ substantially between conditions, error rates (ER) increased from the “ongoing” baseline condition to the most complex condition, where subjects were required to switch between two responses in the presence of a distracting cue. Importantly, subjects switching more often spontaneously were found to be more distractible by task irrelevant cues, but also more flexible in situations, where switching was required. These results support a dichotomy of cognitive flexibility and stability as predicted by the Dual State Theory. Furthermore, they replicate critical aspects of the human paradigm, which indicates the translational potential of the testing procedure and supports the use of touchscreen procedures in preclinical animal research.