Stochastic Dynamics Underlying Cognitive Stability and Flexibility

Dynamic changes in task preparation in a multi-task environment: The task transformation paradigm

A key element of human flexible behavior concerns the ability to continuously predict and prepare for sudden changes in tasks or actions. Here, we tested whether people can dynamically modulate task preparation processes and decision-making strategies when the identity of a to-be-performed task becomes uncertain. To this end, we developed a new paradigm where participants need to prepare for one of nine tasks on each trial. Crucially, in some blocks, the task being prepared could suddenly shift to a different task after a longer cue-target interval, by changing either the stimulus category or categorization rule that defined the initial task. We found that participants were able to dynamically modulate task preparation in the face of this task uncertainty. A second experiment shows that these changes in behavior were not simply a function of decreasing task expectancy, but rather of increasing switch expectancy. Finally, in the third and fourth experiment, we demonstrate that these dynamic modulations can be applied in a compositional manner, depending on whether either only the stimulus category or categorization rule would be expected to change.

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Adapting to these changing goals require corresponding adjustments in cognitive control (e.g., levels of attention, response thresholds). However, adjusting our control to meet new goals comes at a cost: we are better at achieving a goal in isolation than when transitioning between goals. The source of these control adjustment costs remains poorly understood, and the bulk of our understanding of such costs comes from settings in which participants transition between discrete task sets, rather than performance goals. Across four experiments, we show that adjustments in continuous control states incur a performance cost, and that a dynamical systems model can explain the source of these costs. Participants performed a single cognitively demanding task under varying performance goals (e.g., to be fast or to be accurate). We modeled control allocation to include a dynamic process of adjusting from one's current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model accounts for our empirical findings that people under-shoot their target control state more (i.e., exhibit larger adjustment costs) when (a) goals switch rather than remain fixed over a block (Study 1); (b) target control states are more distant from one another (Study 2); (c) less time is given to adjust to the new goal (Study 3); and (d) when anticipating having to switch goals more frequently (Study 4). Our findings characterize the costs of adjusting control to meet changing goals, and show that these costs can emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility in human goal-directed behavior.

Rethinking Movement Disorders

At present, clinical practice and research in movement disorders (MDs) focus on the "normalization" of altered movements. In this review, rather than concentrating on problems and burdens people with MDs undoubtedly have, we highlight their hidden potentials. Starting with current definitions of Parkinson's disease (PD), dystonia, chorea, and tics, we outline that solely conceiving these phenomena as signs of dysfunction falls short of their complex nature comprising both problems and potentials. Such potentials can be traced and understood in light of well-established cognitive neuroscience frameworks, particularly ideomotor principles, and their influential modern derivatives. Using these frameworks, the wealth of data on altered perception-action integration in the different MDs can be explained and systematized using the mechanism-oriented concept of perception-action binding. According to this concept, MDs can be understood as phenomena requiring and fostering flexible modifications of perception-action associations. Consequently, although conceived as being caught in a (trough) state of deficits, given their high flexibility, people with MDs also have high potential to switch to (adaptive) peak activity that can be conceptualized as hidden potentials. Currently, clinical practice and research in MDs are concerned with deficits and thus the "deep and wide troughs," whereas "scattered narrow peaks" reflecting hidden potentials are neglected. To better delineate and utilize the latter to alleviate the burden of affected people, and destigmatize their conditions, we suggest some measures, including computational modeling combined with neurophysiological methods and tailored treatment. © 2024 International Parkinson and Movement Disorder Society.

Dynamic neuronal instability generates synaptic plasticity and behavior: Insights from Drosophila sleep

How do neurons encode the information that underlies cognition, internal states, and behavior? This review focuses on the neural circuit mechanisms underlying sleep in Drosophila and, to illustrate the power of addressing neural coding in this system, highlights a specific circuit mediating the circadian regulation of sleep quality. This circuit exhibits circadian cycling of sleep quality, which depends solely on the pattern (not the rate) of spiking. During the night, the stability of spike waveforms enhances the reliability of spike timing in these neurons to promote sleep quality. During the day, instability of the spike waveforms leads to uncertainty of spike timing, which remarkably produces synaptic plasticity to induce arousal. Investigation of the molecular and biophysical basis of these changes was greatly facilitated by its study in Drosophila, revealing direct connections between genes, molecules, spike biophysical properties, neural codes, synaptic plasticity, and behavior. Furthermore, because these patterns of neural activity change with aging, this model system holds promise for understanding the interplay between the circadian clock, aging, and sleep quality. It is proposed here that neurophysiological investigations of the Drosophila brain present an exceptional opportunity to tackle some of the most challenging questions related to neural coding.

