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We run a cross-sectional survey using a convenience sample (Prolific.co) to understand predictors of pluralistic ignorance surrounding climate change attitudes and policy support. We will regress the perceived support among Americans for different policies on various individual difference measurements. We will test tentative psychological mechanisms that are presumed to drive misperception of public opinions, which may involve (lack of) information availability, motivated reasoning, and non-motivated biases and heuristics. We will further examine participants’ (mis)perception of Americans’ opinions about gun control and immigration policies in exploratory analyses.

Additional file 1: Fig. S1. Length distribution of HiFi reads for two multiplexed samples, ALT1 and ALT2 (shown in color). Each vertical dashed line represents the average read length for each sample. Fig. S2. Summary of assembly-based variant calling outputs using SVIM-asm. Variants are categorized into five types: DEL, deletion; INS, insertion; INV, inversion; DUP_INT, interspersed duplication; and DUP_TAN, tandem duplication. Each graph on the left shows variants 10 kb. The numbers of >10-kb variants were too small and are therefore represented as absolute numbers (0 or 1). Fig. S4. Overlap ratio between all acquired variants detected using CLR and HiFi for each strain. A, Proportion of CLR variants covered by HiFi. B, Proportion of HiFi variants covered by CLR. Color represents partial- or full-matched variant sets. Shapes (circle or triangle) represent each strain. Fig. S5. Performance of four read-based SV callers using HiFi reads in various sequencing depths. Violin plots represent the number of total detected variants. Violin plots represent the detection rate of true-positive variants from the four SV callers, SVIM, Sniffles, PBSV, and MAMnet. We used the full-matched common founder variants in ALT1 and ALT2 described above (total 1804) as the true-positive variant set to estimate the true-positive variant detection rate. Red represents the detection rate based on at least partial match to the true-positive variant set, and blue does only for perfect match. Subsampling was repeated 5 times for each sequencing depth. Each horizontal solid line of the violin plot means a median of each 5 repeats. Fig. S6. Performance of HiFi read-based reliable variant calling at varying sequencing depths. Violin plots representing the detection rate of true-positive variants. The full-matched common founder variants in ALT1 and ALT2 described above (1804 in total) were used as the true-positive variant set. Red represents the detection rate based on at least partial matching to the true-positive variant set and blue represents the detection rate based on perfect matching. Subsampling was repeated five times for each sequencing depth. Reliable variants were filtered from those obtained using read-based variant calling according to their quality metric (Q ≥ 5; Q5). Fig. S7. Performance of the three SV callers, Sniffles, PBSV, and MAMnet, in detecting reliable true-positive variants in various sequencing depths. Violin plots represent the number of total detected variants. Red represents the number of variants at least partially matched to the common founder variants (total 1804), and blue does only for perfect match. Gray represents the remaining variants out of the total. Reliable variants were filtered by their quality metric (Q ≥ 5; Q5) from read-based variant calling, except Sniffles and PBSV, in which the quality metric is over 25 or missing, respectively. Subsampling was repeated 5 times for each sequencing depth. Table S1. Statistics for polymerase reads and subreads. Table S2. Statistics for HiFi reads. Table S3. Statistics for assembled contigs built on CLR or depth-matched HiFi reads. Table S4. Number of founder and acquired variants detected in each assembly for each strain.

The present paper reports an experiment (N = 348) with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take climate actions. Here we focus on the longitudinal results. Our experimental materials were designed to harness the power of two cognitive constraints — coherence and causal invariance, which map onto two narrative proclivities that anthropologists have identified as universal — to promote climate action across the political spectrum. Towards that goal, the essential role of these constraints in belief formation predicts that climate-change information would be more persuasive when it is embedded in a personal climate-action narrative, the evocation of which can benefit from exposure to parsimonious scientific explanations of indisputable everyday observations, juxtaposed with reasoners’ own, typically less coherent explanations, occurring in a context that engages their moral stance. Our brief one-time intervention, conducted in U.S. states with the highest level of climate skepticism, showed that across the political spectrum, our materials raised willingness to take climate actions in the immediate assessment. It also raised the likelihood of reports two years later of having taken or would have taken those actions had the opportunity existed, suggesting long-lasting effects. Our approach adopts the framework that conceptions of reality are representations, and adaptive solutions in that infinite space of representations require cognitive constraints to narrow the search.

There is growing interest in emotions in textual data. Based on psychological theories, research has been conducted on assigning emotions as labels to text datasets or developing models to detect emotions present in text. However, little research has been done on the appropriateness of using these theories. In this study, we reviewed three commonly used basic emotion theories: Ekman’s Basic Emotions, Plutchik’s Wheel of Emotions, and GoEmotions. By leveraging a research finding that evaluated the emotional values of words, we were able to fine-tune a language model emotionally. Using it, we analyzed the emotional relationship between the names of basic emotions and evaluated the adequacy of the emotional structure each theory presents. Clear patterns of similarity emerged based on the emotional meaning of the words. Ekman’s and GoEmotions were almost in line with our results, while Plutchik’s had some differences. We discussed these matches and mismatches.

