The computational and neural basis of cognitive control: charted territory and new frontiers

Brain activity dynamics after traumatic brain injury indicate increased state transition energy and preference of lower order states

Traumatic Brain Injury (TBI) can cause structural damage to the neural tissue and white matter connections in the brain, disrupting its functional coactivation patterns. Although there are a wealth of studies investigating TBI-related changes in the brain's structural and functional connectomes, fewer studies have investigated TBI-related changes to the brain's dynamic landscape. Network control theory is a framework that integrates structural connectomes and functional time-series to quantify brain dynamics. Using this approach, we analyzed longitudinal trajectories of brain dynamics from acute to chronic injury phases in two cohorts of individuals with mild and moderate to severe TBI, and compared them to non-brain-injured, age- and sex-matched control individuals' trajectories. Our analyses suggest individuals with mild TBI initially have brain activity dynamics similar to controls but then shift in the subacute and chronic stages of the injury (1 month and 12 months post-injury) to favor lower-order visual-dominant states compared to higher-order default mode dominant states. We further find that, compared to controls, individuals with mild TBI have overall decreased entropy and increased transition energy demand in the sub-acute and chronic stages that correlates with poorer attention performance. Finally, we found that the asymmetry in top-down to bottom-up transition energies increased in subacute and chronic stages of mild TBI, possibly indicating decreased efficacy of top-down inhibition. We replicate most findings with the moderate to severe TBI dataset, indicating their robustness, with the notable exception of finding the opposite correlation between global transition energy and mean reaction time (MRT). We attribute differences to the cohorts' varied injury severity, with perhaps a stronger compensatory mechanism in moderate to severe TBI. Overall, our findings reveal shifting brain dynamics after mild to severe TBI that relate to behavioral measures of attention, shedding light on post-injury mechanisms of recovery.

Age-related differences in trust decisions: when memory fails and appearances prevail

Older adults are frequent victims of scams, possibly due to biases in how they decide whom to trust. Indeed, older adults' decisions are more likely to be influenced by how generous a person looks and less so by their memory for how this person behaved. Here, we leverage functional magnetic resonance imaging data to clarify the mechanism by which this age-dependent difference emerges. Eighty-six participants learned how much of a $10 endowment an individual shared in a dictator game, and then made decisions about whom to play another round with. As we hypothesized, older adults did not reliably prefer to re-engage with people who had proven themselves to be generous. This bias was driven by a combination of worse associative memory for how much each person shared, linked to decreased medial temporal lobe activity during encoding, and decreased inhibition of irrelevant facial features, linked to reduced activity in the inferior frontal gyrus. Taken together, our findings highlight 'age-related differences' in the ability to both encode relevant information and adaptively deploy it in service of social decisions.

Understanding cognitive control in depression: the interactive role of emotion, expected efficacy and reward

BACKGROUND

Difficulties in cognitive control over negative emotional stimuli are a key characteristic of depression. The Expected Value of Control (EVC) provides a framework for understanding how cognitive control is allocated, focusing on the motivational factors of efficacy and reward. Efficacy is the likelihood that an effort will result in a specific result, while reward is the value assigned to that outcome. However, the impact of emotion on the estimation of EVC has not been explored. We investigated the interplay between emotion and motivation, using the EVC theoretical framework, in depression.

METHODS

We utilized a within-between-subject design. The subjects were healthy controls (n = 31) and those with depression (n = 36), who underwent a clinical diagnostic interview, completed the General Health Questionnaire-12, the Beck Depression Inventory-II, and participated in an incentivized Emotional Stroop Paradigm, whereby participants received cues indicating different levels of efficacy (low vs. high) and reward (low vs. high) prior to the targeted stimuli.

RESULTS

Significant interactions were detected between a) group × emotional valence × efficacy, and b) group × reward regarding accuracy rates on the Emotional Stroop Task. Follow-up analyses revealed that during high-efficacy trials, the Control group demonstrated significantly greater accuracy than the Depressed group for both positive and neutral stimuli. In low-efficacy trials, the Controls were also significantly more accurate than the Depressed group when responding to negative stimuli. Additionally, the Depressed group performed significantly worse than Controls on high-reward trials, no significant difference was detected between the two groups on low-reward trials.

CONCLUSION

The emotional valence of stimuli can influence the assessment of reward efficacy, and individuals with depression may have difficulties focusing on reward cues. Further research is necessary to incorporate emotion into the EVC framework.

Shielding working memory from distraction is more effortful than flexible updating

Exerting cognitive control is known to carry a subjective effort cost and people are generally biased to avoid it. Recent theorizing suggests that the cost of cognitive effort serves as a motivational signal to bias people away from excessive focusing and towards more cognitive flexibility. We asked whether the effort cost of stable distractor resistance is higher than that of non-selective flexible updating of working memory representations. We tested this prediction by using (1) a delayed response paradigm in which we manipulate demands for distractor resistance and flexible updating, as well as (2) a subsequent cognitive effort discounting paradigm that allows us to quantify subjective effort costs. We demonstrate, in two different samples (28 and 62 participants) that participants discount tasks both high in distractor resistance and flexible updating when comparing with taking a break. As predicted, when directly contrasting distractor resistance and non-selective flexible updating the subjective cost of performing a task requiring distractor resistance is higher than that requiring flexible updating.

Oscillations of the Wandering Mind: Neural Evidence for Distinct Exploration/Exploitation Strategies in Younger and Older Adults

This study traced the neurophysiological signals of fluctuating attention and task-related processing to ascertain the mechanistic basis of transient strategic shifts between competing task focus and mind-wandering, as expressed by the 'exploitation/exploration' framework, and explored how they are differentially affected with age. Thirty-four younger (16 female, mean age 22 years) and 34 healthy older (20 female, mean age 71 years) adults performed the Gradual Contrast Change Detection task; monitoring a continuously presented flickering annulus for intermittent gradual contrast reductions and responding to experience sampling probes to discriminate the nature of their thoughts at discrete moments. Electroencephalography and pupillometry were concurrently recorded during target- and probe-related intervals. Older adults tracked the downward stimulus trajectory with greater sensory integrity (reduced target SSVEP amplitude) and demonstrated earlier initiation of evidence accumulation (earlier onset CPP), attenuated variability in the attentional signal (posterior alpha) and more robust phasic pupillary responses to the target, suggesting steadier attentional engagement with age. Younger adults only exhibited intermittent sensory encoding, indexed by greater variability in the sensory (SSVEP) and attentional (alpha) signals before mind-wandering relative to focused states. Attentional variability was accompanied by disrupted behavioural performance and reduced task-related neural processing, independent of age group. Together, this elucidates distinct performance strategies employed by both groups. Older adults suspended mind-wandering and implemented an exploitative oscillation strategy to circumvent their reduced cognitive resources and allay potential behavioural costs. Conversely, younger adults exhibited greater exploration through mind-wandering, utilising their greater cognitive resources to flexibly alternate between competing goal-directed and mind-wandering strategies, with limited costs.

