Author: zz112

Title: Exploring Dimensions of Partisan Bias in Large Real-life Texts
Nak Won Rim, Doctoral Student, Department of Psychology, University of Chicago
People evaluate topics and objects differently based on their political beliefs. Several political and social psychology theories have suggested different dimensions where the evaluations based on political belief differ. For example, moral foundations theory proposes that liberals and conservatives have different moral frameworks – something a conservative evaluates as immoral might be moral to liberals, and vice versa. This work tests these theories in a large-scale, out-of-lab context by utilizing language models (word2vec) trained on large corpora (Reddit comments and news articles). Specifically, we use a combination of the dimension-based approach and a classifier to test (1) if these dimensions can distinguish the models trained on liberal corpora and models trained on conservative corpora above chance, (2) whether these dimensions achieve significantly better accuracies than random dimensions. We show that partisan (democrat vs republican) and morality (moral vs immoral) dimensions have significant classification accuracy in both tests and both corpora. Overall, our results show that morality is a dimension where people with different political beliefs differ to the point that we can detect the division in real-life texts people produce outside labs.

Talk 1:
Title: Loneliness intensifies acute feelings of social rejection
Anita Restrepo, Doctoral Student, Department of Psychology, University of Chicago
Loneliness, or perceived social isolation, is a psychological state where individuals feel unsafe since isolation from their social group leaves them vulnerable to external threats. As such, lonely individuals show hypervigilance to threat cues, particularly information about social threat. Because psychological states of perceived unsafety bias individuals to interpret ambiguous information as more threatening, we explored how the ambiguity of social cues moderated effects of loneliness on feelings of rejection after a social exclusion paradigm. An online sample of 142 adults completed a progressive, 5-round Cyberball ostracism paradigm where they were randomly assigned to either be equally included, excluded, or over-included. Importantly, ambiguity was highest in earlier rounds where exclusion/inclusion cues were unclear and systematically decreased from one round to the next. Participants completed the UCLA Loneliness Scale prior to beginning the task and responded to a brief questionnaire indexing feelings of rejection after each Cyberball round. Hierarchical linear models showed that higher loneliness predicted increased feelings of rejection across conditions (equal inclusion, exclusion, and over-inclusion). Notably, this positive relationship was strongest during earlier rounds when social cues were most ambiguous. These findings aid in integrating our understanding of loneliness within a larger stress response framework and how these systems modulate social perception to enable organisms to adequately adapt to changing circumstances.

Talk 2:
Title: A Metacognitive Framework for Modeling Authoritarian-led Reform via Dynamical Systems
Eugene Yu Ji, Doctoral Student, Department of Psychology, University of Chicago
Recent studies in comparative politics propose that one most frequent economic or political reform path in many authoritarian states in East and Southeast Asia is a top-down, state-led process (e.g. Riedl et al. 2020; Slater and Wong 2022). This influential theory of state-led reform strongly relies on a behavioral assumption: the authoritarian state leadership concedes to a major political reform (such as democratization) when a tradeoff, “bittersweet” signal is cognized, which suggests both high pressures encouraging the reform and a prospect of achieving stability or flourishment after the reform. But in this literature, cognitive and behavioral mechanisms from cognition to decision making are far from being sufficiently explored. Using the dynamical-systems approach, my work proposes a metacognitive model of decision-making of authoritarian-led reform (which is formally inspired by the FitzHugh-Nagumo model in computational neuroscience). The model includes two basic variables of cognitive uncertainty: uncertainty in risk assessment (URA) and uncertainty in expected utility (UEU), and the model investigates the decision-making dynamics generated from four types of metacognition together: metacognitive self-feedback of URA, metacognitive self-feedback of UEU, metacognitive feedback from URA to UEU, and metacognitive feedback from UEU to URA. Current modeling results show that the metacognitively-informed decision-making process can lead to different “prospect trajectories”, which further lead to different equilibria of reform-related decision-making outcomes. Current empirical testing applies this model to examine historical and contemporary cases of major, minor, or no reform in six countries or regions in East and Southeast Asia, and the results demonstrate the effectiveness and potential of this new framework.

Title: A Computational Theory of Flow
David Melnikoff, PhD. Postdoctoral Fellow, Northeastern University
Flow is a coveted psychological state characterized by immersion and engagement in an activity. The benefits of flow for productivity and health are well- documented, but a formal, mechanistic understanding of the flow-generating process remains elusive. I will be discussing recent work that addresses this problem by developing and empirically testing a theory of flow’s computational substrates—the informational theory of flow. The theory draws on the concept of mutual information, a fundamental quantity in information theory that quantifies the strength of association between two variables. The claim is that the mutual information between desired end states and means of attaining them—I(M;E)—gives rise to flow. I will support this claim across six experiments (five preregistered), which show, across multiple activities, that increasing I(M;E) increases flow and has important downstream benefits, including enhanced attention, enjoyment, and skilled performance.

Title: The optics of moral behavior

Moral decisions are complex. They require individuals to make tradeoffs between different moral principles (e.g., equity, honesty, prosociality), and to also weigh their selfish interests. To make things even more complicated, people also care about their public appearance. They wish to appear generous, fair, and honest, and want others to judge their actions as good and moral. In this talk, I will present findings from three lines of research, looking at (1) how concern with moral judgment affects moral behavior; and (2) how moral decisions are actually judged by others. In the first line of research (Shaw, Choshen-Hillel, & Caruso, 2018), we demonstrate that under some circumstances (which I will delineate in the talk), decision makers may actually bias against their friends, in order to appear unbiased. In a second line of research, I show that people may sometimes lie to appear honest. In the final line of research (Bigman, Choshen-Hillel, & Gray, in preparation), I argue that observers are more likely to judge morally questionable decisions positively, when the agent has made a personal sacrifice. The studies use experimental scenarios as well as incentivized lab studies. I discuss the findings in relation to psychological theories on reputation and ethical behavior, and suggest practical implications for individuals and organizations.

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.