<p dir="ltr">Metacontrol describes outsourcing cognitive control to environmental cues, allowing for efficient instantiation of appropriate cognitive control. While recent years have seen many studies characterizing metacontrol in behavioral terms, relatively little effort has been made to characterize the neural mechanisms underlying metacontrol. Thus, the current dissertation project aimed to investigate the neural systems and interactive features of metacontrol, more specifically, proactive and reactive metacontrol that exert context-appropriate control states in preparatory and just-in-time manners, respectively. Specifically, Study 1 employed a functional magnetic resonance imaging technique and identified brain regions activated under proactive and reactive metacontrol operations. Importantly, the study revealed that the two metacontrol modes were supported by distinct neural systems. Building on the premise that distinct neural systems supporting the two metacontrol modes would enable the concurrent operation of both modes, Study 2 tested whether a preparatory metacontrol mode, proactive metacontrol, can coexist with a just-in-time metacontrol mode, reactive metacontrol. Using electroencephalography, Study 2 revealed that the two metacontrol modes can operate in parallel. Lastly, extending Study 2, Study 3 investigated how proactive metacontrol interacts with reactive metacontrol. Study 3 observed that high preparatory control states during proactive metacontrol benefit reactive metacontrol. Specifically, reactive metacontrol was observed only when penalties were at stake, which promoted heightened preparatory control states. In summary, Study 1 demonstrated that the two metacontrol modes are not only operationally distinctive but also characterized by separate neural systems underlying them. This aligns with the observation from Study 2 showing that the parallel operation of two metacontrol modes is feasible. Lastly, Study 3 suggests that despite the two metacontrol modes being supported by distinct neural systems (as shown in Study 1), they are not entirely independent and can interact with each other. Collectively, these findings reveal the relationship between the two metacontrol modes and elucidate their intricate interactions, thereby deepening our understanding of the neurocognitive mechanisms underlying metacontrol.</p>

<div>While cognitive control enables the selection of goal-relevant responses, metacontrol enables the selection of context-appropriate control operations. In task switching, metacontrol modulates task-switching efficiency by retrieving the associations between a contextual cue and a particular cognitive control demand. While the automatic retrieval of cognitive control is appealing due to its time and energy efficiency, the effects of different contextual cues have been shown in separate studies and appear to have different characteristics. Here, we devised a single task-switching paradigm to test whether we can observe both list-wide and item-specific metacontrol within subjects. In two experiments, we demonstrated reduced switch costs in lists associated with a high probability of switching as compared with lists with a low probability of switching (i.e., a list-wide switch probability [LWSP] effect). Similarly, we observed an analogous item-specific switch probability (ISSP) effect such that items associated with a high probability of switching incurred smaller switch costs as compared with items associated with a low probability of switching. We also confirmed that both list-wide and item-specific switch probability effects were not dependent on lower-level stimulus-response associations. However, the LWSP and the ISSP effects were uncorrelated, suggesting a lack of dependence. Together, these findings suggest that there are two distinct modes of metacontrol that are deployed in a context-sensitive manner in order to adapt to specific cognitive demands.</div>

Standard clinical and psychiatric thinking follows a unipolar logic that is centered at “normal” conditions characterized by optimal performance in everyday life, with more atypical conditions being defined by the (degree of) absence of “normality.” A similar logic has been used to describe cognitive control, assuming that optimal control abilities are characterized by a strong focus on the current goal and ignorance of goal-irrelevant information (the concept of willpower), while difficulties in focusing and ignoring are considered indications of the absence of control abilities. However, there is increasing evidence that willpower represents only one side of the control coin. While a strong focus on the current goal can be beneficial under some conditions, other conditions would benefit from a more open mind, from flexibility to consider alternative goals and information related to them. According to the metacontrol model, people can vary in their cognitive processing style, on a dimension with the extreme poles of “persistence” on the one hand and “flexibility” on the other. Whereas a high degree of persistence corresponds to the original idea of cognitive control as willpower, with a strong focus on one goal and the information related to it, a high degree of flexibility is characterized by a more integrative, less selective and exclusive processing style, which facilitates switching between tasks, ideas, and actions, and taking into consideration a broader range of possibilities. We argue that this approach calls for a more bipolar account in the clinical sciences as well. Rather than considering individuals as typical or atypical, it would theoretically and practically make more sense to characterize their cognitive abilities in terms of underlying dimensions, such as the persistence/flexibility dimension. This would reveal that possible weaknesses with respect to one pole, such as persistence, and tasks relying thereupon, may come with corresponding strengths with respect to the other pole, such as flexibility, and respective tasks. We bolster our claim by discussing available evidence suggesting that neurodevelopmental atypicality often comes with weaknesses in tasks related to one pole but strengths in tasks related to the other.

