The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes.

Despite their immense relevance, the neurocognitive mechanisms underlying real-life self-control failures (SCFs) are insufficiently understood. Whereas previous studies have shown that SCFs were associated with decreased activity in the right inferior frontal gyrus (rIFG; a region involved in cognitive control), here we consider the possibility that the reduced implementation of cognitive control in individuals with low self-control may be due to impaired performance monitoring. Following a brain-as-predictor approach, we combined experience sampling of daily SCFs with functional magnetic resonance imaging (fMRI) in a Stroop task. In our sample of 118 participants, proneness to SCF was reliably predicted by low error-related activation of a performance-monitoring network (comprising anterior mid-cingulate cortex, presupplementary motor area, and anterior insula), low posterror rIFG activation, and reduced posterror slowing. Remarkably, these neural and behavioral measures predicted variability in SCFs beyond what was predicted by self-reported trait self-control. These results suggest that real-life SCFs may result from deficient performance monitoring, leading to reduced recruitment of cognitive control after responses that conflict with superordinate goals.

By exploiting information that is contained in the spatial arrangement of neural activations, multivariate pattern analysis (MVPA) can detect distributed brain activations which are not accessible by standard univariate analysis. Recent methodological advances in MVPA regularization techniques have made it feasible to produce sparse discriminative whole-brain maps with highly specific patterns. Furthermore, the most recent refinement, the Graph Net, explicitly takes the 3D-structure of fMRI data into account. Here, these advanced classification methods were applied to a large fMRI sample (N=70) in order to gain novel insights into the functional localization of outcome integration processes. While the beneficial effect of differential outcomes is well-studied in trial-and-error learning, outcome integration in the context of instruction-based learning has remained largely unexplored. In order to examine neural processes associated with outcome integration in the context of instruction-based learning, two groups of subjects underwent functional imaging while being presented with either differential or ambiguous outcomes following the execution of varying stimulus-response instructions. While no significant univariate group differences were found in the resulting fMRI dataset, L1-regularized (sparse) classifiers performed significantly above chance and also clearly outperformed the standard L2-regularized (dense) Support Vector Machine on this whole-brain between-subject classification task. Moreover, additional L2-regularization via the Elastic Net and spatial regularization by the Graph Net improved interpretability of discriminative weight maps but were accompanied by reduced classification accuracies. Most importantly, classification based on sparse regularization facilitated the identification of highly specific regions differentially engaged under ambiguous and differential outcome conditions, comprising several prefrontal regions previously associated with probabilistic learning, rule integration and reward processing. Additionally, a detailed post-hoc analysis of these regions revealed that distinct activation dynamics underlay the processing of ambiguous relative to differential outcomes. Together, these results show that L1-regularization can improve classification performance while simultaneously providing highly specific and interpretable discriminative activation patterns.

By following explicit instructions, humans instantaneously get the hang of tasks they have never performed before. We used a specially calibrated multivariate analysis technique to uncover the elusive representational states during the first few implementations of arbitrary rules such as ‘for coffee, press red button’ following their first-time instruction. Distributed activity patterns within the ventrolateral prefrontal cortex (VLPFC) indicated the presence of neural representations specific of individual stimulus-response (S-R) rule identities, preferentially for conditions requiring the memorization of instructed S-R rules for correct performance. Identity-specific representations were detectable starting from the first implementation trial and continued to be present across early implementation trials. The increasingly fluent application of novel rule representations was channelled through increasing cooperation between VLPFC and anterior striatum. These findings inform representational theories on how the prefrontal cortex supports behavioral flexibility specifically by enabling the ad-hoc coding of newly instructed individual rule identities during their first-time implementation.

Deficient self-control leads to shortsighted decisions and incurs severe personal and societal costs. Although neuroimaging has advanced our understanding of neural mechanisms underlying self-control, the ecological validity of laboratory tasks used to assess self-control remains largely unknown. To increase ecological validity and to test a specific hypothesis about the mechanisms underlying real-life self-control, we combined functional MRI during value-based decision-making with smartphone-based assessment of real-life self-control in a large community sample ( N = 194). Results showed that an increased propensity to make shortsighted decisions and commit self-control failures, both in the laboratory task as well as during real-life conflicts, was associated with a reduced modulation of neural value signals in the ventromedial prefrontal cortex in response to anticipated long-term consequences. These results constitute the first evidence that neural mechanisms mediating anticipations of future consequences not only account for self-control in laboratory tasks but also predict real-life self-control, thereby bridging the gap between laboratory research and real-life behavior.