Cognition

The dynamics of arousal: unifying fluctuations across brain, body, and behavior

Recent studies in awake, behaving animals demonstrate the widespread presence of arousal-related fluctuations across diverse neural, behavioral, and physiological measurements. These observations have led us to view such fluctuations as manifestations of a unified, intrinsic dynamical process that regulates organism-wide physiology. We have recently hypothesized that the spatial structure of brain-wide activity represents one such regulated variable – thus amounting to a generative mechanism underlying the widely studied phenomenon of “resting-state functional connectivity” (Raut et al., 2021 Sci Adv). In this talk, I will elaborate upon this framework by introducing a data-driven approach that enables the reconstruction of observables from a low-rank representation of their shared dynamics. I will demonstrate the success of this procedure across diverse neural and behavioral measurements recorded from awake mice, thus parsimoniously linking high-dimensional observations to a latent dynamical process evolving on a universal, low-dimensional manifold. Taken together, the proposed framework and findings elucidate an arousal-related dynamical mechanism that is both richer and substantially further-reaching than presently recognized. This carries implications for the analysis and interpretation of brain dynamics observed across modalities, tasks, and species.

City Population, Majority Group Size, and Segregation Drive Implicit Racial Biases in U.S. Cities

Implicit biases, expressed as differential treatment towards out-group members, are pervasive in human societies. These biases are often racial or ethnic in nature and create disparities and inequities across many aspects of life. Recent research has revealed that implicit biases are, for the most part, driven by social contexts and local histories. However, it has remained unclear how and if the regular ways in which human societies self-organize in cities produce systematic variation in implicit bias strength. Here we leverage extensions of the mathematical models of urban scaling theory to predict and test between-city differences in implicit racial biases. Our model links scales of organization from city-wide infrastructure to individual psychology to quantitatively predict that cities which are (1) more populous, (2) more diverse, and (3) less segregated have lower levels of implicit biases. We find broad empirical support for each of these predictions in U.S. cities for data spanning a decade of racial implicit association tests from millions of individuals. We conclude that the organization of cities strongly drives the strength of implicit racial biases and provides potential systematic intervention targets for the development and planning of more equitable societies.

Studying large-scale collaborations in online communities
Humans have developed technological repertoires that have enabled us to survive everywhere from the tundra to the Earth’s orbit. However, it can be difficult to trace how these technologies came to be—folk histories of technological achievement often highlight a few brilliant individuals, while losing sight of the rest of the community’s contributions. In this talk, I will present ongoing work analyzing player behavior in One Hour One Life, a multiplayer online game where players can build technologically complex communities over many generations (N = 22,011 players, 2,700 communities, 428,255 lives lived, 127,768,267 interactions). This dataset provides a unique opportunity to test how community-wide expertise and division of labor shape technological development: Players can form communities that endure for many generations, and they can combine thousands of unique materials to build vast technological repertoires. Overall, we find that technological development is not driven merely by a few highly experienced players; instead, the division of labor and interactions between the community as a whole predict the pace of technological development. I will also discuss the opportunities and potential pitfalls of using “found” online datasets to study large-scale social phenomena.

First speaker: Henry Jones [link], a Ph.D. student in Awh/Vogel lab
EEG Decoding Reveals Distinct Processes for Directing Spatial Attention and Encoding into Working Memory
We know that attention operates at both early and late stages of processing, affecting both low-level sensory processing and the selection/prioritization of perceived objects. In EEG, these aspects of attention have been studied via covert spatial attention, captured by spatially-selective alpha, and the number of items currently held in working memory (WM load), captured by raw voltage patterns across the scalp. Recent work has found evidence that spatially-selective alpha and load voltage signals diverge in some circumstances, suggesting that they reflect 2 separate forms of voluntary attentional control. However, the previous literature has made use of relatively coarse measures. To address this, I’ll be presenting a new task that lets us independently manipulate the relevant spatial area to attend to and the relevant number of objects to encode into WM. In combination with more sensitive measures, we find a double dissociation of results, such that alpha signals are impacted by changes to the attended spatial area, but not the number of objects, and load signals are impacted by the number of relevant items, but not changes to the attended spatial area. I’ll propose that this provides evidence for at least 2 forms of voluntary attentional control, and discuss how this could reframe other attentional effects.

