Mental labour

Mental effort is an elementary notion in our folk psychology and a familiar fixture in everyday introspective experience. However, as an object of scientific study, mental effort has remained rather elusive. Cognitive psychology has provided some tools for understanding how effort impacts performance, by linking effort with cognitive control function. What has remained less clear are the principles that govern the allocation of mental effort. Under what circumstances do people choose to invest mental effort, and when do they decline to do so? And what regulates the intensity of mental effort when it is applied? In new and promising work, these questions are being approached with the tools of behavioural economics. Though still in its infancy, this economic approach to mental effort research has already uncovered important aspects of effort-based decision-making, and points clearly to future lines of inquiry, including some intriguing opportunities presented by recent artificial intelligence research.

Integrated externally and internally generated task predictions jointly guide cognitive control in prefrontal cortex

Cognitive control proactively configures information processing to suit expected task demands. Predictions of forthcoming demand can be driven by explicit external cues or be generated internally, based on past experience (cognitive history). However, it is not known whether and how the brain reconciles these two sources of information to guide control. Pairing a probabilistic task-switching paradigm with computational modeling, we found that external and internally generated predictions jointly guide task preparation, with a bias for internal predictions. Using model-based neuroimaging, we then show that the two sources of task prediction are integrated in dorsolateral prefrontal cortex, and jointly inform a representation of the likelihood of a change in task demand, encoded in frontoparietal cortex. Upon task-stimulus onset, dorsomedial prefrontal cortex encoded the need for reactive task-set adjustment. These data reveal how the human brain integrates external cues and cognitive history to prepare for an upcoming task.

A resource-rational theory of set size effects in human visual working memory

Encoding precision in visual working memory decreases with the number of encoded items. Here, we propose a normative theory for such set size effects: the brain minimizes a weighted sum of an error-based behavioral cost and a neural encoding cost. We construct a model from this theory and find that it predicts set size effects. Notably, these effects are mediated by probing probability, which aligns with previous empirical findings. The model accounts well for effects of both set size and probing probability on encoding precision in nine delayed-estimation experiments. Moreover, we find support for the prediction that the total amount of invested resource can vary non-monotonically with set size. Finally, we show that it is sometimes optimal to encode only a subset or even none of the relevant items in a task. Our findings raise the possibility that cognitive "limitations" arise from rational cost minimization rather than from constraints.

On the effects of tyrosine supplementation on interference control in a randomized, double-blind placebo-control trial

Exerting cognitive control is an effortful endeavor that is strongly modulated by the availability of dopamine (DA) and norepinephrine (NE), which are both synthesized from the amino acid precursor tyrosine. Supplementing tyrosine may increase the synthesis of both catecholamines. This has been suggested to improve executive functioning and potentially even counteract depletion effects in this domain. Yet, it has remained unclear whether tyrosine also improves interference control and whether subliminally and consciously triggered response conflicts are subject to the same modulation. We investigated this question in a double-blind intra-individual study design. N = 26 young healthy subjects performed two consecutive cognitive control tasks that triggered automatic incorrect response tendencies; once with tyrosine supplementation and once with a placebo. The results show that tyrosine decreased the size of consciously perceived conflicts in a Simon Task, but not a Flanker task, thus suggesting that stimulus-response conflicts might be modulated differently from stimulus-stimulus conflicts. At the same time, tyrosine supplementation increased the size of subliminally triggered conflicts whenever a different, consciously perceived conflict was also present. This suggests that control-related DA and NE release may increase visuo-motor priming, especially when no conflict-specific top-down control may be triggered to counteract subliminal priming effects. Also, these subliminal conflicts might be aggravated by concurrent control investments in other kinds of conflict. Taken together, our data suggest that beneficial effects of tyrosine supplementation do not require depletion effects, but may be limited to situations where we consciously perceive a conflict and the associated need for conflict-specific control.

Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not "all or none," but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.