Fractal basins as a mechanism for the nimble brain

An interesting feature of the brain is its ability to respond to disparate sensory signals from the environment in unique ways depending on the environmental context or current brain state. In dynamical systems, this is an example of multi-stability, the ability to switch between multiple stable states corresponding to specific patterns of brain activity/connectivity. In this article, we describe chimera states, which are patterns consisting of mixed synchrony and incoherence, in a brain-inspired dynamical systems model composed of a network with weak individual interactions and chaotic/periodic local dynamics. We illustrate the mechanism using synthetic time series interacting on a realistic anatomical brain network derived from human diffusion tensor imaging. We introduce the so-called vector pattern state (VPS) as an efficient way of identifying chimera states and mapping basin structures. Clustering similar VPSs for different initial conditions, we show that coexisting attractors of such states reveal intricately "mingled" fractal basin boundaries that are immediately reachable. This could explain the nimble brain's ability to rapidly switch patterns between coexisting attractors.

Control dilemma: Evidence of the stability-flexibility trade-off

Cognitive control can be applied flexibly when task goals or environments change (i.e., cognitive flexibility), or stably to pursue a goal in the face of distraction (i.e., cognitive stability). Whether these seemingly contradictory characteristics have an inverse relationship has been controversial, as some studies have suggested a trade-off mechanism between cognitive flexibility and cognitive stability, while others have not found such reciprocal associations. This study investigated the possible antagonistic correlation between cognitive flexibility and stability using a novel version of the flexibility-stability paradigm and the classic cued task switching paradigm. In Experiment 1, we showed that cognitive flexibility was inversely correlated with cognitive stability, as increased distractor proportions were associated with decreased cognitive flexibility and greater cognitive stability. Moreover, cognitive flexibility and stability were regulated by a single control system instead of two independent control mechanisms, as the model selection results indicated that the reciprocally regulated model with one integration parameter outperformed all other models, and the model parameter was inversely linked to cognitive flexibility and stability. We found similar results using the classic cued task switching paradigm in Experiment 2. Therefore, a trade-off between cognitive flexibility and stability was observed from the paradigms used in this study.

Pushing the Bounds of Bounded Optimality and Rationality

All forms of cognition, whether natural or artificial, are subject to constraints of their computing architecture. This assumption forms the tenet of virtually all general theories of cognition, including those deriving from bounded optimality and bounded rationality. In this letter, we highlight an unresolved puzzle related to this premise: what are these constraints, and why are cognitive architectures subject to cognitive constraints in the first place? First, we lay out some pieces along the puzzle edge, such as computational tradeoffs inherent to neural architectures that give rise to rational bounds of cognition. We then outline critical next steps for characterizing cognitive bounds, proposing that some of these bounds can be subject to modification by cognition and, as such, are part of what is being optimized when cognitive agents decide how to allocate cognitive resources. We conclude that these emerging views may contribute to a more holistic perspective on the nature of cognitive bounds, as well as their alteration subject to cognition.

The costs and benefits of psychedelics on cognition and mood

Anecdotal evidence has indicated that psychedelic substances may acutely enhance creative task performance, although empirical support for this claim is mixed at best. Clinical research has shown that psychedelics might have enduring effects on mood and well-being. However, there is no neurocognitive framework that ties acute changes in cognition to long-term effects in mood. In this review, we operationalize creativity within an emerging cognitive control framework and assess the current empirical evidence of the effects of psychedelics on creativity. Next, we leverage insights about the mechanisms and computations by which other psychoactive drugs act to enhance versus impair cognition, in particular to those that act on catecholamines, the neurophysiological consequences of which are relatively well understood. Finally, we use the same framework to link the suggested psychedelic-induced improvements in creativity with enduring psychedelic-induced improvements in mood.

