Todd Samuel Braver

Making choices about whether and when to engage cognitive effort is a common feature of human everyday experience with important consequences for academic, career, and health outcomes. Yet, despite its importance, little is understood about the neurochemical mechanisms that underlie individual differences in the willingness to engage in cognitively effortful activities. To examine this question, we employed a multi-modal neuroimaging approach in which participants performed the Cognitive Effort Discounting paradigm (Cog-ED) while simultaneous PET-fMRI was acquired. We assessed both individual differences in dopamine-D2 receptor availability and brain activity during decision-making. In addition, the same participants performed a parametric working memory task (N-Back) and reported the cognitive effort associated with their daily life activities through a seven-day Ecological Momentary Assessment (EMA) protocol. The findings suggest a central role for caudate dopamine-D2 receptors in modulating cognitive effort-related motivation. People with higher caudate dopamine-D2 receptor availability had better working memory function, heightened sensitivity to effort costs, and greater modulation of brain activity during effort valuation. These laboratory findings also translated directly to daily life: higher levels of caudate dopamine-D2 receptors were associated with greater engagement in cognitively effortful activities. Together, the results suggest that how individuals determine the motivational value of cognitively effortful activities strongly depends on the dopamine system. Significance Statement: Weighing the costs and benefits of engaging in cognitive effort is a common feature of daily life. Nevertheless, there is little consensus about how such decisions are implemented in the human brain. This study used a cognitive effort decision-making paradigm during simultaneous PET-fMRI scanning to identify the neurochemical mechanisms engaged while participants decided whether to engage in cognitive effort. Neuroimaging measures were paired with daily life assessments of cognitive effort to directly test if individual differences in dopamine function translate to how people make daily life decisions. The results provide some of the first evidence that dopamine-D2 receptors in the dorsal striatum play an important role in cognitive effort-based decision-making, not only in the laboratory, but also in daily life.

Cognitive control is central to decision-making and goal-directed behavior, but the mental effort it requires is often perceived as costly, especially in the context of aging. Although age-related differences in cognitive effort discounting have primarily been studied in healthy adults, major depressive disorder (MDD), which is associated with altered reward valuation, may modify these patterns. In this preregistered study, we examined whether age was differentially associated with cognitive effort discounting in adults with and without MDD (N=134, ages 35-74). We first estimated subjective value (SV), indexing cognitive effort costs, then assessed choices between options that varied in SV. Replicating prior work, SV declined as cognitive load increased, suggesting less willingness to engage in more effortful tasks. Age-related differences in this discounting pattern were only evident among individuals with MDD but not among healthy controls. Specifically, among individuals with MDD, older age was associated with lower SV at low loads, but a shallower decline in SV as difficulty increased. Across diagnostic groups, the positive association between task accuracy and SV weakened with older age. Finally, the relative SV of the more cognitively demanding task (compared to the less demanding task) predicted the willingness to engage with it. This association was also weakened with age across diagnostic groups, suggesting that as individuals get older, their decisions may be less guided by the SV difference between high- and low-effort options. Together, these findings showcase the combined and distinct effects of aging and MDD in altering the mechanisms of cognitive effort evaluation and decision-making.

Dynamical systems models such as recurrent neural networks (RNNs) are increasingly popular in theoretical neuroscience as a vehicle for hypothesis generation and data analysis. Evaluating the dynamics in such models is key to understanding their learned generative mechanisms. However, such evaluation is impeded by two major challenges: (1) comparison of learned dynamics across models is difficult because a priori there is no enforced equivalence of their coordinate systems, and (2) identification of mechanistically important low-dimensional motifs (e.g., limit sets) is analytically intractable in high-dimensional nonlinear models such as RNNs. Here, we propose a comprehensive framework to address these two issues, termed diffeomorphic vector field alignment for learned models (DFORM). DFORM learns a nonlinear coordinate transformation between the state spaces of two dynamical systems, which aligns their trajectories in a maximally one-to-one manner. In so doing, DFORM enables an assessment of whether a set of models exhibits topological equivalence, that is, their dynamics are mechanistically similar despite differences in their coordinate systems. A by-product of this methodology is a means to locate dynamical motifs on low-dimensional manifolds embedded within higher-dimensional systems. We verified DFORM’s ability to identify linear and nonlinear coordinate transformations using canonical topologically equivalent systems, RNNs, and systems related by nonlinear flows. DFORM was also shown to provide a quantification of similarity between topologically distinct systems. We then demonstrated that DFORM can locate important dynamical motifs including invariant manifolds and saddle limit sets within high-dimensional models. Finally, using a set of RNN models trained on human functional magnetic resonance imaging recordings, we illustrated that DFORM can identify limit cycles from high-dimensional data-driven models, which agreed well with prior numerical analysis.