Motivation dynamically increases noise resistance by internal feedback during movement

Motivation improves performance, pushing us beyond our normal limits. One general explanation for this is that the effects of neural noise can be reduced, at a cost. If this were possible, reward would promote investment in resisting noise. But how could the effects of noise be attenuated, and why should this be costly? Negative feedback may be employed to compensate for disturbances in a neural representation. Such feedback would increase the robustness of neural representations to internal signal fluctuations, producing a stable attractor. We propose that encoding this negative feedback in neural signals would incur additional costs proportional to the strength of the feedback signal. We use eye movements to test the hypothesis that motivation by reward improves precision by increasing the strength of internal negative feedback. We find that reward simultaneously increases the amplitude, velocity and endpoint precision of saccades, indicating true improvement in oculomotor performance. Analysis of trajectories demonstrates that variation in the eye position during the course of saccades is predictive of the variation of endpoints, but this relation is reduced by reward. This indicates that motivation permits more aggressive correction of errors during the saccade, so that they no longer affect the endpoint. We suggest that such increases in internal negative feedback allow attractor stability, albeit at a cost, and therefore may explain how motivation improves cognitive as well as motor precision.

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Motivation can increase speed and reduce behavioural variability.

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This requires stabilising neural representations so they are robust to noise.

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Stable representations or attractors in neural systems may come at the cost of stronger negative feedback.

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Examination of trajectory correlations demonstrates that reward increases negative feedback.

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We propose that the cost of stabilising signals explain why effort is expensive.

Dissociable neural mechanisms track evidence accumulation for selection of attention versus action

Decision-making is typically studied as a sequential process from the selection of what to attend (e.g., between possible tasks, stimuli, or stimulus attributes) to which actions to take based on the attended information. However, people often process information across these various levels in parallel. Here we scan participants while they simultaneously weigh how much to attend to two dynamic stimulus attributes and what response to give. Regions of the prefrontal cortex track information about the stimulus attributes in dissociable ways, related to either the predicted reward (ventromedial prefrontal cortex) or the degree to which that attribute is being attended (dorsal anterior cingulate cortex, dACC). Within the dACC, adjacent regions track correlates of uncertainty at different levels of the decision, regarding what to attend versus how to respond. These findings bridge research on perceptual and value-based decision-making, demonstrating that people dynamically integrate information in parallel across different levels of decision-making.

Electrophysiological indices of anterior cingulate cortex function reveal changing levels of cognitive effort and reward valuation that sustain task performance

Successful execution of goal-directed behaviors often requires the deployment of cognitive control, which is thought to require cognitive effort. Recent theories have proposed that anterior cingulate cortex (ACC) regulates control levels by weighing the reward-related benefits of control against its effort-related costs. However, given that the sensations of cognitive effort and reward valuation are available only to introspection, this hypothesis is difficult to investigate empirically. We have proposed that two electrophysiological indices of ACC function, frontal midline theta and the reward positivity (RewP), provide objective measures of these functions. To explore this issue, we recorded the electroencephalogram (EEG) from participants engaged in an extended, cognitively-demanding task. Participants performed a time estimation task for 2 h in which they received reward and error feedback according to their task performance. We observed that the amplitude of the RewP, a feedback-locked component of the event related brain potential associated with reward processing, decreased with time-on-task. Conversely, frontal midline theta power, which consists of 4-8 Hz EEG oscillations associated with cognitive effort, increased with time-on-task. We also explored how these phenomena changed over time by conducting within-participant multi-level modeling analyses. Our results suggest that extended execution of a cognitively-demanding task is characterized by an early phase in which high control levels foster rapid improvements in task performance, and a later phase in which high control levels were necessary to maintain stable task performance, perhaps counteracting waning reward valuation.