Modelling human behaviour in cognitive tasks with latent dynamical systems

Response time data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data-generating process or are limited to modelling single trials. We introduce task-DyVA, a deep learning framework in which expressive dynamical systems are trained to reproduce sequences of response times observed in data from individual human subjects. Models fitted to a large task-switching dataset captured subject-specific behavioural differences with high temporal precision, including task-switching costs. Through perturbation experiments and analyses of the models' latent dynamics, we find support for a rational account of switch costs in terms of a stability-flexibility trade-off. Thus, our framework can be used to discover interpretable cognitive theories that explain how the brain dynamically gives rise to behaviour.

Individual differences in resilience to stress are associated with affective flexibility

Cognitive flexibility is frequently linked to resilience because of its important contribution to stress regulation. In this context, particularly affective flexibility, defined as the ability to flexibly attend and disengage from affective information, may play a significant role. In the present study, the relationship of cognitive and affective flexibility and resilience was examined in 100 healthy participants. Resilience was measured with three self-report questionnaires, two defining resilience as a personality trait and one focusing on resilience as an outcome in the sense of stress coping abilities. Cognitive and affective flexibility were assessed in two experimental task switching paradigms with non-affective and affective materials and tasks, respectively. The cognitive flexibility paradigm additionally included measures of cognitive stability and spontaneous switching in ambiguous situations. In the affective flexibility paradigm, we explicitly considered the affective valence of the stimuli. Response time switch costs in the affective flexibility paradigm were significantly correlated to all three measures of resilience. The correlation was not specific for particular valences of the stimuli before or during switching. For cognitive (non-affective) flexibility, a significant correlation of response time switch costs was found with only one resilience measure. A regression analysis including both affective and cognitive switch costs as predictors of resilience indicated that only affective, but not cognitive switch costs, explained unique variance components. Furthermore, the experimental measures of cognitive stability and the rate of spontaneous switching in ambiguous situations did not correlate with resilience scores. These findings suggest that specifically the efficiency of flexibly switching between affective and non-affective information is related to resilience.

The modulatory effect of adaptive task-switching training on resting-state neural network dynamics in younger and older adults

With increasing life expectancy and active aging, it becomes crucial to investigate methods which could compensate for generally detected cognitive aging processes. A promising candidate is adaptive cognitive training, during which task difficulty is adjusted to the participants' performance level to enhance the training and potential transfer effects. Measuring intrinsic brain activity is suitable for detecting possible distributed training-effects since resting-state dynamics are linked to the brain's functional flexibility and the effectiveness of different cognitive processes. Therefore, we investigated if adaptive task-switching training could modulate resting-state neural dynamics in younger (18-25 years) and older (60-75 years) adults (79 people altogether). We examined spectral power density on resting-state EEG data for measuring oscillatory activity, and multiscale entropy for detecting intrinsic neural complexity. Decreased coarse timescale entropy and lower frequency band power as well as increased fine timescale entropy and higher frequency band power revealed a shift from more global to local information processing with aging before training. However, cognitive training modulated these age-group differences, as coarse timescale entropy and lower frequency band power increased from pre- to post-training in the old-training group. Overall, our results suggest that cognitive training can modulate neural dynamics even when measured outside of the trained task.

Electroencephalography complexity in resting and task states in adults with attention-deficit/hyperactivity disorder

Analysing EEG complexity could provide insight into neural connectivity underlying attention-deficit/hyperactivity disorder symptoms. EEG complexity was calculated through multiscale entropy and compared between adults with attention-deficit/hyperactivity disorder and their peers during resting and go/nogo task states. Multiscale entropy change from the resting state to the task state was also examined as an index of the brain’s ability to change from a resting to an active state. Thirty unmedicated adults with attention-deficit/hyperactivity disorder were compared with 30 match-paired healthy peers on the multiscale entropy in the resting and task states as well as their multiscale entropy change. Results showed differences in multiscale entropy between individuals with attention-deficit/hyperactivity disorder and their peers during the resting state as well as the task state. The multiscale entropy measured from the comparison group was larger than that from the attention-deficit/hyperactivity disorder group in the resting state, whereas the reverse pattern was found during the task state. Our most robust finding showed that the multiscale entropy change from individuals with attention-deficit/hyperactivity disorder was smaller than that from their peers, specifically at frontal sites. Interestingly, individuals without attention-deficit/hyperactivity disorder performed better with decreasing multiscale entropy changes, demonstrating higher accuracy, faster reaction time and less variability in their reaction times. These data suggest that multiscale entropy could not only provide insight into neural connectivity differences between adults with attention-deficit/hyperactivity disorder and their peers but also into their behavioural performance.