The neuroscientific basis of flow: Learning progress guides task engagement and cognitive control

People often strive for deep engagement in activities, a state typically associated with feelings of flow - full task absorption accompanied by a sense of control and enjoyment. The intrinsic factors driving such engagement and facilitating subjective feelings of flow remain unclear. Building on computational theories of intrinsic motivation, this study examines how learning progress predicts engagement and directs cognitive control. Results showed that task engagement, indicated by feelings of flow and low distractibility, is a function of learning progress. Electroencephalography data further revealed that learning progress is associated with enhanced proactive preparation (e.g., reduced pre-stimulus contingent negativity variance and parietal alpha desynchronization) and improved feedback processing (e.g., increased P3b amplitude and parietal alpha desynchronization). The impact of learning progress on cognitive control is observed at the task-block and goal-episode levels, but not at the trial level. This suggests that learning progress shapes cognitive control over extended periods as progress accumulates. These findings highlight the critical role of learning progress in sustaining engagement and cognitive control in goal-directed behavior.

Are you worth the wait? Waiting time modulates the social feedback processing: Evidence from event-related potentials

Processing social feedback is essential for establishing appropriate social connections. However, social feedback is not always immediate, and the impact of waiting time on social feedback processing remains unexplored. Using electroencephalography (EEG) and event-related potentials (ERPs), the present study investigated how waiting time affects the N170, reward positivity (RewP), and P3. Participants (N = 36) completed a social evaluation task, awaiting feedback from liked and disliked peers with short (800-1200 ms) or long (5000-6000 ms) waiting times. Participants were more motivated to receive feedback from liked peers, and they rated acceptance from liked peers as more pleasant than rejection. Notably, participants found longer waits more worthwhile when receiving acceptance from liked peers, but less worthwhile when awaiting feedback from disliked peers. EEG results revealed that the RewP was increased for long waiting times for feedback from liked peers, and, conversely, reduced for long waiting times for feedback from disliked peers. Additionally, N170 and P3 were found to be sensitive to waiting time, with larger amplitudes for long compared to short waits. Overall, this study demonstrates that waiting time differentially affects social feedback processing, as reflected by changes in the N170, RewP, and P3. Our findings suggest that increased waiting time does not necessarily reduce reward value; it can enhance it depending on subjective social preferences. The increased N170 and P3 amplitudes during longer waits may indicate heightened attentional and memory demands. This study advances our understanding of the neurocognitive mechanisms underlying social decision-making.

Continuous goal representations: Distance in representational space affects goal switching

Theorists across all fields of psychology consider goals crucial for human action control. Still, the question of how precisely goals are represented in the cognitive system is rarely addressed. Here, we explore the idea that goals are represented as distributed patterns of activation that coexist within continuous mental spaces. In doing so, we discuss and extend popular models of cognitive control and goal-directed behavior, which implicitly convey an image of goals as discrete representational units. To differentiate empirically between discrete and continuous formats of goal representation, we employed a set-shifting paradigm in which participants switched between color goals that varied systematically in their distance in representational space. Across three experiments, we found that previous goals biased behavior during goal switches and that the extent of this bias decreased gradually with the previous goal's distance in color space from color information in the current trial. These graded effects of goal distance on performance are difficult to reconcile with the assumption that goals are discrete representational entities. Instead, they suggest that goals are represented as distributed, partly overlapping patterns of activation within continuous mental spaces. Moreover, the monotonous effects of distance in representational space on performance observed across all conditions in all experiments imply that the spreading of goal activation in representational space follows a monotonous (e.g., bell-shaped) distribution and not a nonmonotonous (e.g., Mexican-hat shaped) one. Our findings ask for a stronger consideration of the continuity of goal representations in models and investigations of goal-directed behavior.

Cognitive control & the anterior cingulate cortex: Necessity & coherence

Influential theories of complex behaviour invoke the notion of cognitive control modulated by conflict between counterfactual actions. Medial frontal cortex, notably the anterior cingulate cortex, has been variously posited as critical to such conflict detection, resolution, or monitoring, largely based on correlative data from functional imaging. Examining performance on the most widely used "conflict" task-Stroop-in a large cohort of patients with focal brain injury (N = 176), we compare anatomical patterns of lesion-inferred neural substrate dependence to those derived from functional imaging, meta-analytically summarised. Our results show that whereas performance is sensitive to the integrity of left lateral frontal regions implicated by functional imaging, it does not depend on medial frontal cortex, despite sampling adequate to reveal robust medial effects in the context of phonemic fluency. We suggest that medial frontal cortex is not critically invoked by Stroop and proceed to review the conceptual grounds for rejecting the core notion of conflict-driven cognitive control.

Dissociable Contributions of Goal-Relevant Evidence and Goal-Irrelevant Familiarity to Individual and Developmental Differences in Conflict Recognition

Recent studies using the diffusion decision model find that performance across many cognitive control tasks can be largely attributed to a task-general efficiency of evidence accumulation (EEA) factor that reflects individuals' ability to selectively gather evidence relevant to task goals. However, estimates of EEA from an n-back "conflict recognition" paradigm in the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, a large, diverse sample of youth, appear to contradict these findings. EEA estimates from "lure" trials-which present stimuli that are familiar (i.e., presented previously) but do not meet formal criteria for being a target-show inconsistent relations with EEA estimates from other trials and display atypical v-shaped bivariate distributions, suggesting many individuals are responding based largely on stimulus familiarity rather than goal-relevant stimulus features. We present a new formal model of evidence integration in conflict recognition tasks that distinguishes individuals' EEA for goal-relevant evidence from their use of goal-irrelevant familiarity. We then investigate developmental, cognitive, and clinical correlates of these novel parameters. Parameters for EEA and goal-irrelevant familiarity-based processing showed strong correlations across levels of n-back load, suggesting they are task-general dimensions that influence individuals' performance regardless of working memory demands. Only EEA showed large, robust developmental differences in the ABCD sample and an independent age-diverse sample. EEA also exhibited higher test-retest reliability and uniquely meaningful associations with clinically relevant dimensions. These findings establish a principled modeling framework for characterizing conflict recognition mechanisms and have several broader implications for research on individual and developmental differences in cognitive control.

Understanding dual process cognition via the minimum description length principle

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in domains ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.

Examining the alignment between subjective effort and objective force production

Ratings of Perceived Exertion (RPE) are frequently used to prescribe exercise intensity. A central assumption of using RPE scales is that the subjective perception of effort maps onto objective performance in a consistent way. However, the degree and shape of how RPE aligns with objective performance is not fully understood. Here, we investigate the degree and shape of alignment, as well as how time (i.e., how frequently an effort needs to be performed) and mental effort (i.e., if one has to invest mental effort and physical effort) correspond with the alignment. In a randomized within-subjects experiment, we used a grip-to-scale method that asked participants (N = 43) to repeatedly squeeze a handgrip dynamometer with four to-be-produced RPE target levels relative to their subjective maximum strength (representing 20%, 40%, 60%, or 80%). We found that the RPE-force alignment was not the same across RPE-levels: Whereas subjective differences from 20-40% and 40-60% were met by comparable differences in produced force, a substantially larger difference was observed for the 60-80% interval. Interestingly, exploratory post-hoc analyses revealed that this was mirrored by an increase in variance at the higher effort levels. In addition, at constant RPE-levels, participants produced less force over time, and this effect was more pronounced at lower RPE target levels. Lastly, anticipating mental effort after the physical effort slightly altered the alignment as a function of the to-be-produced RPE-level and experimental duration. Taken together, our results indicate that the mapping of perceived effort on objective performance is intricate, and several factors affect the degree and shape of how RPE and performance align. Understanding the dynamic adjustment of RPE-performance alignment across different RPE levels is particularly relevant for contexts that use RPE as a tool for training load prescription.