The human brain is equipped with the ability to plan ahead, i.e. to mentally simulate the expected consequences of candidate actions to select the one with the most desirable expected long-term outcome. Insufficient planning can lead to maladaptive behaviour and may even be a contributory cause of important societal problems such as the depletion of natural resources or man-made climate change. Understanding the cognitive and neural mechanisms of forward planning and its regulation are therefore of great importance and could ultimately give us clues on how to better align our behaviour with long-term goals. Apart from its potential beneficial effects, planning is time-consuming and therefore associated with opportunity costs. It is assumed that the brain regulates the investment into planning based on a cost-benefit analysis, so that planning only takes place when the perceived benefits outweigh the costs. But how can the brain know in advance how beneficial or costly planning will be? One potential solution is that people learn from experience how valuable planning would be in a given situation. It is however largely unknown how the brain implements such learning, especially in environments with large state spaces. This dissertation tested the hypothesis that humans construct and use so-called control contexts to efficiently adjust the degree of planning to the demands of the current situation. Control contexts can be seen as abstract state representations, that conveniently cluster together situations with a similar demand for planning. Inferring context thus allows to prospectively adjust the control system to the learned demands of the global context. To test the control context hypothesis, two complex sequential decision making tasks were developed. Each of the two tasks had to fulfil two important criteria. First, the tasks should generate both situations in which planning had the potential to improve performance, as well as situations in which a simple strategy was sufficient. Second, the tasks had to feature rich state spaces requiring participants to compress their state representation for efficient regulation of planning. Participants’ planning was modelled using a parametrized dynamic programming solution to a Markov Decision Process, with parameters estimated via hierarchical Bayesian inference. The first study used a 15-step task in which participants had to make a series of decisions to achieve one or multiple goals. In this task, the computational costs of accurate forward planning increased exponentially with the length of the planning horizon. We therefore hypothesized that participants identify ‘distance from goal’ as the relevant contextual feature to guide their regulation of forward planning. As expected we found that participants predominantly relied on a simple heuristic when still far from the goal but progressively switched towards forward planning when the goal approached. In the second study participants had to sustainably invest a limited but replenishable energy resource, that was needed to accept offers, in order to accumulate a maximum number of points in the long run. The demand for planning varied across the different situations of the task, but due to the large number of possible situations (n = 448) it would be difficult for the participants to develop an expectation for each individual situation of how beneficial planning would be. We therefore hypothesized, that to regulate their forward planning participants used a compressed tasks representation, clustering together states with similar demands for planning. Consistent with this, reaction times (operationalising planning duration) increased with trial-by-trial value-conflict (operationalising approximate planning demand), but this increase was more pronounced in a context with generally high demand for planning. We further found that fMRI activity in the dorsal anterior cingulate cortex (dACC) increased with conflict, but this increase was more pronounced in a context with generally high demand for planning as well. Taken together, the results suggest that the dACC integrates representations of planning demand on different levels of abstraction to regulate prospective information sampling in an efficient and situation-appropriate way. This dissertation provides novel insights into the question how humans adapt their planning to the demands of the current situation. The results are consistent with the view that the regulation of planning is based on an integrated signal of the expected costs and benefits of planning. Furthermore, the results of this dissertation provide evidence that the regulation of planning in environments with real-world complexity critically relies on the brain’s powerful ability to construct and use abstract hierarchical representations.

Self-control denotes the ability to override current desires to render behavior consistent with long-term goals. A key assumption is that self-control is required when short-term desires are transiently stronger (more preferred) than long-term goals and people would yield to temptation without exerting self-control. We argue that this widely shared conception of self-control raises a fundamental yet rarely discussed conceptual paradox: How is it possible that a person most strongly desires to perform a behavior (e.g., eat chocolate) and at the same time desires to recruit self-control to prevent themselves from doing it? A detailed analysis reveals that three common assumptions about self-control cannot be true simultaneously. To avoid the paradox, any coherent theory of self-control must abandon either the assumption (a) that recruitment of self-control is an intentional process, or (b) that humans are unitary agents, or (c) that self-control consists in overriding the currently strongest desire. We propose a taxonomy of different kinds of self-control processes that helps organize current theories according to which of these assumptions they abandon. We conclude by outlining unresolved questions and future research perspectives raised by different conceptions of self-control and discuss implications for the question of whether self-control can be considered rational.

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.