Event-based control of autonomic and emotional states by the anterior cingulate cortex

Despite being an intensive area of research, the function of the anterior cingulate cortex (ACC) remains somewhat of a mystery. Human imaging studies implicate the ACC in various cognitive functions, yet surgical ACC lesions used to treat emotional disorders have minimal lasting effects on cognition. An alternative view is that ACC regulates autonomic states, consistent with its interconnectivity with autonomic control regions and that stimulation evokes changes in autonomic/emotional states. At the cellular level, ACC neurons are highly multi-modal and promiscuous, and can represent a staggering array of task events. These neurons nevertheless combine to produce highly event-specific ensemble patterns that likely alter activity in downstream regions controlling emotional and autonomic tone. Since neuromodulators regulate the strength of the ensemble activity patterns, they would regulate the impact these patterns have on downstream targets. Through these mechanisms, the ACC may determine how strongly to react to the very events its ensembles represent. Pathologies arise when specific event-related representations gain excessive control over autonomic/emotional states.

Dopamine, Cognitive Flexibility, and IQ: Epistatic Catechol-O-MethylTransferase:DRD2 Gene-Gene Interactions Modulate Mental Rigidity

Cognitive flexibility has been hypothesized to be neurochemically rooted in dopamine neurotransmission. Nonetheless, underpowered sample sizes and contradictory meta-analytic findings have obscured the role of dopamine genes in cognitive flexibility and neglected potential gene-gene interactions. In this largest neurocognitive-genetic study to date (n = 1400), single nucleotide polymorphisms associated with elevated prefrontal dopamine levels (catechol-O-methyltransferase; rs4680) and diminished striatal dopamine (C957T; rs6277) were both implicated in Wisconsin Card Sorting Test performance. Crucially, however, these genetic effects were only evident in low-IQ participants, suggesting high intelligence compensates for, and eliminates, the effect of dispositional dopamine functioning on flexibility. This interaction between cognitive systems may explain and resolve previous empirical inconsistencies in highly educated participant samples. Moreover, compensatory gene-gene interactions were discovered between catechol-O-methyltransferase and DRD2, such that genotypes conferring either elevated prefrontal dopamine or diminished striatal dopamine-via heightened striatally concentrated D2 dopamine receptor availability-are sufficient for cognitive flexibility, but neither is necessary. The study has therefore revealed a form of epistatic redundancy or substitutability among dopamine systems in shaping adaptable thought and action, thus defining boundary conditions for dopaminergic effects on flexible behavior. These results inform theories of clinical disorders and psychopharmacological interventions and uncover complex fronto-striatal synergies in human flexible cognition.

Rationalizing constraints on the capacity for cognitive control

Humans are remarkably limited in: (i) how many control-dependent tasks they can execute simultaneously, and (ii) how intensely they can focus on a single task. These limitations are universal assumptions of most theories of cognition. Yet, a rationale for why humans are subject to these constraints remains elusive. This feature review draws on recent insights from psychology, neuroscience, and machine learning, to suggest that constraints on cognitive control may result from a rational adaptation to fundamental, computational dilemmas in neural architectures. The reviewed literature implies that limitations in multitasking may result from a trade-off between learning efficacy and processing efficiency and that limitations in the intensity of commitment to a single task may reflect a trade-off between cognitive stability and flexibility.

Within-Trial Persistence of Learned Behavior as a Dissociable Behavioral Component in Hippocampus-Dependent Memory Tasks: A Potential Postlearning Role of Immature Neurons in the Adult Dentate Gyrus

The term “memory strength” generally refers to how well one remembers something. But more precisely it contains multiple modalities, such as how easily, how accurately, how confidently and how vividly we remember it. In human, these modalities of memory strength are dissociable. In this study, we asked whether we can isolate a behavioral component that is dissociable from others in hippocampus-dependent memory tasks in mice, which potentially reflect a modality of memory strength. Using a virus-mediated inducible method, we ablated immature neurons in the dentate gyrus in mice after we trained the mice with hippocampus-dependent memory tasks normally. In memory retrieval tests, these ablated mice initially showed intact performance. However, the ablated mice ceased learned behavior prematurely within a trial compared with control mice. In addition, the ablated mice showed shorter duration of individual episodes of learned behavior. Both affected behavioral measurements point to persistence of learned behavior. Thus, the effect of the postlearning manipulation showed dissociation between initial performance and persistence of learned behavior. These two behavioral components are likely to reflect different brain functions and be mediated by separate mechanisms, which might represent different modalities of memory strength. These simple dissociable measurements in widely used behavioral paradigms would be useful to understand detailed mechanisms underlying the expression of learned behavior and potentially different modalities of memory strength in mice. We also discuss a potential role that immature neurons in the dentate gyrus may play in persistence of learned behavior.

Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology.

Working memory requires the brain to switch between cognitive states and activity patterns. Here, the authors show that the steering of these neural network dynamics is influenced by dopamine D1- and D2-receptor function and altered in schizophrenia.

Component processes underlying voluntary task selection: Separable contributions of task-set inertia and reconfiguration

Most theories describing the cognitive processes underlying task switching allow for contributions of active task-set reconfiguration and task set inertia. Manipulations of the Cue-to-Stimulus-Interval (CSI) are generally thought to influence task set reconfiguration, while Response-to-Cue (RCI) manipulations are thought to influence task set inertia. Together, these intervals compose the Response-to-Stimulus (RSI) interval. However, these theories do not adequately account for voluntary task switching, because a participant can theoretically prepare for an upcoming trial at any point. We used drift diffusion models to examine the contributions of reconfiguration and task set inertia to performance in single- and double-registrant-registrant voluntary task switching. In both paradigms, RSI length moderated nondecision time, suggesting both switch-specific and general preparation prior to cue presentation. In only the double-registrant registrant paradigm, RSI length additionally moderated task set inertia and CSI length affected general (but not switch-specific) preparation. The effects of cue timing (CSI length) depended upon required response to the cue. Future work should attempt to corroborate our findings regarding switch-specific and general preparation effects of interval lengths using EEG.

The anterior cingulate cortex and event-based modulation of autonomic states

In spite of being an intensive area of research focus, the anterior cingulate cortex (ACC) remains somewhat of an enigma. Many theories have focused on its role in various aspects of cognition yet surgically precise lesions of the ACC, used to treat severe emotional disorders in human patients, typically have no lasting effects on cognition. An alternative view is that the ACC has a prominent role in regulating autonomic states. This view is consistent with anatomical data showing that a main target of the ACC are regions involved in autonomic control and with the observation that stimulation of the ACC evokes changes in autonomic states in both animals and humans. From an electrophysiological perspective, ACC neurons appear able to represent virtually any event or internal state, even though there is not always a strong link between these representations and behavior. Ensembles of neurons form robust contextual representations that strongly influence how specific events are encoded. The activity patterns associated with these contextually-based event representations presumably impact activity in downstream regions that control autonomic state. As a result, the ACC may regulate the autonomic and perhaps emotional reactions to events it is representing. This event-based control of autonomic tone by the ACC would likely arise during all types of cognitive and affective processes, without necessarily being critical for any of them.

Cognitive and behavioural flexibility: neural mechanisms and clinical considerations

Cognitive and behavioural flexibility permit the appropriate adjustment of thoughts and behaviours in response to changing environmental demands. Brain mechanisms enabling flexibility have been examined using non-invasive neuroimaging and behavioural approaches in humans alongside pharmacological and lesion studies in animals. This work has identified large-scale functional brain networks encompassing lateral and orbital frontoparietal, midcingulo-insular and frontostriatal regions that support flexibility across the lifespan. Flexibility can be compromised in early-life neurodevelopmental disorders, clinical conditions that emerge during adolescence and late-life dementias. We critically evaluate evidence for the enhancement of flexibility through cognitive training, physical activity and bilingual experience.