From movement to motivation: a proposed framework to understand the antidepressant effect of exercise

Depression is the leading cause of disability worldwide, exerting a profound negative impact on quality of life in those who experience it. Depression is associated with disruptions to several closely related neural and cognitive processes, including dopamine transmission, fronto-striatal brain activity and connectivity, reward processing and motivation. Physical activity, especially aerobic exercise, reduces depressive symptoms, but the mechanisms driving its antidepressant effects are poorly understood. Here we propose a novel hypothesis for understanding the antidepressant effects of exercise, centred on motivation, across different levels of explanation. There is robust evidence that aerobic exercise decreases systemic inflammation. Inflammation is known to reduce dopamine transmission, which in turn is strongly implicated in effort-based decision making for reward. Drawing on a broad range of research in humans and animals, we propose that by reducing inflammation and boosting dopamine transmission, with consequent effects on effort-based decision making for reward, exercise initially specifically improves 'interest-activity' symptoms of depression-namely anhedonia, fatigue and subjective cognitive impairment - by increasing propensity to exert effort. Extending this framework to the topic of cognitive control, we explain how cognitive impairment in depression may also be conceptualised through an effort-based decision-making framework, which may help to explain the impact of exercise on cognitive impairment. Understanding the mechanisms underlying the antidepressant effects of exercise could inform the development of novel intervention strategies, in particular personalised interventions and boost social prescribing.

A recurrent network model of planning explains hippocampal replay and human behavior

When faced with a novel situation, people often spend substantial periods of time contemplating possible futures. For such planning to be rational, the benefits to behavior must compensate for the time spent thinking. Here, we capture these features of behavior by developing a neural network model where planning itself is controlled by the prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences from its own policy, which we call 'rollouts'. In a spatial navigation task, the agent learns to plan when it is beneficial, which provides a normative explanation for empirical variability in human thinking times. Additionally, the patterns of policy rollouts used by the artificial agent closely resemble patterns of rodent hippocampal replays. Our work provides a theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by-and adaptively affect-prefrontal dynamics.

Language models, like humans, show content effects on reasoning tasks

reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks but exhibit many imperfections. However, human abstract reasoning is also imperfect. Human reasoning is affected by our real-world knowledge and beliefs, and shows notable "content effects"; humans reason more reliably when the semantic content of a problem supports the correct logical inferences. These content-entangled reasoning patterns are central to debates about the fundamental nature of human intelligence. Here, we investigate whether language models-whose prior expectations capture some aspects of human knowledge-similarly mix content into their answers to logic problems. We explored this question across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task. We evaluate state of the art LMs, as well as humans, and find that the LMs reflect many of the same qualitative human patterns on these tasks-like humans, models answer more accurately when the semantic content of a task supports the logical inferences. These parallels are reflected in accuracy patterns, and in some lower-level features like the relationship between LM confidence over possible answers and human response times. However, in some cases the humans and models behave differently-particularly on the Wason task, where humans perform much worse than large models, and exhibit a distinct error pattern. Our findings have implications for understanding possible contributors to these human cognitive effects, as well as the factors that influence language model performance.

Motivational context and neurocomputation of stop expectation moderate early attention responses supporting proactive inhibitory control

Alterations in attention to cues signaling the need for inhibitory control play a significant role in a wide range of psychopathology. However, the degree to which motivational and attentional factors shape the neurocomputations of proactive inhibitory control remains poorly understood. The present study investigated how variation in monetary incentive valence and stake modulate the neurocomputational signatures of proactive inhibitory control. Adults (N = 46) completed a Stop-Signal Task (SST) with concurrent EEG recording under four conditions associated with stop performance feedback: low and high punishment (following unsuccessful stops) and low and high reward (following successful stops). A Bayesian learning model was used to infer individual's probabilistic expectations of the need to stop on each trial: P(stop). Linear mixed effects models were used to examine whether interactions between motivational valence, stake, and P(stop) parameters predicted P1 and N1 attention-related event-related potentials (ERPs) time-locked to the go-onset stimulus. We found that P1 amplitudes increased at higher levels of P(stop) in punished but not rewarded conditions, although P1 amplitude differences between punished and rewarded blocks were maximal on trials when the need to inhibit was least expected. N1 amplitudes were positively related to P(stop) in the high punishment condition (low N1 amplitude), but negatively related to P(stop) in the high reward condition (high N1 amplitude). Critically, high P(stop)-related N1 amplitude to the go-stimulus predicted behavioral stop success during the high reward block, providing evidence for the role of motivationally relevant context and inhibitory control expectations in modulating the proactive allocation of attentional resources that affect inhibitory control. These findings provide novel insights into the neurocomputational mechanisms underlying proactive inhibitory control under valence-dependent motivational contexts, setting the stage for developing motivation-based interventions that boost inhibitory control.

The generative neural microdynamics of cognitive processing

The entorhinal cortex and hippocampus form a recurrent network that informs many cognitive processes, including memory, planning, navigation, and imagination. Neural recordings from these regions reveal spatially organized population codes corresponding to external environments and abstract spaces. Aligning the former cognitive functionalities with the latter neural phenomena is a central challenge in understanding the entorhinal-hippocampal circuit (EHC). Disparate experiments demonstrate a surprising level of complexity and apparent disorder in the intricate spatiotemporal dynamics of sequential non-local hippocampal reactivations, which occur particularly, though not exclusively, during immobile pauses and rest. We review these phenomena with a particular focus on their apparent lack of physical simulative realism. These observations are then integrated within a theoretical framework and proposed neural circuit mechanisms that normatively characterize this neural complexity by conceiving different regimes of hippocampal microdynamics as neuromarkers of diverse cognitive computations.

Cognitive Control in Schizophrenia: Advances in Computational Approaches

Psychiatric research is undergoing significant advances in an emerging subspeciality of computational psychiatry, building upon cognitive neuroscience research by expanding to neurocomputational modeling. Here, we illustrate some research trends in this domain using work on proactive cognitive control deficits in schizophrenia as an example. We provide a selective review of formal modeling approaches to understanding cognitive control deficits in psychopathology, focusing primarily on biologically plausible connectionist-level models as well as mathematical models that generate parameter estimates of putatively dissociable psychological or neural processes. We illustrate some of the advantages of these models in terms of understanding both cognitive control deficits in schizophrenia and the potential roles of effort and motivation. Further, we highlight critical future directions for this work, including a focus on establishing psychometric properties, additional work modeling psychotic symptoms and their interaction with cognitive control, and the need to expand both behavioral and neural modeling to samples that include individuals with different mental health conditions, allowing for the examination of dissociable neural or psychological substrates for seemingly similar cognitive impairments across disorders.