The Dual Mechanisms of Control (DMC) framework postulates two distinct and semi-independent modes of cognitive control. Proactive control is hypothesized to be anticipatory, engaged prior to stimulus onset, whereas reactive control is triggered by stimulus or response features. In the current study, we test a key prediction of this framework: that individual differences in proactive control and reactive control should be weakly correlated, if at all. There is a key challenge faced by correlational analyses: measurement error will systematically lower correlation values; so much so that observing a small (or non-existent) correlation is difficult to interpret. To address this challenge, we develop a hierarchical model that appropriately estimates measurement error and individual covariation simultaneously. Drawing on a recent large-sample dataset that experimentally dissociates proactive and reactive control during the Stroop color word task (Ileri-Tayar et al, 2025), we demonstrate that hierarchical-model-based analyses not only disattenuate correlations from the impact of measurement error noise, but also provide trustworthy credible intervals that are better suited for statistical inference. With measurement error properly estimated and removed, we find evidence that proactive and reactive control indices are only weakly related, consistent with the DMC framework. This work showcases the advantages of hierarchical models for individual differences research, by providing a blueprint for how researchers can accurately estimate correlations as well as measurement-related uncertainty in low-reliability task contexts.

Across the last three decades, functional magnetic resonance imaging (fMRI) research - through both resting-state (rsfMRI) and task-based (tfMRI) studies - has greatly advanced our understanding regarding the neural basis of cognition. Yet the mechanistic relationship between rsfMRI and tfMRI is still poorly understood. In particular, it remains unclear how and why the brain activation patterns observed during the resting state are linked to cognitive functioning and individual differences present during task performance. Here, we test a unifying computational account which postulates that task contexts modulate the nonlinear attractor landscape and associated dynamical properties of the brain present under resting conditions, and further that the nature of this modulation is impacted by meaningful cognitive individual differences. To test this account, we develop a joint rsfMRI-tfMRI modeling and analysis framework called Mesoscale Individualized NeuroDynamics with eXogenous inputs (MINDy-X) and apply it to resting and N-back working memory task data from the Human Connectome Project. We first validated that the joint model can simulate and predict both rsfMRI and tfMRI data accurately, consistent with a common underlying dynamical system. Analyses of this joint model revealed that task-related modulation bifurcated the predominantly multistable attractor dynamics present during the resting state towards a predominantly monostable dynamics observed during N-back task states. This topological shift was also accompanied by a geometric reconfiguration, with the task state characterized by an enrichment of dynamical attractor "motifs" clustered around the frontoparietal (FPN) and default mode (DMN) networks. Task-related modulations of this attractor landscape were further subject to clear individual differences, such that individuals who did not exhibit a shift in attractor topology were more error-prone and less cautious in responding, while closer geometric proximity to the FPN and DMN motifs explained additional aspects of task performance. N-back behavior was best characterized by the combination of topological and geometric properties present in both task and rest states, suggesting that they each account for unique aspects of individual variability. The current work supports a novel computational framework for understanding the whole-brain neural activity patterns observed during rsfMRI and tfMRI as reflecting different states within a common non-linear dynamical system. This framework provides a new vocabulary for characterizing cognitive functioning in terms of the unique geometric and topological configuration of the associated attractor landscapes, with the potential for wide application in many domains of basic and clinical neuroscience research.

Neural oscillatory activity within the alpha and theta bands have long been considered putative markers of state mindfulness, yet understanding of their functional role has been limited by the challenges of linking objective brain indices with subjective experience. To address this gap, the current study applied longitudinal network analysis to a unique dataset from sixteen novices who completed up to 24 laboratory training sessions of both focused attention (FA) and open monitoring (OM) meditation, during which both EEG and self-report measures of state mindfulness quality were collected. This approach enabled the parsimonious characterization of both cross-lagged temporal (across-session) and contemporaneous (within-session) influences of regional spectral power on state mindfulness. The analysis revealed distinguishable neurophenomenological network structures for each practice, providing data-driven support for their theoretical differentiation. These distinctions emerged alongside shared commonalities to both practices, including strong autoregressive effects for state mindfulness, consistent with training-related skill acquisition, and opposing regional influences of frontal vs. posterior alpha power. Taken together, these findings challenge monolithic interpretations of meditation related EEG activity, advancing a more nuanced neurophenomenological approach wherein the functional significance of neural activity is dynamically situated within the specific type and time course of training.

