Publications
publications by categories in reversed chronological order.
Up-to-date publications are also available on Google Scholar.
Peer-reviewed Articles
2024
- A shared structure for emotion experiences from narratives, videos, and everyday lifeYanting Han and Ralph AdolphsIscience, Jun 2024
Our knowledge of the diversity and psychological organization of emotion experiences is based primarily on studies that used a single type of stimulus with an often limited set of rating scales and analyses. Here we take a comprehensive data-driven approach. We surveyed 1,000+ participants on a diverse set of ratings of emotion experiences to a validated set of ca. 150 text narratives, a validated set of ca. 1,000 videos, and over 10,000 personal experiences sampled longitudinally in everyday life, permitting a unique comparison. All three types of emotion experiences were characterized by similar dimensional spaces that included valence and arousal, as well as dimensions related to generalizability. Emotion experiences were distributed along continuous gradients, with no clear clusters even for the so-called basic emotions. Individual differences in personality traits were associated with differences in everyday emotion experiences but not with emotions evoked by narratives or videos.
2023
- COVID-Dynamic: A large-scale longitudinal study of socioemotional and behavioral change across the pandemicTessa Rusch* , Yanting Han* , Dehua Liang* , Amber R Hopkins , Caroline V Lawrence, and 4 more authorsScientific data, Feb 2023
The COVID-19 pandemic has caused enormous societal upheaval globally. In the US, beyond the devastating toll on life and health, it triggered an economic shock unseen since the great depression and laid bare preexisting societal inequities. The full impacts of these personal, social, economic, and public-health challenges will not be known for years. To minimize societal costs and ensure future preparedness, it is critical to record the psychological and social experiences of individuals during such periods of high societal volatility. Here, we introduce, describe, and assess the COVID-Dynamic dataset, a within-participant longitudinal study conducted from April 2020 through January 2021, that captures the COVID-19 pandemic experiences of >1000 US residents. Each of 16 timepoints combines standard psychological assessments with novel surveys of emotion, social/political/moral attitudes, COVID-19-related behaviors, tasks assessing implicit attitudes and social decision-making, and external data to contextualize participants’ responses. This dataset is a resource for researchers interested in COVID-19-specific questions and basic psychological phenomena, as well as clinicians and policy-makers looking to mitigate the effects of future calamities.
2020
- Estimating the heritability of psychological measures in the Human Connectome Project datasetYanting Han and Ralph AdolphsPLoS One, Jul 2020
The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.
2018
- Resting-state functional brain connectivity best predicts the personality dimension of openness to experienceJulien Dubois , Paola Galdi , Yanting Han , Lynn K Paul , and Ralph AdolphsPersonality neuroscience, Jul 2018
Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 884 young healthy adults in the Human Connectome Project database. We attempted to predict personality traits from the “Big Five,” as assessed with the Neuroticism/Extraversion/Openness Five-Factor Inventory test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness, and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two intersubject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 hr of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; three denoising strategies; two alignment schemes; three models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=.24, R2=.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=.26, R2=.044). Other factors (Extraversion, Neuroticism, Agreeableness, and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the Neuroticism/Extraversion/Openness Five-Factor Inventory factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=.27, R2=.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.
2013
- Ultra-compliant liquid metal electrodes with in-plane self-healing capability for dielectric elastomer actuatorsYang Liu , Meng Gao , Shengfu Mei , Yanting Han , and Jing LiuApplied Physics Letters, Jul 2013
The method of directly printing liquid metal films as highly conductive and super compliant electrodes for dielectric elastomer actuator (DEA) was proposed and experimentally demonstrated with working mechanisms interpreted. Such soft electrodes enable DE film to approach its maximum strain and stress at relatively low voltages. Further, its unique capability of achieving two-dimensional in-plane self-healing by merely actuating the DEA was disclosed, which would allow actuators more tolerant to fault and resilient to abusive environments. This high performance actuator has important value in a wide spectrum of situations ranging from artificial muscle, flexible electronics to smart clothing etc.
Manuscripts under review and in prep
2025
- Self-reported subjective experience of emotions in alexithymiaYanting Han , Stavya Arora , Brenna Outten , and Ralph Adolphs2025In Prep
- Investigating the structure of emotion: tools, pitfalls and recommendations.Chujun Lin , Yanting Han , Umit Keles , Yue Xu , Junsong Lu, and 2 more authors2025Under review
2022
- Trait resilience protects against depression caused by loneliness during the COVID pandemicYanting Han and Ralph Adolphs2022