Cognitive, Affective, and Feedback-Based Flexibility - Disentangling Shared and Different Aspects of Three Facets of Psychological Flexibility

Cognitive flexibility - the ability to adjust one ´s behavior to changing environmental demands - is crucial for controlled behavior. However, the term 'cognitive flexibility' is used heterogeneously, and associations between cognitive flexibility and other facets of flexible behavior have only rarely been studied systematically. To resolve some of these conceptual uncertainties, we directly compared cognitive flexibility (cue-instructed switching between two affectively neutral tasks), affective flexibility (switching between a neutral and an affective task using emotional stimuli), and feedback-based flexibility (non-cued, feedback-dependent switching between two neutral tasks). Three experimental paradigms were established that share as many procedural features (in terms of stimuli and/or task rules) as possible and administered in a pre-registered study plan (N = 100). Correlation analyses revealed significant associations between the efficiency of cognitive and affective task switching (response time switch costs). Feedback-based flexibility (measured as mean number of errors after rule reversals) did not correlate with task switching efficiency in the other paradigms, but selectively with the effectiveness of affective switching (error rate costs when switching from neutral to emotion task). While preregistered confirmatory factor analysis (CFA) provided no clear evidence for a shared factor underlying the efficiency of switching in all three domains of flexibility, an exploratory CFA suggested commonalities regarding switching effectiveness (accuracy-based switch costs). We propose shared mechanisms controlling the efficiency of cue-dependent task switching across domains, while the relationship to feedback-based flexibility may depend on mechanisms controlling switching effectiveness. Our results call for a more stringent conceptual differentiation between different variants of psychological flexibility.

Measuring Cognitive Flexibility with the Flexible Item Selection Task: From fMRI Adaptation to Individual Connectome Mapping

Cognitive flexibility, the ability to appropriately adjust behavior in a changing environment, has been challenging to operationalize and validate in cognitive neuroscience studies. Here, we investigate neural activation and directed functional connectivity underlying cognitive flexibility using an fMRI-adapted version of the Flexible Item Selection Task (FIST) in adults (n = 32, ages 19-46 years). The fMRI-adapted FIST was reliable, showed comparable performance to the computer-based version of the task, and produced robust activation in frontoparietal, anterior cingulate, insular, and subcortical regions. During flexibility trials, participants directly engaged the left inferior frontal junction, which influenced activity in other cortical and subcortical regions. The strength of intrinsic functional connectivity between select brain regions was related to individual differences in performance on the FIST, but there was also significant individual variability in functional network topography supporting cognitive flexibility. Taken together, these results suggest that the FIST is a valid measure of cognitive flexibility, which relies on computations within a broad corticosubcortical network driven by inferior frontal junction engagement.

A computational perspective on the roles of affect in cognitive control

Previous work has demonstrated that cognitive control can be influenced by affect, both when it is tied to the anticipated outcomes for cognitive performance (integral affect) and when affect is induced independently of performance (incidental affect). However, the mechanisms through which such interactions occur remain debated, in part because they have yet to be formalized in a way that allows experimenters to test quantitative predictions of a putative mechanism. To generate such predictions, we leveraged a recent model that determines cognitive control allocation by weighing potential costs and benefits in order to determine the overall Expected Value of Control (EVC). We simulated potential accounts of how integral and incidental affect might influence this valuation process, including whether incidental positive affect influences how difficult one perceives a task to be, how effortful it feels to exert control, and/or the marginal utility of succeeding at the task. We find that each of these accounts makes dissociable predictions regarding affect's influence on control allocation and measures of task performance (e.g., conflict adaptation, switch costs). We discuss these findings in light of the existing empirical findings and theoretical models. Collectively, this work grounds existing theories regarding affect-control interactions, and provides a method by which specific predictions of such accounts can be confirmed or refuted based on empirical data.

Brain Development and Stochastic Processes During Prenatal and Early Life: You Can't Lose It if You've Never Had It; But It's Better to Have It and Lose It, Than Never to Have Had It at All

Brain development, although largely driven by genetic processes, also is influenced by environmental factors. However, there has been little discussion in the psychiatric literature on the role of stochastic, or chance, events that take place during neurodevelopment. Studies suggest that the brain capitalizes on and regulates the extent of stochastic processes during development. Furthermore, because neurodevelopment is influenced by environmental factors, there is emerging evidence that fostering those positive environmental factors during prenatal and early life could optimize neurodevelopment and provide greater resilience, including those potentially resulting from stochastic processes. Evidence for the role of environmental factors in optimizing early brain development is supported by work in large population-based studies of child development, randomized control trials in high-risk populations, and early-life adoption studies. The public health message is that creating an environment that fosters optimal brain development during prenatal and early life could prevent psychopathology and provide the developing brain the best chance against negative stochastic processes and potential stressors that are inevitable later in life.