Large-scale brain activity reflects changes in attention. To understand how, we tested a hypothesis that the geometry of neural dynamics on the cortical attractor landscape, or movement along its “hills and valleys”, reflects attentional states. A dynamical systems model separating intrinsic dynamics from stimulus-driven influences was fit to fMRI data collected during rest, tasks, and movie-watching. Model simulations revealed a set of attractors aligned with canonical functional brain networks. The speed and direction of neural trajectories toward these attractors varied systematically with attentional states over time and across contexts. When participants were paying attention to effortful tasks, neural dynamics converged fast and directly toward a task-relevant attractor. In contrast, when participants were engaged in sitcom episodes, neural dynamics occupied a flatter region of the landscape, directed away from attractors. These findings demonstrate that while attractor locations are largely determined by cortical organization, the geometry of neural dynamics changes systematically across attentional states and contexts.

Objectives: Focused attention (FA) and open monitoring (OM) are distinct mindfulness practices that produce unique psychological effects. Prior research has frequently investigated the neural correlates of FA and OM states utilizing electroencephalographic (EEG) methods, focusing on changes in spectral power within theta and alpha bands. Yet the functional significance of these neural changes has remained unclear. Here we utilized a fully-within subject state induction protocol to more directly test whether EEG spectral power during FA and OM is differentially associated with subjective ratings of state mindfulness. Methods: While continuous EEG was recorded, participants engaged in audio-guided FA and OM practices, as well as an active control condition (C), and then self-reported their state mindfulness afterwards. Linear mixed effects models were used to rigorously assess how condition-level variation in spectral power predicted state mindfulness scores across the three inductions. Results: Validating the approach, participants reported higher state mindfulness and decreased theta power during both FA and OM relative to C; additionally, reduced alpha power was found in OM relative to FA. Most importantly, increased theta power was associated with higher state mindfulness in FA and even more strongly in OM, whereas reduced alpha power was linked to higher state mindfulness selectively in OM. Conclusions: These findings add to the growing literature suggesting that FA and OM represent distinct mindfulness states. We further establish that the functional significance of theta and alpha power is context-dependent, clearly linking these neural measures to the subjective quality of specific meditation states and practices.

Rapid instructed task learning (RITL), the ability to quickly learn and implement a new task from instructions, is hypothesized to rely on proactive cognitive control. In three experiments, we examined self-paced preparation time as an index of proactive control during the performance of novel versus previously practiced tasks. We hypothesized that both between individuals and on a trial-by-trial basis, longer preparation times would be associated with improved performance on novel tasks. Furthermore, we predicted that incentivizing performance with monetary rewards would increase proactive control, as indexed by preparation times and improved task performance. Replicating Cole et al. (2018), we observed that longer preparation times were associated with higher accuracy on novel tasks, both between-subjects and trial-by-trial. However, proactive control appeared to be more important for practiced tasks than previously observed, with a similar pattern to novel tasks observed between subjects. In examining reward incentive effects, we observed that larger rewards were associated with increased proactive control in terms of slower preparation times; however, this effect did not translate to general accuracy benefits on high-reward trials. Exploratory analyses suggest that this surprising result may be due to a more complex relationship between preparation time and accuracy than previously recognized, as subject- and trial-based variation in preparation time interacted in predicting accuracy outcomes. Together, our results highlight the key role of proactive control in the successful learning of new tasks, while also demonstrating that proactive control-related performance benefits may depend on crucial interactions between individual differences and trial-level demands.

The brain is a complex dynamical system whose activity reflects changes in internal states, such as attention. While prior work has shown that large-scale brain activity reflects attention, the mechanism governing this association in a time-varying and task-dependent manner remains unknown. Here, we tested a hypothesis that the geometry of neural dynamics on the attractor landscape, or the movement along the "hills and valleys", reflects changes in attentional states over time and variations across controlled and naturalistic contexts. We fit a parametric dynamical systems model to fMRI data collected during rest, task performance, and naturalistic movie-watching. The model decomposes neural dynamics into components that are intrinsic versus extrinsically driven by stimuli. Model parameters were biologically meaningful, reflecting both cognitive states and individual differences. Model simulations revealed a set of attractors that mirrored functional brain networks, spanning the canonical gradient from sensorimotor to default mode network regions. The speed and direction of neural trajectories toward these attractors systematically varied across attentional states in a context-dependent manner. When participants were paying attention to effortful tasks, neural dynamics converged directly toward a task-relevant attractor, suggesting that it occupied a steeper region of the attractor landscape. In contrast, when participants were engaged in sitcom episodes, neural dynamics were in a flattened region of the landscape, directed away from the attractors. These findings demonstrate that while the positions of the attractors are largely determined by the cortical organization, the geometry of neural dynamics on the attractor landscape changes systematically across attentional states and situational contexts.