Integrating Social Cognition Into Domain-General Control: Interactive Activation and Competition for the Control of Action (ICON)

Social cognition differs from general cognition in its focus on understanding, perceiving, and interpreting social information. However, we argue that the significance of domain-general processes for controlling cognition has been historically undervalued in social cognition and social neuroscience research. We suggest much of social cognition can be characterized as specialized feature representations supported by domain-general cognitive control systems. To test this proposal, we develop a comprehensive working model, based on an interactive activation and competition architecture and applied to the control of action. As such, we label the model "ICON" (interactive activation and competition model for the control of action). We used the ICON model to simulate human performance across various laboratory tasks. Our simulations emphasize that many laboratory-based social tasks do not require socially specific control systems, such as those that are argued to rely on neural networks associated with theory-of-mind. Moreover, our model clarifies that perceived disruptions in social cognition, even in what appears to be disruption to the control of social cognition, can stem from deficits in social representation instead. We advocate for a "default stance" in social cognition, where control is usually general, but representation is specific. This study underscores the importance of integrating social cognition within the broader realm of domain-general control processing, offering a unified perspective on task processing.

Towards a conflict account of déjà vu: The role of memory errors and memory expectation conflict in the experience of déjà vu

Déjà vu can be defined as conflict between a subjective evaluation of familiarity and a concurrent evaluation of novelty. Accounts of the déjà vu experience have not explicitly referred to a "conflict account of déjà vu" despite the acceptance of conflict-based definitions of déjà vu and relatively recent neuroimaging work that has implicated brain areas associated with conflict as underpinning the experience. Conflict monitoring functioning follows a similar age-related trajectory to déjà vu with a peak in young adulthood and a subsequent age-related decline. In this narrative review of the literature to date, we consider how déjà vu is defined and how this has influenced the understanding of déjà vu. We also review how déjà vu can be understood within theories of recognition memory and cognitive control. Finally, we summarise the conflict account of déjà vu and propose that this account of the experience may provide a coherent explanation as to why déjà vu experiences tend to decrease with age in the non-clinical population.

Pretrial predictors of conflict response efficacy in the human prefrontal cortex

The ability to perform motor actions depends, in part, on the brain's initial state. We hypothesized that initial state dependence is a more general principle and applies to cognitive control. To test this idea, we examined human single units recorded from the dorsolateral prefrontal (dlPFC) cortex and dorsal anterior cingulate cortex (dACC) during a task that interleaves motor and perceptual conflict trials, the multisource interference task (MSIT). In both brain regions, variability in pre-trial firing rates predicted subsequent reaction time (RT) on conflict trials. In dlPFC, ensemble firing rate patterns suggested the existence of domain-specific initial states, while in dACC, firing patterns were more consistent with a domain-general initial state. The deployment of shared and independent factors that we observe for conflict resolution may allow for flexible and fast responses mediated by cognitive initial states. These results also support hypotheses that place dACC hierarchically earlier than dlPFC in proactive control.

The relation between executive functions, error-related brain activity, and ADHD symptoms in clinically evaluated school-aged children

Two event-related potentials (ERPs) elicited following errors, the error-related negativity (ERN) and error positivity (Pe), have been proposed to reflect cognitive control, though the specific processes remain debated. Few studies have examined the ERN and Pe's relations with individual differences in cognitive control/executive functioning using well-validated tests administered separately from the inhibition tasks used to elicit the ERN/Pe. Additionally, neurocognitive tests of executive functions tend to strongly predict ADHD symptoms, but the extent to which task-based and EEG-based estimates of executive functioning/cognitive control account for the same variance in ADHD symptoms remains unclear. The current study addressed these limitations by examining relations between the ERN/Pe and three core executive functions (working memory, inhibitory control, set shifting) in a clinically-evaluated sample of 53 children ages 8-12 (Mage = 10.36, SD = 1.42; 77.4% White/Non-Hispanic; 16 girls) with and without ADHD. Results demonstrated that neither the ERN nor Pe were related to overall cognitive control/executive functioning, or to working memory or set shifting specifically (all 95%CIs include 0.0). In contrast, a larger Pe was associated with better-developed inhibitory control (β=-.35, 95%CI excludes 0.0), but did not capture aspects of inhibitory control that are important for predicting ADHD symptoms. Neither the ERN nor Pe predicted ADHD symptoms (95%CIs include 0.0). Results were generally robust to control for age, sex, SES, ADHD symptom cluster, and anxiety, and emphasize the need for caution when interpreting the ERN/Pe as indices of broad-based cognitive control/executive functioning, as well as using the ERN/Pe to examine cognitive processes contributing to ADHD symptomatology.

Neural mechanisms underlying interindividual differences in intergenerational sustainable behavior

Intergenerational sustainability is a pressing challenge, which is exacerbated by the fact that the current generation must make sacrifices today to ensure the well-being of future generations. There are large interindividual differences in intergenerational sustainable behavior. However, the neural mechanisms underlying these interindividual differences have remained unexplored. Here, we combined fMRI with a consequential intergenerational sustainability paradigm in a sample of 72 healthy students. Specifically, we analyzed task-dependent functional activity and connectivity during intergenerational sustainable decision-making, focusing on the state-like neurophysiological processes giving rise to behavioral heterogeneity in sustainability. We found that differences in neural communication within and between the mentalizing (TPJ/DMPFC) and cognitive control (ACC/DLPFC) network are related to interindividual differences in intergenerational sustainable behavior. Specifically, the stronger the functional connectivity within and between these networks during decision-making, the more individuals behaved intergenerationally sustainably. Corroborated by mediation analyses, these findings suggest that differences in the engagement of perspective-taking and self-control processes underly interindividual differences in intergenerational sustainable behavior. By answering recent calls for leveraging behavioral and neuroscience for sustainability research, we hope to contribute to interdisciplinary efforts to advance the understanding of interindividual differences in intergenerational sustainability.

How Do Executive Functions Influence Children's Reasoning About Counterintuitive Concepts in Mathematics and Science?

Many scientific and mathematical concepts are counterintuitive because they conflict with misleading perceptual cues or incorrect naive theories that we build from our everyday experiences of the world. Executive functions (EFs) influence mathematics and science achievement, and inhibitory control (IC), in particular, might facilitate counterintuitive reasoning. Stop & Think (S&T) is a computerised learning activity that trains IC skills. It has been found effective in improving primary children's mathematics and science academic performance in a large scale RCT trial (Palak et al., 2019; Wilkinson et al., Journal of Cognitive Enhancement, 4, 296-314, 2020). The current study aimed to investigate the role of EFs and the moderating effects of S&T training on counterintuitive mathematics and science reasoning. A sample of 372 children in school Years 3 (7- to 8-year-olds) and 5 (9- to 10-year-olds) were allocated to S&T, active control or teaching as usual conditions, and completed tasks assessing verbal and visuospatial working memory (WM), IC, IQ, and counterintuitive reasoning, before and after training. Cross-sectional associations between counterintuitive reasoning and EF were found in Year 5 children, with evidence of a specific role of verbal WM. The intervention benefited counterintuitive reasoning in Year 3 children only and EF measures were not found to predict which children would most benefit from the intervention. Combined with previous research, these results suggest that individual differences in EF play a lesser role in counterintuitive reasoning in younger children, while older children show a greater association between EFs and counterintuitive reasoning and are able to apply the strategies developed during the S&T training to mathematics and science subjects. This work contributes to understanding why specifically the S&T intervention is effective. This work was preregistered with the ISRCTN registry (TRN: 54726482) on 10/10/2017.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41465-023-00271-0.