Reward circuitry activation reflects social preferences in the face of cognitive effort

Research at the intersection of social neuroscience and cognitive effort is an interesting new area for exploration. There is great potential to broaden our understanding of how social context and cognitive effort processes, currently addressed in disparate literatures, interact with one another. In this paper, we briefly review the literature on cognitive effort, focusing on effort-linked valuation and the gap in the literature regarding cognitive effort in the social domain. Next, we present a study designed to explore valuation processes linked to cognitive effort within the social context of an inequality manipulation. More specifically, we created monetary inequality among the participant (SELF, endowed with $50) and two confederates: one also endowed with $50 (OTHER HIGH) and another with only $5 (OTHER LOW). We then scanned participants using fMRI as they attempted to earn bonus payments for themselves and others through a cognitively effortful feedback-based learning task. Positive feedback produced significantly greater activation than negative feedback in key valuation regions, the ventral striatum (VS) and ventromedial prefrontal cortex (vmPFC), both when participants were performing the task on their own behalf and when earning rewards for others. While reward-related activity in the VS was exaggerated for SELF compared to OTHER HIGH for both positive and negative feedback, activity in the vmPFC did not distinguish between recipients in the group-level results. Furthermore, participants naturally fell into two groups: those most engaged when playing for themselves and those who reported engagement for others. While Self-Engaged participants showed differences between the SELF and both OTHER conditions in the VS and vmPFC, Other-Engaged participants only showed an attenuated response to negative feedback for OTHER HIGH compared to SELF in the VS and no differences between recipient conditions in the vmPFC. Together, this work shows the importance of individual differences and the fragility of advantageous inequality aversion in the face of cognitive effort, highlighting the need to study cognitive effort in the social domain.

The feeling of effort during mental activity

The feeling of effort is familiar to most, if not all, humans. Prior research shows that the feeling of effort shapes judgments (e.g., of agency) and decisions (e.g., to quit the current task) in various ways, but the proximal causes of the feeling of effort are not well understood. In this research, I address these proximal causes. In particular, I conducted two preregistered experiments in which participants performed a difficult vs. easy cognitive task, while I measured effort-related phenomenology (feeling of effort) and physiology (pupil dilation) on a moment-to-moment basis. In both experiments, difficult tasks increased the feeling of effort; however, this effect could not be explained by concurrent increases in physiological effort. To explain these findings, I suggest that the feeling of effort during mental activity stems from the decision to exert physiological effort, rather than from physiological effort itself.

Adolescent Development of Value-Guided Goal Pursuit

Adolescents are challenged to orchestrate goal-directed actions in increasingly independent and consequential ways. In doing so, it is advantageous to use information about value to select which goals to pursue and how much effort to devote to them. Here, we examine age-related changes in how individuals use value signals to orchestrate goal-directed behavior. Drawing on emerging literature on value-guided cognitive control and reinforcement learning, we demonstrate how value and task difficulty modulate the execution of goal-directed action in complex ways across development from childhood to adulthood. We propose that the scope of value-guided goal pursuit expands with age to include increasingly challenging cognitive demands, and scaffolds on the emergence of functional integration within brain networks supporting valuation, cognition, and action.

The removal of information from working memory

What happens to goal-relevant information in working memory after it is no longer needed? Here, we review evidence for a selective removal process that operates on outdated information to limit working memory load and hence facilitates the maintenance of goal-relevant information. Removal alters the representations of irrelevant content so as to reduce access to it, thereby improving access to the remaining relevant content and also facilitating the encoding of new information. Both behavioral and neural evidence support the existence of a removal process that is separate from forgetting due to decay or interference. We discuss the potential mechanisms involved in removal and characterize the time course and duration of the process. In doing so, we propose the existence of two forms of removal: one is temporary, and reversible, which modifies working memory content without impacting content-to-context bindings, and another is permanent, which unbinds the content from its context in working memory (without necessarily impacting long-term forgetting). Finally, we discuss limitations on removal and prescribe conditions for evaluating evidence for or against this process.

