Open Access

Diana J. N. Armbruster-Genç

• The ability to flexibly adjust behavior according to a changing environment is crucial to ensure a species' survival. However, the successful pursuit of goals also requires the stable maintenance of behavior in the face of potential distractors. Thus, cognitive flexibility and cognitive stability are important processes for the cognitive control of behavior. There is a large body of behavioral and neuroimaging research concerning cognitive control in general, but also specifically on cognitive flexibility and cognitive stability, albeit most often assessed in separate task paradigms. Nevertheless, whether cognitive flexibility and cognitive stability depend upon separate or shared neuronal bases is still a matter of debate. Complementing empirical research, computational models have become an important strategy in neuroscientific research, as they have the potential of providing mechanistic explanations of empirical observations, for example by allowing for the direct manipulation of molecular parameters in simulated neural networks. The computational model underlying the so-called Dual-State Theory contains specific hypotheses with respect to cognitive flexibility and cognitive stability. The neural networks simulated by this model exhibit multiple stable firing states, i.e., the neural network can maintain a high firing state also without continuing external input due to a network architecture consisting of recurrently connected neurons. Transitions between such network states, also called attractor states, can be induced by external input, and represent working memory contents or active task rules. Simulations showed that the stability of these attractor states, and thus of task rule representations, depend on the dopamine state of the system and can consequently vary between persons. The Dual-State Theory predicts an antagonistic relationship between cognitive flexibility and cognitive stability, as robust attractor states would facilitate the inhibition of distractors, but impair efficient task switching, while rather unstable attractor states would promote efficient transitions between representations but would also come at the cost of increased distractibility. Based on the Dual-State Theory, a task paradigm was designed allowing for the simultaneous assessment of cognitive flexibility, in the sense of rule-based task switching, and cognitive stability, in the sense of inhibiting irrelevant distractors. Furthermore, a behavioral measure was developed to assess the individual attractor state stability, named spontaneous switching rate (SSR). In the first study of this work, this paradigm was tested in a sample of healthy human subjects using functional magnetic resonance imaging (fMRI). An overlapping fronto-parietal network was activated for both cognitive flexibility and cognitive stability. Furthermore, behavioral as well as neuroimaging results are in favor of an antagonistic relationship between cognitive flexibility and cognitive stability. A specific prefrontal region, the inferior frontal junction (IFJ), was implied to potentially contain the relevant neural networks mediating the transitions between attractor states, i.e., task rule representations, as its activity was modulated by the SSR such that persons with rather unstable attractor states activated it less during task switching while showing better performance. Most importantly, functional connectivity of the IFJ was antagonistically modulated by the SSR: more flexible persons connected it less to another prefrontal area during task switching, while showing higher functional connectivity during distractor inhibition. In a second study, a larger human sample was assessed and further hypotheses derived from the Dual-State Theory on variability of neural processing were tested: we hypothesized that persons with high brain signal variability should have less stable network states and thus benefit on tasks requiring cognitive flexibility but suffer from it when the task requires a higher degree of cognitive stability. Furthermore, recent fMRI-research on brain signal variability revealed beneficial effects of higher brain signal variability on cognitive performance in general. Using a novel customized analysis pipeline to measure trial-to-trial fMRI-signal variability, we indeed found differential effects of brain signal variability: higher levels of brain signal variability were found to be beneficial for effectiveness, i.e., performance in terms of error rates, for both cognitive flexibility and stability. However, brain signal variability impaired the efficiency in terms of response times of inhibiting distractors, i.e., cognitive stability. Due to further predictions of the Dual-State Theory concerning schizophrenia and the dopaminergic system, it was considered valuable to pursue a translational approach and thus allowing for the employment of animal models of psychiatric diseases. Consequently, in a first step the human paradigm was translated for a murine population using an innovative touchscreen approach. Results showed analogous behavioral effects in wildtype mice as before in healthy humans: the antagonistic relation between cognitive flexibility and cognitive stability was replicated and also for mice, a behavioral measure for the individual attractor stability was established and validated, named the individual spontaneous switching score. To conclude, we established a novel paradigm assessing both cognitive flexibility and stability simultaneously showing an antagonistic relationship between these two cognitive functions on the behavioral level in two different species, which supports predictions from the Dual-­State Theory. This was further underlined by evidence on the activation, functional connectivity and signal variability level in the human brain.