Why it is good to communicate the bad: understanding the influence of message framing in persuasive communication on consumer decision-making processes

Introduction

One approach to bridging the gap between consumer intentions and behavior is persuasive communication to reinforce their intentions and thereby support their behavior change. Message framing has proven to be a useful, persuasive communication tool. However, message framing is considered more complicated than other types of framing because, in addition to concept-specific elements, it is also strongly influenced by and, in turn, influences emotions. Therefore, it is almost impossible for consumers to verbally express their attitudes, so the challenge is to explain and measure its impact. This research aims to help in this regard by suggesting a theoretical model to understand how message framing is processed from a consumer neuroscience perspective. More precisely, the factors that constitute message framing are systematized and built on a reflective-impulsive model and a neural emotion-cognition framework interpreted to explain the persuasive effects of message framing.

Method

A functional magnetic resonance imaging (fMRI) experiment is used to examine the effects of message framing for four different frame types that are hypothesized to affect consumer information processing differently.

Result

The results suggest that communication strategies should take into account the valence of the objects and the frame used. The behavioral results partially confirm the assumption that two types of information processing could take place, as suggested by the reflective-impulsive model. At the neural level, using the network perspective, the results show that certain brain regions primarily associated with emotional and cognitive interaction processes are active during processing, depending on the framing of the message.

Discussion

In cases of indirect avoidance value-consistent framing, it may be good to communicate the bad in the appropriate frame to influence information processing.

Spatio-temporal evolution of human neural activity during visually cued hand movements

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.

Exploring expected reward and efficacy in enhancing cognitive control in patients with depression

BACKGROUND

Depression is associated with impairments in cognitive control. Considering the lack of mechanistic models accounting for cognitive control deficits in depression, the expected value of control (EVC) theory offers a mechanistic view for allocating cognitive control emphasizing motivational components (efficacy, value). Efficacy refers to the possibility that an effort leads to a special outcome and reward refers to the value (amount) associated with the outcome. This study aimed to examine the role of the EVC in depression.

METHOD

This study used a within-between-subject design. Participants with depression (n = 36) and healthy controls (n = 31) completed a clinical diagnostic interview, the Beck Depression Inventory-II, the General Health Questionnaire-12, and a computer-based incentivized Stroop Color-Word Paradigm in which levels of efficacy (high vs. low) and the amount of rewards (high vs. low) were presented as cues before target stimuli.

RESULTS

We found significant interaction effects of group × efficacy and efficacy × reward in terms of reaction time in the Stroop Paradigm. Follow-up analyses indicated the Depressed group were significantly slower than Controls on high efficacy trials, but the two groups did not differ significantly on low efficacy trials. Additionally, on high efficacy trials, reward did not influence performance, but on low efficacy trials, high reward improved performance in both groups.

LIMITATION

Lack of neurological measures and eye tracking techniques.

CONCLUSION

Overall, our findings suggest that reward and efficacy may jointly improve cognitive control allocation and highlight the need for further research examining EVC theory as a mechanistic account of cognitive control deficits in depression.

The Relationship between Cognition and Brain Size or Neuron Number

The comparative approach is a powerful way to explore the relationship between brain structure and cognitive function. Thus far, the field has been dominated by the assumption that a bigger brain somehow means better cognition. Correlations between differences in brain size or neuron number between species and differences in specific cognitive abilities exist, but these correlations are very noisy. Extreme differences exist between clades in the relationship between either brain size or neuron number and specific cognitive abilities. This means that correlations become weaker, not stronger, as the taxonomic diversity of sampled groups increases. Cognition is the outcome of neural networks. Here we propose that considering plausible neural network models will advance our understanding of the complex relationships between neuron number and different aspects of cognition. Computational modelling of networks suggests that adding pathways, or layers, or changing patterns of connectivity in a network can all have different specific consequences for cognition. Consequently, models of computational architecture can help us hypothesise how and why differences in neuron number might be related to differences in cognition. As methods in connectomics continue to improve and more structural information on animal brains becomes available, we are learning more about natural network structures in brains, and we can develop more biologically plausible models of cognitive architecture. Natural animal diversity then becomes a powerful resource to both test the assumptions of these models and explore hypotheses for how neural network structure and network size might delimit cognitive function.

Adaptive coding of stimulus information in human frontoparietal cortex during visual classification

The neural mechanisms of how frontal and parietal brain regions support flexible adaptation of behavior remain poorly understood. Here, we used functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA) to investigate frontoparietal representations of stimulus information during visual classification under varying task demands. Based on prior research, we predicted that increasing perceptual task difficulty should lead to adaptive changes in stimulus coding: task-relevant category information should be stronger, while task-irrelevant exemplar-level stimulus information should become weaker, reflecting a focus on the behaviorally relevant category information. Counter to our expectations, however, we found no evidence for adaptive changes in category coding. We did find weakened coding at the exemplar-level within categories however, demonstrating that task-irrelevant information is de-emphasized in frontoparietal cortex. These findings reveal adaptive coding of stimulus information at the exemplar-level, highlighting how frontoparietal regions might support behavior even under challenging conditions.

Apathy in melancholic depression and abnormal neural activity within the reward-related circuit

Major depressive disorder is a heterogeneous syndrome, of which the most common subtype is melancholic depression (MEL). Previous studies have indicated that anhedonia is frequently a cardinal feature in MEL. As a common syndrome of motivational deficit, anhedonia is closely associated with dysfunction in reward-related networks. However, little is currently known about apathy, another syndrome of motivational deficits, and the underlying neural mechanisms in MEL and non-melancholic depression (NMEL). Herein, the Apathy Evaluation Scale (AES) was used to compare apathy between MEL and NMEL. On the basis of resting-state functional magnetic resonance imaging, functional connectivity strength (FCS) and seed-based functional connectivity (FC) were calculated within reward-related networks and compared among 43 patients with MEL, 30 patients with NMEL, and 35 healthy controls. Patients with MEL had higher AES scores than those with NMEL (t = -2.20, P = 0.03). Relative to NMEL, MEL was associated with greater FCS (t = 4.27, P < 0.001) in the left ventral striatum (VS), and greater FC of the VS with the ventral medial prefrontal cortex (t = 5.03, P < 0.001) and dorsolateral prefrontal cortex (t = 3.18, P = 0.005). Taken together the results indicate that reward-related networks may play diverse pathophysiological roles in MEL and NMEL, thus providing potential directions for future interventions in the treatment of various depression subtypes.