Regional Striatal Cholinergic Involvement in Human Behavioral Flexibility

Animal studies have shown that the striatal cholinergic system plays a role in behavioral flexibility but, until recently, this system could not be studied in humans due to a lack of appropriate noninvasive techniques. Using proton magnetic resonance spectroscopy, we recently showed that the concentration of dorsal striatal choline (an acetylcholine precursor) changes during reversal learning (a measure of behavioral flexibility) in humans. The aim of the present study was to examine whether regional average striatal choline was associated with reversal learning. A total of 22 participants (mean age = 25.2 years, range = 18-32 years, 13 female) reached learning criterion in a probabilistic learning task with a reversal component. We measured choline at rest in both the dorsal and ventral striatum using magnetic resonance spectroscopy. Task performance was described using a simple reinforcement learning model that dissociates the contributions of positive and negative prediction errors to learning. Average levels of choline in the dorsal striatum were associated with performance during reversal, but not during initial learning. Specifically, lower levels of choline in the dorsal striatum were associated with a lower number of perseverative trials. Moreover, choline levels explained interindividual variance in perseveration over and above that explained by learning from negative prediction errors. These findings suggest that the dorsal striatal cholinergic system plays an important role in behavioral flexibility, in line with evidence from the animal literature and our previous work in humans. Additionally, this work provides further support for the idea of measuring choline with magnetic resonance spectroscopy as a noninvasive way of studying human cholinergic neurochemistry.SIGNIFICANCE STATEMENT Behavioral flexibility is a crucial component of adaptation and survival. Evidence from the animal literature shows that the striatal cholinergic system is fundamental to reversal learning, a key paradigm for studying behavioral flexibility, but this system remains understudied in humans. Using proton magnetic resonance spectroscopy, we showed that choline levels at rest in the dorsal striatum are associated with performance specifically during reversal learning. These novel findings help to bridge the gap between animal and human studies by demonstrating the importance of cholinergic function in the dorsal striatum in human behavioral flexibility. Importantly, the methods described here cannot only be applied to furthering our understanding of healthy human neurochemistry, but also to extending our understanding of cholinergic disorders.

Drift-diffusion models for multiple-alternative forced-choice decision making

The canonical computational model for the cognitive process underlying two-alternative forced-choice decision making is the so-called drift-diffusion model (DDM). In this model, a decision variable keeps track of the integrated difference in sensory evidence for two competing alternatives. Here I extend the notion of a drift-diffusion process to multiple alternatives. The competition between n alternatives takes place in a linear subspace of [Formula: see text] dimensions; that is, there are [Formula: see text] decision variables, which are coupled through correlated noise sources. I derive the multiple-alternative DDM starting from a system of coupled, linear firing rate equations. I also show that a Bayesian sequential probability ratio test for multiple alternatives is, in fact, equivalent to these same linear DDMs, but with time-varying thresholds. If the original neuronal system is nonlinear, one can once again derive a model describing a lower-dimensional diffusion process. The dynamics of the nonlinear DDM can be recast as the motion of a particle on a potential, the general form of which is given analytically for an arbitrary number of alternatives.

Motivation and cognitive control in depression

Depression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes. We review depression-related impairments in key components of motivation along with new cognitive neuroscience models that focus on the role of motivation in the decision-making about cognitive control allocation. Based on this review we propose a unifying framework which connects motivational and cognitive control deficits in depression. This framework is rooted in computational models of cognitive control and offers a mechanistic understanding of cognitive control deficits in depression.

Both a Gauge and a Filter: Cognitive Modulations of Pupil Size

Over 50 years of research have established that cognitive processes influence pupil size. This has led to the widespread use of pupil size as a peripheral measure of cortical processing in psychology and neuroscience. However, the function of cortical control over the pupil remains poorly understood. Why does visual attention change the pupil light reflex? Why do mental effort and surprise cause pupil dilation? Here, we consider these functional questions as we review and synthesize two literatures on cognitive effects on the pupil: how cognition affects pupil light response and how cognition affects pupil size under constant luminance. We propose that cognition may have co-opted control of the pupil in order to filter incoming visual information to optimize it for particular goals. This could complement other cortical mechanisms through which cognition shapes visual perception.