The opportunity cost of time modulates cognitive effort

A spate of recent work demonstrates that humans seek to avoid the expenditure of cognitive effort, much like physical effort or economic resources. Less is clear, however, about the circumstances dictating how and when people decide to expend cognitive effort. Here we adopt a popular theory of opportunity costs and response vigor and to elucidate this question. This account, grounded in Reinforcement Learning, formalizes a trade-off between two costs: the harder work assumed necessary to emit faster actions and the opportunity cost inherent in acting more slowly (i.e., the delay that results to the next reward and subsequent rewards). Recent work reveals that the opportunity cost of time-operationalized as the average reward rate per unit time, theorized to be signaled by tonic dopamine levels, modulates the speed with which a person responds in a simple discrimination tasks. We extend this framework to cognitive effort in a diverse range of cognitive tasks, for which 1) the amount of cognitive effort demanded from the task varies from trial to trial and 2) the putative expenditure of cognitive effort holds measureable consequences in terms of accuracy and response time. In the domains of cognitive control, perceptual decision-making, and task-switching, we found that subjects tuned their level of effort exertion in accordance with the experienced average reward rate: when the opportunity cost of time was high, subjects made more errors and responded more quickly, which we interpret as a withdrawal of cognitive effort. That is, expenditure of cognitive effort appeared to be modulated by the opportunity cost of time. Further, and consistent with our account, the strength of this modulation was predicted by individual differences in efficacy of cognitive control. Taken together, our results elucidate the circumstances dictating how and when people expend cognitive effort.

Adaptive control and the avoidance of cognitive control demands across development

Young adults adaptively coordinate their behavior to avoid demands placed on cognitive control. We investigated how this adaptive coordination develops by having 6-7- and 11-12-year-olds and young adults complete a demand selection task, in which participants could select between two tasks that varied in cognitive control demands via differences in rule switch frequency. Adults and older children exhibited significant preference for selecting the less demanding task, as well as a metacognitive signal guiding adaptive demand avoidance behavior across a variety of behavioral and self-report assessments. In contrast, despite evidence of differential demands on cognitive control, younger children did not coordinate their task selections to avoid higher demand. Together, these findings suggest that sensitivity and adaptive responses to control demands emerge with development and are consistent with gradual development of lateral prefrontal cortex, dorsal anterior cingulate cortex, and their functional connectivity, which support effort avoidance in adults.

Differential effects of sustained and transient effort triggered by reward - A combined EEG and pupillometry study

In instrumental task contexts, incentive manipulations such as posting reward on successful performance usually trigger increased effort, which is signified by effort markers like increased pupil size. Yet, it is not fully clear under which circumstances incentives really promote performance, and which role effort plays therein. In the present study, we compared two schemes of associating reward with a Flanker task, while simultaneously acquiring electroencephalography (EEG) and pupillometry data in order to explore the contribution of effort-related processes. In Experiment 1, reward was administered in a block-based fashion, with series of targets in pure reward and no-reward blocks. The results imply increased sustained effort in the reward blocks, as reflected in particular in sustained increased pupil size. Yet, this was not accompanied by a behavioral benefit, suggesting a failure of translating increased effort into a behavioral pay-off. In Experiment 2, we introduced trial-based cues in order to also promote transient preparatory effort application, which indeed led to a behavioral benefit. Again, we observed a sustained pupil-size increase, but also transient ones. Consistent with this, the EEG data of Experiment 2 indicated increased transient preparatory effort preceding target onset, as well as reward modulations of target processing that arose earlier than in Experiment 1. Jointly, our results indicate that incentive-triggered effort can operate on different time-scales, and that, at least for the current task, its transient (and largely preparatory) form is critical for achieving a behavioral benefit, which may relate to the temporal dynamics of the catecholaminergic systems.

