Individual differences in naturalistic learning link negative emotionality to the development of anxiety

Organisms learn from prediction errors (PEs) to predict the future. Laboratory studies using small financial outcomes find that humans use PEs to update expectations and link individual differences in PE-based learning to internalizing disorders. Because of the low-stakes outcomes in most tasks, it is unclear whether PE learning emerges in naturalistic, high-stakes contexts and whether individual differences in PE learning predict psychopathology risk. Using experience sampling to assess 625 college students\u27 expected exam grades, we found evidence of PE-based learning and a general tendency to discount negative PEs, an optimism bias. However, individuals with elevated negative emotionality, a personality trait linked to the development of anxiety disorders, displayed a global pessimism and learning differences that impeded accurate expectations and predicted future anxiety symptoms. A sensitivity to PEs combined with an aversion to negative PEs may result in a pessimistic and inaccurate model of the world, leading to anxiety

Negative Interpretation Bias Connects to Real-World Daily Affect: A Multistudy Approach

Negative interpretation bias, the tendency to appraise ambiguous stimuli as threatening, shapes our emotional lives. Various laboratory tasks, which differ in stimuli features and task procedures, can quantify negative interpretation bias. However, it is unknown whether these tasks globally predict individual differences in real-world negative (NA) and positive (PA) affect. Across two studies, we tested whether different lab-based negative interpretation bias tasks predict daily NA and PA, measured via mobile phone across months. To quantify negative interpretation bias, Study 1 (N = 69) used a verbal, self-referential task whereas Study 2 (N = 110) used a perceptual, emotional image task with faces and scenes. Across tasks, negative interpretation bias was linked to heightened daily NA. However, only negative interpretation bias in response to ambiguous faces was related to decreased daily PA. These results illustrate the ecological validity of negative interpretation bias tasks and highlight converging and unique relationships between distinct tasks and naturalistic emotion

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Correction to: First results on survival from a large Phase 3 clinical trial of an autologous dendritic cell vaccine in newly diagnosed glioblastoma

Following publication of the original article [1], the authors reported an error in the spelling of one of the author names. In this Correction the incorrect and correct author names are indicated and the author name has been updated in the original publication. The authors also reported an error in the Methods section of the original article. In this Correction the incorrect and correct versions of the affected sentence are indicated. The original article has not been updated with regards to the error in the Methods section.https://deepblue.lib.umich.edu/bitstream/2027.42/144529/1/12967_2018_Article_1552.pd

Depression is associated with blunted affective responses to naturalistic reward prediction errors

Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown. Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events - the reveal of midterm exam grades - to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs. Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes - that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding. Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression

The Neuroscience of Affective Dynamics

Emotions are time-varying internal states that emerge, persist, and decay to promote survival in the face of dynamic environments and shifting homeostatic needs. Research in non-human organisms has recently afforded specific insights into the neural mechanisms that support the emergence, persistence, and decay of affective states. Concurrently, a separate affective neuroscience literature has begun to dissect the neural bases of affective dynamics in humans. However, the circuit-level mechanisms identified in animals lack a clear mapping to the human neuroscience literature. As a result, critical questions pertaining to the neural bases of affective dynamics in humans remain unanswered. To address these shortcomings, the present review integrates findings from humans and non-human organisms to highlight the neural mechanisms that govern the temporal features of emotional states. Using the theory of affective chronometry as an organizing framework, we describe the specific neural mechanisms and modulatory factors that ultimately arbitrate the rise-time, intensity, and duration of emotional experiences

The affective benefits of real-world exploration during the COVID-19 pandemic

Increasing daily exploration is linked to improvements in affective well-being. However, COVID-19 elevated uncertainty when leaving the home, altering the risk-reward of balance of geospatial novelty. To this end, we simultaneously collected real-world geospatial tracking and experience sampling of emotion, prior to and during the first year of the pandemic in 630 individuals. COVID-19 reduced exploration and subjective well-being. Yet, despite the health risks of exploring during the pandemic, the days of highest affective well-being were those when individuals explored the most. However, this was not true for everyone: during the first months of the pandemic, at the height of the uncertainty surrounding the transmissibility and prognosis of a COVID-19 infection, more anxious individuals experienced no affective benefit to leaving home. Taken together, real-world exploration improved well-being regardless of the presence of real-world threat, but anxiety mitigated these benefits. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Temporal dynamics of affect in the brain: Evidence from human imaging and animal models

•Affective states are understood to emerge and persist through patterns of neural activity.•Human and animal neuroscience research on how the brain directs the unfolding of emotion over time are scarcely integrated.•We highlight the specific neural mechanisms and modulators that arbitrate emotional state rise-time, intensity, and duration. Emotions are time-varying internal states that promote survival in the face of dynamic environments and shifting homeostatic needs. Research in non-human organisms has recently afforded specific insights into the neural mechanisms that support the emergence, persistence, and decay of affective states. Concurrently, a separate affective neuroscience literature has begun to dissect the neural bases of affective dynamics in humans. However, the circuit-level mechanisms identified in animals lack a clear mapping to the human neuroscience literature. As a result, critical questions pertaining to the neural bases of affective dynamics in humans remain unanswered. To address these shortcomings, the present review integrates findings from humans and non-human organisms to highlight the neural mechanisms that govern the temporal features of emotional states. Using the theory of affective chronometry as an organizing framework, we describe the specific neural mechanisms and modulatory factors that arbitrate the rise-time, intensity, and duration of emotional states

Temporal dynamics of real-world emotion are more strongly linked to prediction error than outcome

Primarily based on laboratory studies, theories of affect propose that emotions are driven by the valence of outcomes as well as the difference between the outcome itself and the expected outcome (i.e., the prediction error [PE]). Yet no work has assessed the drivers of emotion using real-world, personally meaningful events on timescales over which human emotion unfolds. We developed an event-triggered, ecological momentary assessment procedure measuring positive and negative affect (PA and NA, respectively) in university students as they received exam grades for which they had made predictions. We split data into exploratory and confirmatory samples, and built computational models predicting the time course of PA and NA and demonstrate that a model incorporating both exam grade and grade PE accounted for the time course of PA and NA better than a model solely using exam grades. Further, grade PEs were stronger predictors of the time course of PA and NA than the grades themselves. Similarly, the effects of PEs also persisted longer for NA than PA. These data indicate that deviations from expectations are critical determinants of the temporal dynamics of real-world emotion. (PsycInfo Database Record (c) 2020 APA, all rights reserved)