No abstract

A large body of research has shown that learning about relationships between neutral stimuli and events of significance-rewards or punishments-influences the extent to which people attend to those stimuli in future. However, different accounts of this influence differ in terms of the critical variable that is proposed to determine learned changes in attention. We describe two experiments using eye-tracking with a rewarded visual search procedure to investigate whether attentional capture is influenced by the predictiveness of stimuli (i.e., the extent to which they provide information about upcoming events) or by their absolute associative value (that is, the expected incentive value of the outcome that a stimulus predicts). Results demonstrated a clear influence of associative value on the likelihood that stimuli will capture eye-movements, but the evidence for a distinct influence of predictiveness was less compelling. The results of these experiments can be reconciled within a simple account under which attentional prioritization is a monotonic function of the expected, subjective value of the reward that is signalled by a stimulus.

In uncertain or unstable environments, sometimes the best decision is to change your mind. To shed light on this flexibility, we evaluated how the underlying decision policy adapts when the most rewarding action changes. Human participants performed a dynamic two-armed bandit task that manipulated the certainty in relative reward (conflict) and the reliability of action-outcomes (volatility). Continuous estimates of conflict and volatility contributed to shifts in exploratory states by changing both the rate of evidence accumulation (drift rate) and the amount of evidence needed to make a decision (boundary height), respectively. At the trialwise level, following a switch in the optimal choice, the drift rate plummets and the boundary height weakly spikes, leading to a slow exploratory state. We find that the drift rate drives most of this response, with an unreliable contribution of boundary height across experiments. Surprisingly, we find no evidence that pupillary responses associated with decision policy changes. We conclude that humans show a stereotypical shift in their decision policies in response to environmental changes.

Cognitive control, the ability to engage in goal-related behavior, is linked to frontal, parietal, and cingulate brain regions. However, the underlying function(s) of these regions is still in question, with ongoing discussions about their specificity and/or multifunctionality. These brain regions are also among the most variable across individuals, which may confound multi-functionality with inter-individual heterogeneity. Precision fMRI-extended data acquisition from single individuals-allows for reliable individualized mapping of brain organization. We review examples of recent studies that use precision fMRI to surmount inter-individual variability in functional neuroanatomy. These studies provide evidence of interleaved specialized and multifunctional regions in the frontal cortex. We discuss the potential for these techniques to address outstanding controversies on the neural underpinnings of cognitive control. Highlights

Obesity is associated with poorer executive functioning and reward sensitivity. Yet, we know very little about whether weight loss through diet and/or increased exercise engagement improves cognitive function. This study evaluated whether weight loss following a dietary and exercise intervention was associated with improved cognitive performance. We enrolled 125 middle-aged adults with overweight and obesity (98 female) into a 12-month behavioral weight loss intervention. Participants were assigned to one of three groups: energy-restricted diet alone, an energy-restricted diet plus 150 min of moderate intensity exercise per week or an energy restricted diet plus 250 min of exercise per week. All participants completed tests measuring executive functioning and/or reward sensitivity, including the Iowa Gambling Task (IGT). Following the intervention, weight significantly decreased in all groups. A MANCOVA controlling for age, sex and race revealed a significant multivariate effect of group on cognitive changes. Post-hoc ANCOVAs revealed a Group x Time interaction only on IGT reward sensitivity, such that the high exercise group improved their performance relative to the other two intervention groups. Post-hoc ANCOVAs also revealed a main effect of Time, independent of intervention group, on IGT net payoff score. Changes in weight were not associated with other changes in cognitive performance. Engaging in a high amount of exercise improved reward sensitivity above and beyond weight loss alone. This suggests that there is additional benefit to adding exercise into behavioral weight loss regimens on executive functioning, even without additional benefit to weight loss.

Completing complex tasks requires that we flexibly integrate information across brain areas. While studies have shown how functional networks are altered during different tasks, this work has generally focused on a cross-subject approach, emphasizing features that are common across people. Here we used extended sampling “precision” fMRI data to test the extent to which task states generalize across people or are individually specific. We trained classifiers to decode state using functional network data in single-person datasets across 5 diverse task states. Classifiers were then tested on either independent data from the same person or new individuals. Individualized classifiers were able to generalize to new participants. However, classification performance was significantly higher within a person, a pattern consistent across model types, people, tasks, feature subsets, and even for decoding very similar task conditions. Notably, these findings also replicated in a new independent dataset. These results suggest that individual-focused approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individual-focused approaches have the potential to deepen our understanding of brain interactions during complex cognition.

Completing complex tasks requires flexible integration of functions across brain regions. While studies have shown that functional networks are altered across tasks, recent work highlights that brain networks exhibit substantial individual differences. Here we asked whether individual differences are important for predicting brain network interactions across cognitive states. We trained classifiers to decode state using data from single person "precision" fMRI datasets across 5 diverse cognitive states. Classifiers were then tested on either independent sessions from the same person or new individuals. Classifiers were able to decode task states in both the same and new participants above chance. However, classification performance was significantly higher within a person, a pattern consistent across model types, datasets, tasks, and feature subsets. This suggests that individualized approaches can uncover robust features of brain states, including features obscured in cross-subject analyses. Individualized approaches have potential to deepen our understanding of brain interactions during complex cognition.

Health behaviors arise from the dynamics of highly interconnected networks in the brain and variability in these networks drives individual differences in behavior. In this review, we show how many factors that predict the physical health of the body also correlate with variability of the myelinated fascicles, called white matter, that connect brain regions together. The general pattern present in the literature is that as predictors of physical health decline, there is often a coincident reduction in the integrity of major white matter pathways. We also highlight a plausible mechanism, inflammatory pathways, whereby health-related activation of the immune system can impact the myelin sheath, a protective tissue that facilitates long range communication in the brain. The growing body of evidence supports the hypothesis that degrading health in the periphery may disrupt the communication efficiency of the macroscopic neural circuits that mediate complex behaviors, which can in turn contribute to poorer physical health.