Rational metareasoning and the plasticity of cognitive control

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

The human brain has the impressive ability to adapt how it processes information to high level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we derive a computational model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert from a formal theory of the function of cognitive control. Across five experiments, we find that our model correctly predicts that people learn to adaptively regulate their attention and decision-making and how these learning effects transfer to novel situations. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

Planning Complexity Registers as a Cost in Metacontrol

Decision-making algorithms face a basic tradeoff between accuracy and effort (i.e., computational demands). It is widely agreed that humans can choose between multiple decision-making processes that embody different solutions to this tradeoff: Some are computationally cheap but inaccurate, whereas others are computationally expensive but accurate. Recent progress in understanding this tradeoff has been catalyzed by formalizing it in terms of model-free (i.e., habitual) versus model-based (i.e., planning) approaches to reinforcement learning. Intuitively, if two tasks offer the same rewards for accuracy but one of them is much more demanding, we might expect people to rely on habit more in the difficult task: Devoting significant computation to achieve slight marginal accuracy gains would not be "worth it." We test and verify this prediction in a sequential reinforcement learning task. Because our paradigm is amenable to formal analysis, it contributes to the development of a computational model of how people balance the costs and benefits of different decision-making processes in a task-specific manner; in other words, how we decide when hard thinking is worth it.

Heart work after errors: Behavioral adjustment following error commission involves cardiac effort

Posterror slowing (PES) is the observation that people respond slower on trials subsequent to error commissions than on trials subsequent to correct responses. Different accounts have been proposed to explain PES. On the one hand, it has been suggested that PES arises from an adaptive increase in cognitive control following error commission, thereby making people more cautious after making an error. On the other hand, PES has been attributed to an orienting response, indicating that attention is shifted toward the error. In the present study we tested these accounts by investigating the effects of error commission in both flanker and switch tasks on two task-evoked cardiac measures: the interbeat interval-that is, the interval between two consecutive R peaks-and the RZ interval-that is, the interval between the R peak and the Z point-as measured using electro- and impedance cardiography, respectively. These measures allowed us to measure cardiac deceleration (autonomic orienting) and cardiac effort mobilization, respectively. Our results revealed a shorter RZ interval during posterror trials, indicating increased effort mobilization following errors. In addition, we replicated earlier studies that have shown cardiac slowing during error trials. However, multilevel analyses showed that only the posterror decrease in RZ interval predicted posterror reaction times, whereas there was no positive relationship between error-related cardiac deceleration and posterror reaction times. Our results suggest that PES is related to increased cardiac effort, supporting a cognitive-control account of PES.

The Effort Paradox: Effort Is Both Costly and Valued

According to prominent models in cognitive psychology, neuroscience, and economics, effort (be it physical or mental) is costly: when given a choice, humans and non-human animals alike tend to avoid effort. Here, we suggest that the opposite is also true and review extensive evidence that effort can also add value. Not only can the same outcomes be more rewarding if we apply more (not less) effort, sometimes we select options precisely because they require effort. Given the increasing recognition of effort's role in motivation, cognitive control, and value-based decision-making, considering this neglected side of effort will not only improve formal computational models, but also provide clues about how to promote sustained mental effort across time.

