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Abstract Stable individual differences in cognitive motivation (i.e., the tendency to engage in and enjoy effortful cognitive activities) have been documented with self-report measures, yet convergent support for a trait-level construct is still lacking. In the present study, we used an innovative decision-making paradigm (COG-ED) to quantify the costs of cognitive effort, a metric of cognitive motivation, across two distinct cognitive domains: working memory (an N-back task) and speech comprehension (understanding spoken sentences in background noise). We hypothesized that cognitive motivation operates similarly within individuals, regardless of domain. Specifically, in 104 adults aged 18–40 years, we tested whether individual differences in effort costs are stable across domains, even after controlling for other potential sources of shared individual variation. Conversely, we evaluated whether the costs of cognitive effort across domains may be better explained in terms of other relevant cognitive and personality-related constructs, such as working memory capacity or reward sensitivity. We confirmed a reliable association among effort costs in both domains, even when these other sources of individual variation, as well as task load, are statistically controlled. Taken together, these results add support for trait-level variation in cognitive motivation impacting effort-based decision making across multiple domains.

Measurement(s) Blood Oxygen Level-Dependent Functional MRI Technology Type(s) functional MRI scanner Factor Type(s) cognitive control tasks Sample Characteristic - Organism Homo Sample Characteristic - Environment laboratory facility Sample Characteristic - Location contiguous United States of America

Summary: This protocol describes the materials and approaches for administering liquid incentives to human participants during fMRI scanning. We first describe preparation of the liquid solutions (e.g., neutral solution and saltwater) and liquid delivery setups. We then detail steps to connect the setups to the computer-controlled syringe pump in the MRI control room, followed by procedures for testing the syringe pump dispensing a liquid bolus during the task. Description of custom software and required adapters for implementing the liquid setup are included.For complete details on the use and execution of this protocol, please refer to Yee et al. (2021). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Stable individual differences in cognitive motivation (i.e., the tendency to engage in and enjoy effortful cognitive activities) have been documented with self-report measures, yet convergent support for a trait-level construct is still lacking. In the present study, we use an innovative decision-making paradigm (COG-ED) to quantify the costs of cognitive effort, a metric of cognitive motivation, across two distinct cognitive domains (working memory and speech comprehension). We hypothesize that cognitive motivation operates similarly within individuals, regardless of domain. Specifically, we test whether individual differences in effort costs are stable across domains, even after controlling for other potential sources of shared individual variation. Conversely, we evaluate whether the costs of cognitive effort across domains may be better explained in terms of other relevant cognitive and personality-related constructs, such as working memory capacity or reward sensitivity.

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first “filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.

Studies of working memory (WM) function have tended to adopt either a within-subject approach, focusing on effects of load manipulations, or a between-subjects approach, focusing on individual differences. This dichotomy extends to WM neuroimaging studies, with different neural correlates being identified for within- and between-subjects variation in WM. Here, we examined this issue in a systematic fashion, leveraging the large-sample Human Connectome Project dataset, to conduct a well-powered, whole-brain analysis of the N-back WM task. We first demonstrate the advantages of parcellation schemes for dimension reduction, in terms of load-related effect sizes. This parcel-based approach is then utilized to directly compare the relationship between load-related (within-subject) and behavioral individual differences (between-subject) effects through both correlational and predictive analyses. The results suggest a strong linkage of within-subject and between-subject variation, with larger load-effects linked to stronger brain-behavior correlations. In frontoparietal cortex no hemispheric biases were found towards one type of variation, but the Dorsal Attention Network did exhibit greater sensitivity to between over within-subjects variation, whereas in the Somatomotor network, the reverse pattern was observed.Cross-validated predictive modeling capitalizing on this tight relationship between the two effects indicated greater predictive power for load-activated than load-deactivated parcels, while also demonstrating that load-related effect size can serve as an effective guide to feature (i.e., parcel) selection, in maximizing predictive power while maintaining interpretability. Together, the findings demonstrate an important consistency across within- and between-subjects approaches to identifying the neural substrates of WM, which can be effectively harnessed to develop more powerful predictive models.