Stabilizing expectations when shifting from analytical to intuitive reasoning: The role of prediction errors in reasoning

As humans, we rely on intuitive reasoning for most of our decisions. However, when there is a novel or atypical decision to be made, we must rely on a slower and more deliberative thought process-analytical reasoning. As we gain experience with these novel or atypical decisions, our reasoning shifts from analytical to intuitive, which parallels a reduction in the need for cognitive control. Here, we sought to confirm this claim by employing electroencephalographic (EEG) measures of cognitive control as participants performed a simple perceptual decision-making task. Specifically, we had participants categorize "blobs" into families based on their visual attributes so we could examine how their reasoning changed with learning. In a key manipulation, halfway through the experiment we introduced novel blob families to categorize, thus temporarily increasing the need for analytical reasoning (i.e., cognitive control). Congruent with past research, we focused our EEG analyses on frontal theta activity as it has been linked to cognitive control and analytical thinking. As hypothesized, we found a transition from analytical to intuitive decision-making systems with learning as indexed by a decrease in frontal theta power. Further, when the novel blobs were introduced at the midpoint of the experiment, we found that decisions about these stimuli recruited analytical reasoning as indicated by increased theta power in comparison to decisions about well-practiced stimuli. We propose our findings to reflect prediction errors to decision demands-a monitoring process that determines whether our expectations of demands are met. Shifting from analytical to intuitive reasoning thus reflects the stabilization of our expectations of decision demands, which can be violated with unexpected demands when encountering novel stimuli.

Overlapping and unique brain responses to cognitive and response inhibition

Although cognitive inhibition and response inhibition fall under the umbrella term of inhibition, the question remains whether the two aspects of inhibition engage shared or distinct brain regions. The current study is one of the first to examine the neural underpinnings of cognitive inhibition (e.g. the Stroop incongruency effect) and response inhibition (e.g. "no-go" response) within a single task. Adult participants (n = 77) completed an adapted version of the Simon Task in a 3T MRI scanner. The results demonstrated that cognitive and response inhibition recruited a group of overlapping brain regions (inferior frontal cortex, inferior temporal lobe, precentral cortex, parietal cortex). However, a direct comparison of cognitive and response inhibition revealed that the two aspects of inhibition also engaged distinct, task-specific brain regions (voxel-wise FWE corrected p < 0.05). Cognitive inhibition was associated with increases in multiple brain regions within the prefrontal cortex. On the other hand, response inhibition was associated with increases in distinct regions of the prefrontal cortex, right superior parietal cortex, and inferior temporal lobe. Our findings advance the understanding of the brain basis of inhibition by suggesting that cognitive inhibition and response inhibition engage overlapping but distinct brain regions.

Learning when effort matters: neural dynamics underlying updating and adaptation to changes in performance efficacy

To determine how much cognitive control to invest in a task, people need to consider whether exerting control matters for obtaining rewards. In particular, they need to account for the efficacy of their performance-the degree to which rewards are determined by performance or by independent factors. Yet it remains unclear how people learn about their performance efficacy in an environment. Here we combined computational modeling with measures of task performance and EEG, to provide a mechanistic account of how people (i) learn and update efficacy expectations in a changing environment and (ii) proactively adjust control allocation based on current efficacy expectations. Across 2 studies, subjects performed an incentivized cognitive control task while their performance efficacy (the likelihood that rewards are performance-contingent or random) varied over time. We show that people update their efficacy beliefs based on prediction errors-leveraging similar neural and computational substrates as those that underpin reward learning-and adjust how much control they allocate according to these beliefs. Using computational modeling, we show that these control adjustments reflect changes in information processing, rather than the speed-accuracy tradeoff. These findings demonstrate the neurocomputational mechanism through which people learn how worthwhile their cognitive control is.

Hearts, flowers, and fruits: All children need to reveal their post-error slowing

Slowing down responses after errors (i.e., post-error slowing [PES]) is an established finding in adults. Yet PES in young children is still not well understood. In this study, we investigated (a) whether young children show PES in tasks with different types of cognitive conflict and differing demands on executive functions, (b) whether PES is adaptive and efficient in the sense that it is associated with better task performance, and (c) whether PES correlates between tasks. We tested 4- to 6-year-old children on the Funny Fruits task (FF; n = 143), a Stroop-like task that incorporates semantic conflict and taxes children's inhibition skills, and the Hearts and Flowers task (HF; n = 170), which incorporates spatial conflict and taxes children's inhibition skills in its incongruent block and taxes both inhibition and cognitive flexibility (rule-switching) skills in its mixed block. A subgroup of children were tested on both FF and HF (n = 74). Results revealed that, first, children showed PES in FF and both blocks of HF, indicating that PES occurs in both types of conflict and under varying executive demands. Second, PES was associated with task accuracy, but only for FF and the mixed HF. Third, a between-task association in PES emerged only between FF and the mixed HF. Together, these findings indicate that PES is still a developing strategy in young children; it is present but only adaptive for, and correlates between, semantic inhibition and spatial flexibility.

Perception-action integration during inhibitory control is reflected in a concomitant multi-region processing of specific codes in the neurophysiological signal

The integration of perception and action has long been studied in psychological science using overarching cognitive frameworks. Despite these being very successful in explaining perception-action integration, little is known about its neurophysiological and especially the functional neuroanatomical foundations. It is unknown whether distinct brain structures are simultaneously involved in the processing of perception-action integration codes and also to what extent demands on perception-action integration modulate activities in these structures. We investigate these questions in an EEG study integrating temporal and ICA-based EEG signal decomposition with source localization. For this purpose, we used data from 32 healthy participants who performed a 'TEC Go/Nogo' task. We show that the EEG signal can be decomposed into components carrying different informational aspects or processing codes relevant for perception-action integration. Importantly, these specific codes are processed independently in different brain structures, and their specific roles during the processing of perception-action integration differ. Some regions (i.e., the anterior cingulate and insular cortex) take a 'default role' because these are not modulated in their activity by demands or the complexity of event file coding processes. In contrast, regions in the motor cortex, middle frontal, temporal, and superior parietal cortices were not activated by 'default' but revealed modulations depending on the complexity of perception-action integration (i.e., whether an event file has to be reconfigured). Perception-action integration thus reflects a multi-region processing of specific fractions of information in the neurophysiological signal. This needs to be taken into account when further developing a cognitive science framework detailing perception-action integration.

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances.SIGNIFICANCE STATEMENT The brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

Development of complex executive function over childhood: Longitudinal growth curve modeling of performance on the Groton Maze Learning Task

This longitudinal study modeled children's complex executive function (EF) development using the Groton Maze Learning Task (GMLT). Using a cohort-sequential design, 147 children (61 males, 5.5-11 years) were recruited from six multicultural primary schools in Melbourne and Perth, Australia. Race/ethnicity data were not available. Children were assessed on the GMLT at 6-month intervals over 2-years between 2010 and 2012. Growth curve models describe age-related change from 5.5 to 12.5 years old. Results showed a quadratic growth trajectory on each measure of error-that is, those that reflect visuospatial memory, executive control (or the ability to apply rules for action), and complex EF. The ability to apply rules for action, while a rate-limiting factor in complex EF, develops rapidly over early-to-mid childhood.

Cognitive control of song production by humpback whales

Singing humpback whales are highly versatile vocalizers, producing complex sequences of sounds that they vary throughout adulthood. Past analyses of humpback whale song have emphasized yearly variations in structural features of songs made collectively by singers within a population with comparatively little attention given to the ways that individual singers vary consecutive songs. As a result, many researchers describe singing by humpback whales as a process in which singers produce sequences of repeating sound patterns. Here, we show that such characterizations misrepresent the degree to which humpback whales flexibly and dynamically control the production of sounds and sound patterns within song sessions. Singers recorded off the coast of Hawaii continuously morphed units along multiple acoustic dimensions, with the degree and direction of morphing varying across parallel streams of successive units. Individual singers also produced multiple phrase variants (structurally similar, but acoustically distinctive sequences) within song sessions. The precision with which individual singers maintained some acoustic properties of phrases and morphing trajectories while flexibly changing others suggests that singing humpback whales actively select and adjust acoustic elements of their songs in real time rather than simply repeating stereotyped sound patterns within song sessions.