Effects of reward on spatial working memory in schizophrenia

Reward processing and cognition are disrupted in schizophrenia (SCZ), yet how these processes interface is unknown. In SCZ, deficits in reward representation may affect motivated, goal-directed behaviors. To test this, we examined the effects of monetary reward on spatial working memory (WM) performance in patients with SCZ. To capture complimentary effects, we tested biophysically grounded computational models of neuropharmacologic manipulations onto a canonical fronto-parietal association cortical microcircuit capable of WM computations. Patients with SCZ (n = 33) and healthy control subjects (HCS; n = 32) performed a spatial WM task with 2 reward manipulations: reward cues presented prior to each trial, or contextually prior to a block of trials. WM performance was compared with cortical circuit models of WM subjected to feed-forward glutamatergic excitation, feed-forward GABAergic inhibition, or recurrent modulation strengthening local connections. Results demonstrated that both groups improved WM performance to reward cues presented prior to each trial (HCS d = -0.62; SCZ d = -1.0), with percent improvement correlating with baseline WM performance (r = .472, p < .001). However, rewards presented contextually before a block of trials did not improve WM performance in patients with SCZ (d = 0.01). Modeling simulations achieved improved WM precision through strengthened local connections via neuromodulation, or feed-forward inhibition. Taken together, this work demonstrates that patients with SCZ can improve WM performance to short-term, but not longer-term rewards-thus, motivated behaviors may be limited by strength of reward representation. A potential mechanism for transiently improved WM performance may be strengthening of local fronto-parietal microcircuit connections via neuromodulation or feed-forward inhibitory drive. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Dynamic graph metrics: Tutorial, toolbox, and tale

The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.

Dynamic graph metrics: Tutorial, toolbox, and tale

The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.

Levodopa improves response inhibition and enhances striatal activation in early-stage Parkinson's disease

Dopaminergic medications improve the motor symptoms of Parkinson's disease (PD), but their effect on response inhibition, a critical executive function, remains unclear. Previous studies primarily enrolled patients in more advanced stages of PD, when dopaminergic medication loses efficacy, and patients were typically on multiple medications. Here, we recruited 21 patients in early-stage PD on levodopa monotherapy and 37 age-matched controls to perform the stop-signal task during functional magnetic resonance imaging. In contrast to previous studies reporting null effects in more advanced PD, levodopa significantly improved response inhibition performance in our sample. No significant group differences were found in brain activations to pure motor inhibition or error processing (stop success vs. error trials). However, relative to controls, the PD group showed weaker striatal activations to salient events (infrequent vs. frequent events: stop vs. go trials) and fronto-striatal task-residual functional connectivity; both were restored with levodopa. Thus, levodopa appears to improve an important executive function in early-stage PD via enhanced salient signal processing, shedding new light on the role of dopaminergic signaling in response inhibition.

Functional neurochemical imaging of the human striatal cholinergic system during reversal learning

Animal studies have shown that acetylcholine (ACh) levels in the dorsal striatum play a role in reversal learning. However, this has not been studied in humans due to a lack of appropriate non-invasive techniques. Proton magnetic resonance spectroscopy (¹ H-MRS) can be used to measure metabolite levels in humans in vivo. Although it cannot be used to study ACh directly, ¹ H-MRS can be used to study choline, an ACh precursor, which is linked to activity-dependent ACh release. The aim of this study was to use functional-¹ H-MRS (fMRS) to measure changes in choline levels in the human dorsal striatum during performance of a probabilistic reversal learning task. We demonstrate a task-dependent decrease in choline, specifically during reversal, but not initial, learning. We interpret this to reflect a sustained increase in ACh levels, which is in line with findings from the animal literature. This task-dependent change was specific to choline and was not observed in control metabolites. These findings provide support for the use of fMRS in the in vivo study of the human cholinergic system.

Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability

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

SIGNIFICANCE STATEMENT

Recent neuroscientific research showed that the human brain signal is intrinsically variable and suggested that this variability improves performance. Computational models of prefrontal neural networks predict differential effects of variability for different behavioral situations requiring either cognitive flexibility or stability. However, this hypothesis has so far not been put to an empirical test. In this study, we assessed cognitive flexibility and cognitive stability, and, besides a generally positive effect of neural variability on accuracy measures, we show that neural variability in a prefrontal brain area at the inferior frontal junction is differentially associated with performance: higher levels of variability are beneficial for the effectiveness of task switching (cognitive flexibility) but detrimental for the efficiency of distractor inhibition (cognitive stability).