Neural Mechanisms for Adaptive Learned Avoidance of Mental Effort

Humans tend to avoid mental effort. Previous studies have demonstrated this tendency using various demand-selection tasks; participants generally avoid options associated with higher cognitive demand. However, it remains unclear whether humans avoid mental effort adaptively in uncertain and nonstationary environments. If so, it also remains unclear what neural mechanisms underlie such learned avoidance and whether they remain the same regardless of cognitive-demand types. We addressed these issues by developing novel demand-selection tasks where associations between choice options and cognitive-demand levels change over time, with two variations using mental arithmetic and spatial reasoning problems (males/females: 29:4 and 18:2). Most participants showed avoidance, and their choices depended on the demand experienced on multiple preceding trials. We assumed that participants updated the expected cost of mental effort through experience, and fitted their choices by reinforcement learning models, comparing several possibilities. Model-based fMRI analyses revealed that activity in the dorsomedial and lateral frontal cortices was positively correlated with the trial-by-trial expected cost for the chosen option commonly across the different types of cognitive demand. Analyses also revealed a trend of negative correlation in the ventromedial prefrontal cortex. We further identified correlates of cost-prediction error at time of problem presentation or answering the problem, the latter of which partially overlapped with or were proximal to the correlates of expected cost at time of choice cue in the dorsomedial frontal cortex. These results suggest that humans adaptively learn to avoid mental effort, having neural mechanisms to represent expected cost and cost-prediction error, and the same mechanisms operate for various types of cognitive demand.SIGNIFICANCE STATEMENT In daily life, humans encounter various cognitive demands and tend to avoid high-demand options. However, it remains unclear whether humans avoid mental effort adaptively under dynamically changing environments. If so, it also remains unclear what the underlying neural mechanisms are and whether they operate regardless of cognitive-demand types. To address these issues, we developed novel tasks where participants could learn to avoid high-demand options under uncertain and nonstationary environments. Through model-based fMRI analyses, we found regions whose activity was correlated with the expected mental effort cost, or cost-prediction error, regardless of demand type. These regions overlap, or are adjacent with each other, in the dorsomedial frontal cortex. This finding helps clarify the mechanisms for cognitive-demand avoidance, and provides empirical building blocks for the emerging computational theory of mental effort.

Interactions of Motivation and Cognitive Control

There is general agreement that both motivation and cognitive control play critical roles in shaping goal-directed behavior, but only recently has scientific interest focused around the question of motivation-control interactions. Here we briefly survey this literature, organizing contemporary findings around three issues: 1) whether motivation preferentially impacts cognitive control processes, 2) the neural mechanisms that underlie motivation-cognition interactions, and 3) why motivation might be relevant for overcoming the costs of control. Dopamine (DA) is discussed as a key neuromodulator in these motivation-cognition interactions. We conclude by highlighting open issues, specifically Pavlovian versus instrumental control distinctions and effects of motivational valence and conflict, which could benefit from future research attention.

Interrupting behaviour: Minimizing decision costs via temporal commitment and low-level interrupts

Ideal decision-makers should constantly assess all sources of information about opportunities and threats, and be able to redetermine their choices promptly in the face of change. However, perpetual monitoring and reassessment impose inordinate sensing and computational costs, making them impractical for animals and machines alike. The obvious alternative of committing for extended periods of time to limited sensory strategies associated with particular courses of action can be dangerous and wasteful. Here, we explore the intermediate possibility of making provisional temporal commitments whilst admitting interruption based on limited broader observation. We simulate foraging under threat of predation to elucidate the benefits of such a scheme. We relate our results to diseases of distractibility and roving attention, and consider mechanistic substrates such as noradrenergic neuromodulation.

Animals should ideally be able to monitor all relevant aspects of their environment constantly and be ever prepared to alter their course of action in the face of unexpected change. However, the impractically high costs of continual monitoring and deliberation mean that a more realistic strategy is required. Here, we explore a solution in which an animal makes provisional commitments to a temporally-extended action while maintaining the ability to interrupt this behaviour prematurely on the basis of more limited sensing. We demonstrate the benefits of such a scheme through the example of foraging under predation risk, and propose a simple mechanism for implementing interruption. We suggest possible relationships between these results and neural substrates, particularly norepinephrine, and also highlight potential relevance to diseases of distractibility.

Dopamine and Proximity in Motivation and Cognitive Control

Cognitive control - the ability to override a salient or prepotent action to execute a more deliberate one - is required for flexible, goal-directed behavior, and yet it is subjectively costly: decision-makers avoid allocating control resources, even when doing so affords more valuable outcomes. Dopamine likely offsets effort costs just as it does for physical effort. And yet, dopamine can also promote impulsive action, undermining control. We propose a novel hypothesis that reconciles opposing effects of dopamine on cognitive control: during action selection, striatal dopamine biases benefits relative to costs, but does so preferentially for "proximal" motor and cognitive actions. Considering the nature of instrumental affordances and their dynamics during action selection facilitates a parsimonious interpretation and conserved corticostriatal mechanisms across physical and cognitive domains.