Distributional analyses reveal the individual differences in congruency sequence effect

As a sequential modulation of conflict, congruency sequence effect indexes a conflict-induced performance improvement, which is observed as reduced congruency effects for trials after the incongruent trials than for trials after the congruent trials. Although congruency sequence effect has been investigated widely in healthy humans, the studies of distributional characteristics across prototypical congruency tasks are scarce. To investigate this issue, the present study adopts the between-subjects design to carry out three experiments, where subjects were separately informed to perform the Stroop, word Flanker, and letter Flanker tasks. The results showed that congruency sequence effect occurred in the congruent and incongruent trials in the Stroop and word Flanker tasks, respectively, and absented in the letter Flanker task, which is interpreted as the differences in the nature and difficulty of the tasks. The distributional properties of congruency sequence effect did not significantly differ from the Gaussian distribution in the Stroop and word Flanker tasks, but not in the letter Flanker task, suggesting the inter-individual variability of congruency sequence effect depends on the nature of tasks. Importantly, the delta plot analyses showed pronouncedly increased congruency sequence effect over the slowest percentile bines in both the Stroop and word Flanker tasks, verifying the activation suppression hypothesis. Altogether, the present study enriches the literature on the distributional characteristics of congruency sequence effect.

Theta-phase connectivity between medial prefrontal and posterior areas underlies novel instructions implementation

Implementing novel instructions is a complex and uniquely human cognitive ability, that requires the rapid and flexible conversion of symbolic content into a format that enables the execution of the instructed behavior. Preparing to implement novel instructions, as opposed to their mere maintenance, involves the activation of the instructed motor plans, and the binding of the action information to the specific context in which this should be executed. Recent evidence and prominent computational models suggest that this efficient configuration of the system might involve a central role of frontal theta oscillations in establishing top-down long-range synchronization between distant and task-relevant brain areas. In the present EEG study (human subjects, 30 females, 4 males), we demonstrate that proactively preparing for the implementation of novels instructions, as opposed to their maintenance, involves a strengthened degree of connectivity in the theta frequency range between medial prefrontal and motor/visual areas. Moreover, we replicated previous results showing oscillatory features associated specifically with implementation demands, and extended on them demonstrating the role of theta oscillations in mediating the effect of task demands on behavioral performance. Taken together, these findings support our hypothesis that the modulation of connectivity patterns between frontal and task-relevant posterior brain areas is a core factor in the emergence of a behavior-guiding format from novel instructions.Significance statementEveryday life requires the use and manipulation of currently available information to guide behavior and reach specific goals. In the present study we investigate how the same instructed content elicits different neural activity depending on the task being performed. Crucially, connectivity between medial prefrontal cortex and posterior brain areas is strengthened when novel instructions have to be implemented, rather than simply maintained. This finding suggests that theta oscillations play a role in setting up a dynamic and flexible network of task-relevant regions optimized for the execution of the instructed behavior.

Integrated Features for Optimizing Machine Learning Classifiers of Pediatric and Young Adults With a Post-Traumatic Headache From Healthy Controls

Post-traumatic headache (PTH) is a challenging clinical condition to identify and treat as it integrates multiple subjectively defined symptoms with underlying physiological processes. The precise mechanisms underlying PTH are unclear, and it remains to be understood how to integrate the patient experience with underlying biology when attempting to classify persons with PTH, particularly in the pediatric setting where patient self-report may be highly variable. The objective of this investigation was to evaluate the use of different machine learning (ML) classifiers to differentiate pediatric and young adult subjects with PTH from healthy controls using behavioral data from self-report questionnaires that reflect concussion symptoms, mental health, pain experience of the participants, and structural brain imaging from cortical and sub-cortical locations. Behavioral data, alongside brain imaging, survived data reduction methods and both contributed toward final models. Behavioral data that contributed towards the final model included both the child and parent perspective of the pain-experience. Brain imaging features produced two unique clusters that reflect regions that were previously found in mild traumatic brain injury (mTBI) and PTH. Affinity-based propagation analysis demonstrated that behavioral data remained independent relative to neuroimaging data that suggest there is a role for both behavioral and brain imaging data when attempting to classify children with PTH.

Theta oscillations shift towards optimal frequency for cognitive control

Cognitive control allows to flexibly guide behaviour in a complex and ever-changing environment. It is supported by theta band (4-7 Hz) neural oscillations that coordinate distant neural populations. However, little is known about the precise neural mechanisms permitting such flexible control. Most research has focused on theta amplitude, showing that it increases when control is needed, but a second essential aspect of theta oscillations, their peak frequency, has mostly been overlooked. Here, using computational modelling and behavioural and electrophysiological recordings, in three independent datasets, we show that theta oscillations adaptively shift towards optimal frequency depending on task demands. We provide evidence that theta frequency balances reliable set-up of task representation and gating of task-relevant sensory and motor information and that this frequency shift predicts behavioural performance. Our study presents a mechanism supporting flexible control and calls for a reevaluation of the mechanistic role of theta oscillations in adaptive behaviour.

Forward planning driven by context-dependant conflict processing in anterior cingulate cortex

Cognitive control and forward planning in particular is costly, and therefore must be regulated such that the amount of cognitive resources invested is adequate to the current situation. However, knowing in advance how beneficial forward planning will be in a given situation is hard. A way to know the exact value of planning would be to actually do it, which would ab initio defeat the purpose of regulating planning, i.e. the reduction of computational and time costs. One possible solution to this dilemma is that planning is regulated by learned associations between stimuli and the expected demand for planning. Such learning might be based on generalisation processes that cluster together stimulus states with similar control relevant properties into more general control contexts. In this way, the brain could infer the demand for planning, based on previous experience with situations that share some structural properties with the current situation. Here, we used a novel sequential task to test the hypothesis that people use control contexts to efficiently regulate their forward planning, using behavioural and functional magnetic resonance imaging data. Consistent with our hypothesis, reaction times increased with trial-by-trial conflict, where this increase was more pronounced in a context with a learned high demand for planning. Similarly, we found that fMRI activity in the dorsal anterior cingulate cortex (dACC) increased with conflict, and this increase was more pronounced in a context with generally high demand for planning. Taken together, the results indicate that the dACC integrates representations of planning demand at different levels of abstraction to regulate planning in an efficient and situation-appropriate way.