The ability to flexibly adapt thoughts and actions in a goal-directed manner appears to rely on cognitive control mechanisms that are strongly impacted by individual differences. A powerful research strategy for investigating the nature of individual variation is to study monozygotic (identical) twins. Evidence of twin effects have been observed in prior behavioral and neuroimaging studies, yet within the domain of cognitive control, it remains to be demonstrated that the neural underpinnings of such effects are specific and reliable. Here, we utilize a multi-task, within-subjects event-related neuroimaging design with functional magnetic resonance imaging to investigate twin effects through multivariate pattern similarity analyses. We focus on fronto-parietal brain regions exhibiting consistently increased activation associated with cognitive control demands across four task domains: selective attention, context processing, multi-tasking, and working memory. Healthy young adult monozygotic twin pairs exhibited increased similarity of within- and cross-task activation patterns in these fronto-parietal regions, relative to unrelated pairs. Twin activation pattern similarity effects were clearest under high control demands, were not present in a set of task-unrelated parcels or due to anatomic similarity, and were primarily observed during the within-trial timepoints in which the control demands peaked. Together, these results indicate that twin similarity in the neural representation of cognitive control may be domain-general but also functionally and temporally specific in relation to the level of control demand. The findings suggest a genetic and/or environmental basis for individual variation in cognitive control function, and highlight the potential of twin-based neuroimaging designs for exploring heritability questions within this domain.

A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1–3 ​min per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

Humans are social creatures and, as such, can be motivated by aspects of social life (e.g., approval from others) to guide decision-making in everyday contexts. Indeed, a common view is that people may have stronger orientation toward social goals or incentives relative to other incentive modalities, such as food or money. However, current studies have only rarely addressed how social incentives compare to other types of rewards in motivating goal-directed behavior. The current study tested this claim; across two separate experiments, the effects of liquid and social incentives were compared in terms of their subsequent impact on task performance and self-reported affect and motivation. Critically, valenced social incentives offered both ecological validity (short video clips—Experiment 1) and continuity with prior stimuli used in the social reward and motivation literature (static images—Experiment 2) when examining their effect on behavior. Across both studies, the results replicate and extend prior work, demonstrating robust effects of liquid incentives on task performance and self-reported affect and motivation, while also supporting an interpretation of weaker motivational and affective effects for social incentives. These patterns of results highlight the complex and wide-ranging effects of social incentives and call into question the effectiveness of social incentives, relative to other incentive modalities, in motivating behavior.

The growing popularity of mindfulness-based interventions (MBIs) has prompted exciting scientific research investigating their beneficial effects on well-being and health. Most mindfulness programs are provided as multi-faceted packages encompassing a set of different mindfulness techniques, each with distinct focus and mechanisms. However, this approach overlooks potential individual differences, which may arise in response to practicing various mindfulness techniques. The present study investigated preferences for four prototypical mindfulness techniques [focused attention (FA), open monitoring (OM), loving-kindness (LK), and body scan (BS)] and identified factors that may contribute to individual differences in these preferences. Participants without prior mindfulness experiences were exposed to each technique through audio-guided instructions and were asked to rank their preferences at the end of all practices. Results indicated that preferences for loving-kindness were predicted by empathy, and that females tended to prefer loving-kindness more than males. Conversely, preferences for open monitoring were predicted by nonreactivity and nonjudgment of present moment experiences. Additionally, higher state mindfulness was detected for individuals’ preferred technique relative to other alternatives. These findings suggest that individuals tend to prefer techniques compatible with their personalities, as the predictor variables encompass trait capacities specifically relevant to practicing these techniques. Together, our results suggest the possibility that assessing individual difference and then tailoring MBIs to individual needs could be a useful way to improve intervention effectiveness and subsequent outcomes.