Causal Evidence for Learning-Dependent Frontal Lobe Contributions to Cognitive Control

The lateral prefrontal cortex (LPFC) plays a central role in the prioritization of sensory input based on task relevance. Such top-down control of perception is of fundamental importance in goal-directed behavior, but can also be costly when deployed excessively, necessitating a mechanism that regulates control engagement to align it with changing environmental demands. We have recently introduced the "flexible control model" (FCM), which explains this regulation as resulting from a self-adjusting reinforcement-learning mechanism that infers latent statistical structure in dynamic task environments to predict forthcoming states. From this perspective, LPFC-based control is engaged as a function of anticipated cognitive demand, a notion for which we previously obtained correlative neuroimaging evidence. Here, we put this hypothesis to a rigorous, causal test by combining the FCM with a transcranial magnetic stimulation (TMS) intervention that transiently perturbed the LPFC. Human participants (male and female) completed a nonstationary version of the Stroop task with dynamically changing probabilities of conflict between task-relevant and task-irrelevant stimulus features. TMS was given on each trial before stimulus onset either over the LPFC or over a control site. In the control condition, we observed adaptive performance fluctuations consistent with demand predictions that were inferred from recent and remote trial history and effectively captured by our model. Critically, TMS over the LPFC eliminated these fluctuations while leaving basic cognitive and motor functions intact. These results provide causal evidence for a learning-based account of cognitive control and delineate the nature of the signals that regulate top-down biases over stimulus processing.SIGNIFICANCE STATEMENT A core function of the human prefrontal cortex is to control the signal flow in sensory brain regions to prioritize processing of task-relevant information. Abundant work suggests that such control is flexibly recruited to accommodate dynamically changing environmental demands, yet the nature of the signals that serve to engage control remains unknown. Here, we combined computational modeling with noninvasive brain stimulation to show that changes in control engagement are captured by a self-adjusting reinforcement-learning mechanism that tracks changing environmental statistics to predict forthcoming processing demands and that transient perturbation of the prefrontal cortex abolishes these adjustments. These findings delineate the learning signals that underpin adaptive engagement of prefrontal control functions and provide causal evidence for their relevance in behavioral control.

Voluntary modulation of mental effort investment: an fMRI study

Mental effort is a common phenomenological construct deeply linked to volition and self-control. While it is often assumed that the amount of exertion invested in a task can be voluntarily regulated, the neural bases of such faculty and its behavioural effects are yet insufficiently understood. In this study, we investigated how the instructions to execute a demanding cognitive task either “with maximum exertion” or “as relaxed as possible” affected performance and brain activity. The maximum exertion condition, compared to relaxed execution, was associated with speeded motor responses without an accuracy trade-off, and an amplification of both task-related activations in dorsal frontoparietal and cerebellar regions, and task-related deactivations in default mode network (DMN) areas. Furthermore, the visual cue to engage maximum effort triggered an anticipatory widespread increase of activity in attentional, sensory and executive regions, with its peak in the brain stem reticular activating system. Across individuals, this surge of activity in the brain stem, but also in medial wall cortical regions projecting to the adrenal medulla, positively correlated with increases in heart rate, suggesting that the intention to willfully modulate invested effort involves mechanisms related to catecholaminergic transmission and a suppression of DMN activity in favor of externally-directed attentional processes.