Cognitive Control as a Multivariate Optimization Problem

A hallmark of adaptation in humans and other animals is our ability to control how we think and behave across different settings. Research has characterized the various forms cognitive control can take-including enhancement of goal-relevant information, suppression of goal-irrelevant information, and overall inhibition of potential responses-and has identified computations and neural circuits that underpin this multitude of control types. Studies have also identified a wide range of situations that elicit adjustments in control allocation (e.g., those eliciting signals indicating an error or increased processing conflict), but the rules governing when a given situation will give rise to a given control adjustment remain poorly understood. Significant progress has recently been made on this front by casting the allocation of control as a decision-making problem. This approach has developed unifying and normative models that prescribe when and how a change in incentives and task demands will result in changes in a given form of control. Despite their successes, these models, and the experiments that have been developed to test them, have yet to face their greatest challenge: deciding how to select among the multiplicity of configurations that control can take at any given time. Here, we will lay out the complexities of the inverse problem inherent to cognitive control allocation, and their close parallels to inverse problems within motor control (e.g., choosing between redundant limb movements). We discuss existing solutions to motor control's inverse problems drawn from optimal control theory, which have proposed that effort costs act to regularize actions and transform motor planning into a well-posed problem. These same principles may help shed light on how our brains optimize over complex control configuration, while providing a new normative perspective on the origins of mental effort.

Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making

While often seeming to investigate rather different problems, research into value-based decision making and cognitive control have historically offered parallel insights into how people select thoughts and actions. While the former studies how people weigh costs and benefits to make a decision, the latter studies how they adjust information processing to achieve their goals. Recent work has highlighted ways in which decision-making research can inform our understanding of cognitive control. Here, we provide the complementary perspective: how cognitive control research has informed understanding of decision-making. We highlight three particular areas of research where this critical interchange has occurred: (1) how different types of goals shape the evaluation of choice options, (2) how people use control to adjust the ways they make their decisions, and (3) how people monitor decisions to inform adjustments to control at multiple levels and timescales. We show how adopting this alternate viewpoint offers new insight into the determinants of both decisions and control; provides alternative interpretations for common neuroeconomic findings; and generates fruitful directions for future research.

Altered resting-state functional connectivity of the dorsal anterior cingulate cortex with intrinsic brain networks in male problematic smartphone users

The excessive use of smartphones is associated with various medical complications and mental health problems. However, existing research findings on neurobiological mechanisms behind problematic smartphone use are limited. In this study, we investigated functional connectivity in problematic smartphone users, focusing on the default mode network (DMN) and attentional networks. We hypothesized that problematic smartphone users would have alterations in functional connectivity between the DMN and attentional networks and that such alterations would correlate with the severity of problematic smartphone use. This study included 30 problematic smartphone users and 35 non-problematic smartphone users. We carried out group independent component analysis (group ICA) to decompose resting-state functional magnetic resonance imaging (fMRI) data into distinct networks. We examined functional connectivity using seed-to-seed analysis and identified the nodes of networks in group ICA, which we used as region of interest. We identified greater functional connectivity of the dorsal anterior cingulate cortex (dACC) with the ventral attention network (VAN) and with the DMN in problematic smartphone users. In seed-to-seed analysis, problematic smartphone users showed atypical dACC-VAN functional connectivity which correlated with the smartphone addiction proneness scale total scores. Our resting-state fMRI study found greater functional connectivity between the dACC and attentional networks in problematic smartphone users. Our findings suggest that increased bottom-up and interoceptive attentional processing might play an important role in problematic smartphone use.

Neural markers of proactive and reactive cognitive control are altered during walking: A Mobile Brain-Body Imaging (MoBI) study

The processing of sensory information and the generation of motor commands needed to produce coordinated actions can interfere with ongoing cognitive tasks. Even simple motor behaviors like walking can alter cognitive task performance. This cognitive-motor interference (CMI) could arise from disruption of planning in anticipation of carrying out the task (proactive control) and/or from disruption of the execution of the task (reactive control). In young healthy adults, walking-induced interference with behavioral performance may not be readily observable because flexibility in neural circuits can compensate for the added demands of simultaneous loads. In this study, cognitive-motor loads were systematically increased during cued task-switching while underlying neurophysiologic changes in proactive and reactive mechanisms were measured. Brain activity was recorded from 22 healthy young adults using 64-channel electroencephalography (EEG) based Mobile Brain/Body Imaging (MoBI) as they alternately sat or walked during performance of cued task-switching. Walking altered neurophysiological indices of both proactive and reactive control. Walking amplified cue-evoked late fontal slow waves, and reduced the amplitude of target-evoked fronto-central N2 and parietal P3. The effects of walking on evoked neural responses systematically increased as the task became increasingly difficult. This may provide an objective brain marker of increasing cognitive load, and may prove to be useful in identifying seemingly healthy individuals who are currently able to disguise ongoing degenerative processes through active compensation. If, however, degeneration continues unabated these people may reach a compensatory limit at which point both cognitive performance and control of coordinated actions may decline rapidly.

Neurobiological mechanisms of control in alcohol use disorder - moving towards mechanism-based non-invasive brain stimulation treatments

Alcohol use disorder (AUD) is characterized by excessive habitual drinking and loss of control over alcohol intake despite negative consequences. Both of these aspects foster uncontrolled drinking and high relapse rates in AUD patients. Yet, common interventions mostly focus on the phenomenological level, and prioritize the reduction of craving and withdrawal symptoms. Our review provides a mechanistic understanding of AUD and suggests alternative therapeutic approaches targeting the mechanisms underlying dysfunctional alcohol-related behaviours. Specifically, we explain how repeated drinking fosters the development of rigid drinking habits and is associated with diminished cognitive control. These behavioural and cognitive effects are then functionally related to the neurobiochemical effects of alcohol abuse. We further explain how alterations in fronto-striatal network activity may constitute the neurobiological correlates of these alcohol-related dysfunctions. Finally, we discuss limitations in current pharmacological AUD therapies and suggest non-invasive brain stimulation (like TMS and tDCS interventions) as a potential addition/alternative for modulating the activation of both cortical and subcortical areas to help re-establish the functional balance between controlled and automatic behaviour.

Linear reinforcement learning in planning, grid fields, and cognitive control

It is thought that the brain’s judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of specific, quantifiable biases. It replaces the classic nonlinear, model-based optimization with a linear approximation that softly maximizes around (and is weakly biased toward) a default policy. This solution demonstrates connections between seemingly disparate phenomena across behavioral neuroscience, notably flexible replanning with biases and cognitive control. It also provides insight into how the brain can represent maps of long-distance contingencies stably and componentially, as in entorhinal response fields, and exploit them to guide choice even under changing goals.

Models of decision making have so far been unable to account for how humans’ choices can be flexible yet efficient. Here the authors present a linear reinforcement learning model which explains both flexibility, and rare limitations such as habits, as arising from efficient approximate computation

Decomposing the motivation to exert mental effort

Achieving most goals demands cognitive control, yet people vary widely in their success at meeting these demands. While motivation is known to be fundamental to determining these successes, what determines one's motivation to perform a given task remains poorly understood. Here, we describe recent efforts towards addressing this question using the Expected Value of Control model, which simulates the process by which people weigh the costs and benefits of exerting mental effort. By functionally decomposing this cost-benefit analysis, this model has been used to fill gaps in our understanding of the mechanisms of mental effort and to generate novel predictions about the sources of variability in real-world performance. We discuss the opportunities the model provides for formalizing hypotheses about why people vary in their motivation to perform tasks, as well as for understanding limitations in our ability to test these hypotheses based on a given measure of performance.