Working memory (WM) function has traditionally been investigated in terms of two dimensions: within-individual effects of WM load, and between-individual differences in task performance. In human neuroimaging studies, the N-back task has frequently been used to study both. A reliable finding is that activation in frontoparietal regions exhibits an inverted-U pattern, such that activity tends to decrease at high load levels. Yet it is not known whether such U-shaped patterns are a key individual differences factor that can predict load-related changes in task performance. The current study investigated this question by manipulating load levels across a much wider range than explored previously (N ​= ​1–6), and providing a more comprehensive examination of brain-behavior relationships. In a sample of healthy young adults (n ​= ​57), the analysis focused on a distinct region of left lateral prefrontal cortex (LPFC) identified in prior work to show a unique relationship with task performance and WM function. In this region it was the linear slope of load-related activity, rather than the U-shaped pattern, that was positively associated with individual differences in target accuracy. Comprehensive supplemental analyses revealed the brain-wide selectivity of this pattern. Target accuracy was also independently predicted by the global resting-state connectivity of this LPFC region. These effects were robust, as demonstrated by cross-validation analyses and out-of-sample prediction, and also critically, were primarily driven by the high-load conditions. Together, the results highlight the utility of high-load conditions for investigating individual differences in WM function.

A growing body of research indicates that mindfulness training can have beneficial effects on critical aspects of psychological well-being, cognitive function, and brain health. Although these benefits have been generalized to the population level, individual variability in observed effects of mindfulness training has not been systematically investigated. Research on other similar forms of psychological intervention demonstrates that individual differences are prominent in terms of intervention responsiveness and outcomes. Furthermore, individual characteristics such as personality traits have been shown to play a crucial role in influencing the effects of intervention. In light of these lines of evidence, we review representative work on individual differences in mindfulness training and advocate for an individual difference perspective in mindfulness training research. We discuss relevant empirical evidence of individual differences potentially influencing behavioral outcomes of mindfulness training, focusing on both cognitive function and psychological well-being. Finally, theoretical considerations and potentially fruitful research strategies and directions for studying individual differences in mindfulness training are discussed, including those involving cognitive neuroscience methods.

Previous research has linked working memory capacity (WMC) with enhanced proactive control. However, it remains unclear the extent to which this relationship reflects the influence of WMC on the tendency to engage proactive control, or rather, the ability to implement it. The current study sought to clarify this ambiguity by leveraging the Dual Mechanisms of Cognitive Control (DMCC) version of the AX-CPT task, in which the mode of cognitive control is experimentally manipulated across distinct testing sessions. To adjudicate between competing hypotheses, Bayesian mixed modeling was used to conduct sequential analyses involving two separate data sets. Posterior parameter estimates obtained from the initial analysis were entered as informed priors during the replication analysis to evaluate the influence of new data on previous estimates. Results yielded strong evidence demonstrating that the influence of WMC on proactive control is most robust under experimentally controlled conditions, during which use of proactive control is standardized across participants via explicit training and instruction. Critically, the observed pattern of findings suggests that the relationship between WMC and proactive control may be better characterized as individual differences in the ability to implement proactive control, rather than a more generalized tendency to engage it.

Investigating individual differences in cognition requires addressing questions not often thought about in standard experimental designs, especially regarding the psychometric properties of the task. Using the AX-CPT cognitive control task as a case study example, we address four concerns that one may encounter when researching the topic of individual differences in cognition. First, we demonstrate the importance of variability in task scores, which in turn directly impacts reliability, particularly when comparing correlations in different populations. Second, we demonstrate the importance of variability and reliability for evaluating potential failures to replicate predicted correlations, even within the same population. Third, we demonstrate how researchers can turn to evaluating psychometric properties as a way of evaluating the feasibility of utilizing the task in new settings (e.g., online administration). Lastly, we show how the examination of psychometric properties can help researchers make informed decisions when designing a study, such as determining the appropriate number of trials for a task.

The Dual Mechanisms of Control (DMC) account (Braver, 2012) proposes two distinct mechanisms of cognitive control, proactive and reactive. This account has been supported by a large number of studies using the AX-CPT paradigm that have demonstrated not only between-group differences, but also within-subjects variability in the use of the two control mechanisms. Yet there has been little investigation of task manipulations that can experimentally modulate the use of proactive control in healthy young adults; such manipulations could be useful to better understand the workings of cognitive control mechanisms. In the current study, a series of three experiments demonstrate how individuals can be systematically biased towards and away from the utilization of proactive control, via strategy training and no-go manipulations, respectively. These results provide increased support for the DMC framework, and provide a new basis from which to examine group-based differences and neural mechanisms underlying the two control modes.