Anticipating cognitive effort: roles of perceived error-likelihood and time demands

Why are some actions evaluated as effortful? In the present set of experiments we address this question by examining individuals' perception of effort when faced with a trade-off between two putative cognitive costs: how much time a task takes vs. how error-prone it is. Specifically, we were interested in whether individuals anticipate engaging in a small amount of hard work (i.e., low time requirement, but high error-likelihood) vs. a large amount of easy work (i.e., high time requirement, but low error-likelihood) as being more effortful. In between-subject designs, Experiments 1 through 3 demonstrated that individuals anticipate options that are high in perceived error-likelihood (yet less time consuming) as more effortful than options that are perceived to be more time consuming (yet low in error-likelihood). Further, when asked to evaluate which of the two tasks was (a) more effortful, (b) more error-prone, and (c) more time consuming, effort-based and error-based choices closely tracked one another, but this was not the case for time-based choices. Utilizing a within-subject design, Experiment 4 demonstrated overall similar pattern of judgments as Experiments 1 through 3. However, both judgments of error-likelihood and time demand similarly predicted effort judgments. Results are discussed within the context of extant accounts of cognitive control, with considerations of how error-likelihood and time demands may independently and conjunctively factor into judgments of cognitive effort.

Self-Control as Value-Based Choice

Self-control is often conceived as a battle between "hot" impulsive processes and "cold" deliberative ones. Heeding the angel on one shoulder leads to success; following the demon on the other leads to failure. Self-control feels like a duality. What if that sensation is misleading, and, despite how they feel, self-control decisions are just like any other choice? We argue that self-control is a form of value-based choice wherein options are assigned a subjective value and a decision is made through a dynamic integration process. We articulate how a value-based choice model of self-control can capture its phenomenology and account for relevant behavioral and neuroscientific data. This conceptualization of self-control links divergent scientific approaches, allows for more robust and precise hypothesis testing, and suggests novel pathways to improve self-control.

Assessing the role of reward in task selection using a reward-based voluntary task switching paradigm

People exhibit a remarkable ability to both maintain controlled focus on executing a single task and flexibly shift between executing several tasks. Researchers studying human multitasking have traditionally focused on the cognitive control mechanisms that allow for such stable and flexible task execution, but there has been a recent interest in how cognitive control mechanisms drive the decision of task selection. The present research operationalizes a foraging analogy to investigate what factors drive the decision to either exploit task repetitions or explore task switches. A novel paradigm-reward-based voluntary task switching-ascribes point values to tasks where the overall goal is to accumulate points as quickly as possible. The reward structure generally rewards switching tasks, thereby juxtaposing the motivation to gain increased reward (by exploring task switches) against the motivation to perform quickly (by exploiting task repetitions). Results suggest that people are highly sensitive to changes in both reward and effort demands when making task selections, and that the task selection process is efficient and flexible. We argue that a cost-benefit mechanism might underlie decisions in multitasking contexts, whereby people compute task selections based on both the reward available for selecting a task and the effort necessary to execute a task.

Cognitive persistence: Development and validation of a novel measure from the Wisconsin Card Sorting Test

The Wisconsin Card Sorting Test (WCST) has long been used as a neuropsychological assessment of executive function abilities, in particular, cognitive flexibility or "set-shifting". Recent advances in scoring the task have helped to isolate specific WCST performance metrics that index set-shifting abilities and have improved our understanding of how prefrontal and parietal cortex contribute to set-shifting. We present evidence that the ability to overcome task difficulty to achieve a goal, or "cognitive persistence", is another important prefrontal function that is characterized by the WCST and that can be differentiated from efficient set-shifting. This novel measure of cognitive persistence was developed using the WCST-64 in an adult lifespan sample of 230 participants. The measure was validated using individual variation in cingulo-opercular cortex function in a sub-sample of older adults who had completed a challenging speech recognition in noise fMRI task. Specifically, older adults with higher cognitive persistence were more likely to demonstrate word recognition benefit from cingulo-opercular activity. The WCST-derived cognitive persistence measure can be used to disentangle neural processes involved in set-shifting from those involved in persistence.