Second speaker: Michael Cohen [link], a postdoctoral researcher (senior research analyst) in Decety lab
Individual differences in and Neural mechanisms of Continued Influence Effects from false political accusations.
We examined how misinformation influences subsequent judgments and decisions, following prior work on Continued Influence Effects (CIEs). We developed a set of novel political candidate stimuli with accusations and refutations based on true stories. We observed robust within-participant CIEs: candidates targeted by corrected accusations evoke lower feeling thermometer ratings than candidates not targeted by accusations. In two experiments examining individual differences in CIEs, we found that self-reported reliance on intuition/feelings is associated with larger CIEs, while digital literacy knowledge is associated with lessened CIEs. Affective polarization may predict larger CIEs, but this finding was not consistently observed. CIEs are not predicted by Republican political orientation or Actively Open-Minded Thinking, two factors that are strong predictors of headline accuracy discernment (a different way of assessing misinformation vulnerability).  An fMRI experiment also showed that lateral orbitofrontal cortex (lOFC) and L TPJ are associated with dislike of candidates targeted with accusations regardless of correction, suggesting an important role for socioemotional processing in CIEs. Finally, between individuals, brain activity indicative of greater mentalizing/empathy towards candidates, and activity in executive control regions suggesting more analytic thinking while processing refutations, are associated with lessened CIEs.

Motivated learning and memory processes across development

The ability to learn from and remember salient information is essential for an individual to survive and thrive throughout life. Considerable research has focused on emotional reactivity, regulation, and their neural mechanisms during the transitional stage of adolescence. However, fewer studies have examined how emotion and motivation shape how we learn and what we remember from childhood to adulthood. My work aims to characterize how aversive and appetitive motivational inputs influence learning and lasting memories across adolescence. We find that the context in which learning takes place modulates age-related differences in learning from positive and negative outcomes. We also find memory enhancements by both aversive and appetitive motivation across all ages as well as adolescent-specific differences in reward-motivated memory. Neuroimaging results suggest potential developmental differences in the contributions of subcortical and prefrontal cortical brain mechanisms supporting reward-motivated memory. Ultimately, this research has the potential to uncover cognitive and neural mechanisms through which motivation shapes the memories that drive future behavior from childhood to adulthood.

Physiological and psychosocial correlates of “good sleep”: Implications for cognition, health, and aging

What makes your sleep “good”? Few wake intrusions? Falling asleep once your head hits the pillow? Waking up refreshed and ready for your day? All the above? Science is still grappling with the answers to this question, yet we do know that a period of sleep helps us think, learn, and remember better. Additionally, specific neural changes during sleep support human cognition. To date, my research program has examined how these neural features and specific changes in our body 1) help us define “good” sleep and 2) support cognition. In this talk, I will review this body of work and identify future directions aligned with this research trajectory. Additionally, data suggests that 35% of people do not get the recommended amount of sleep at night. This widespread sleeplessness comes with significant costs to our overall health. Yet, the burden of sleep loss does not fall on everyone equally. I will discuss disparities in sleep health and access, discuss historical links, and on-going and future projects that address these topics.

The Development of Decision-Making Across Diverse Cultural Contexts

The human behavioral repertoire is uniquely diverse, with an unmatched flexibility that has allowed our species to flourish in every ecology on the planet. Despite its importance, the roots of this behavioral diversity — and how it manifests across development and contexts — remain largely unexplored. I argue that a full account of human behavior requires a cross-cultural, developmental approach that systematically examines how environmental variability shapes behavioral processes. In this talk, I use the development of decision-making across diverse contexts as a window into the relationship between the socioecological environment and behavior. First, I present the results of a cross-cultural investigation of risk and time preferences among children in India, Argentina, the United States, and the Ecuadorian Amazon, suggesting that market integration and related socioecological shifts lead to the development of more risk-seeking and future-oriented preferences. Second, I present the early results of a five-culture investigation into the ontogeny of social preferences — namely, trustworthiness, forgiveness, and fairness. Taken together, these studies help elucidate the developmental origins of behavioral diversity across diverse contexts, and underscore the utility of interdisciplinary research for explaining human behavior.

Cognitive state fluctuations impact learning in different contexts

We are constantly learning from the world around us. How do changes in our cognitive and attentional states impact this process? I will describe two projects examining relationships between internal state fluctuations and an automatic, fundamental process of learning—statistical learning, and a noisy, dynamic form of learning—adaptive learning. In the first project, we examined the consequences of sustained attention fluctuations for statistical learning. Participants completed a continuous performance task with shape stimuli online. Unbeknownst to participants, we manipulated what they saw in real time by inserting visual regularities (a sequence of three regular shapes) into the task trial stream when their response times suggested that they were in especially high or low attentional states. Demonstrating that attentional state impacts statistical learning, we observed greater evidence for learning of the regular sequence encountered in the high vs. the low attentional state. In project two, we reanalyzed an openly available fMRI dataset collected as participants performed an adaptive learning task in which they learned to make accurate predictions about the locations of a fallen object in an noisy and dynamically changing environment. Individual differences in a brain network signature of sustained attention predicted individual learning style, with individuals with network signatures of stronger attention showing a learning style more like that of a normative model. In addition, trial-to-trial fluctuations in a distinct network signature of working memory predicted learning performance, such that trials on which participants showed a network signature of stronger working memory were followed by closer alignment between human and model predictions on the next trial. Together, these studies reveal consequences of sustained attention and working memory fluctuations for learning in different contexts.