In an online study, we will ask participants to estimate the percent of Americans who agree/agreed with a number of statements that appeared in public opinion polls in the past and recent years. We test whether their estimates are influenced by the order of estimations and explicit mentioning of a year as a reference point.

Using Prolific.co participants who identify as liberal or conservative Americans, We investigate whether stereotype-consistent information about ideological in- and out-group members is remembered differently from stereotype-inconsistent information.

We investigate whether stereotype-consistent information about in- and out-group members is remembered differently from stereotype-inconsistent information.

We designed an online intervention based on fundamental principles of cognitive science to promote laypeople's willingness to take various pro-biodiversity actions and their appreciation of parsimony in science.

Data and experiment materials from a cross-cultural study on the judgment of collective family responsibility.

We investigate whether stereotype-consistent information about in- and out-group members is remembered differently from stereotype-inconsistent information.

Measuring the emotion in words is valuable in that it analyzes emotions through language. However, it is difficult to find such measurements in low-resource languages. In this paper, we proposed a method to expand the affective lexicon by utilizing the context of words. The proposed model predicted the Valence and Arousal values of words using their dictionary definitions. In Study 1, we reviewed previous studies about the Korean affective lexicon and integrated data from these studies. The model was trained to minimize the MSE error between the Valence and Arousal values of the words and their predictions. We then checked the distribution of Valence and Arousal values of Korean vocabulary by applying our model to the Korean dictionary. In Study 2, a new affective lexicon was built to empirically validate our model. We found a negatively biased error pattern on model predictions and discussed why it happened.

When a group member commits wrongdoing, people sometimes assign responsibility and blame not only to the wrongdoer but also to other members of the same group. We examined such assignment of collective responsibility in the context of exploitation of one family by another. Participants were recruited from the United States and South Korea, which are known to vary in cultural norms and endorsement of collectivistic values. Participants in both countries rated the degree to which an agent (grandson) should be held responsible for his grandfather’s exploitation of a victimized family, while varying the closeness of familial connection. Participants’ responsibility judgments showed sensitivity to whether the grandson received financial benefit from the wrongdoer and to the perceived closeness between the grandson and the wrongdoer. Korean participants imposed greater responsibility on the agent than did American participants. Implications for understanding the influence of social norms on moral judgments are discussed.

Competence and morality are two of the most important dimensions in social evaluation. Recent studies have suggested the primacy of morality, showing that information about immorality of an ordinary target person decreases evaluation of their competence. We examined the effect of moral taint on multiple non-moral judgments: ratings of the competence, accomplishment, and contribution of fictitious professionals who were described as highly successful in various fields. Moral taint significantly decreased participants’ non-moral social evaluations of professionals regardless of their field. Mediation analyses showed that the negative impact of immoral character on competence judgments is more strongly mediated by the decrease in participants’ psychological involvement with the target, rather than a decrease in perceived social intelligence of the target. These findings suggest that motivation to distance oneself from immoral others plays a critical role in the revision of social evaluations.

We created practical moral dilemmas for which participants imagined witnessing a transgression by a target person. The identity of the transgressor was manipulated to be either a stranger or the participant's brother. In Experiment 1, whether the target person committed a violation was left ambiguous. Participants made factual (how strongly they believe the target person actually committed a transgression) and unethicality judgments regarding the incident, and rated their willingness to report the transgressor to the police. Given ambiguity (Experiment 1), participants interpreted the facts in favor of their brother, but not in favor of a stranger. This interpretation led to moral judgments and willingness to report that favored family over strangers, while creating overall coherence in reasoning. In Experiment 2, we eliminated the ambiguity of the factual situation so that the possibility of achieving coherence between unethicality of an act and leniency toward a family member was blocked. Nonetheless, participants were less willing to report their brother to the police. Experiment 3 replicated the findings of the first two experiments within an integrated study design. Results from path analyses indicated that the factual judgment depended on factual understanding of an event, but willingness to report depended on identity of the target (i.e., brother vs. stranger), even at the cost of reduced coherence in reasoning. Moral decisions are thus strongly influenced by agent-relative obligations, such as duty to protect a family member.

Convolutional neural networks (CNN) have been extensively used for inverse problems. However, their prediction error for unseen test data is difficult to estimate a priori since the neural networks are trained using only selected data and their architecture are largely considered a blackbox. This poses a fundamental challenge to neural networks for unsupervised learning or improvement beyond the label. In this paper, we show that the recent unsupervised learning methods such as Noise2Noise, Stein's unbiased risk estimator (SURE)-based denoiser, and Noise2Void are closely related to each other in their formulation of an unbiased estimator of the prediction error, but each of them are associated with its own limitations. Based on these observations, we provide a novel boosting estimator for the prediction error. In particular, by employing combinatorial convolutional frame representation of encoder-decoder CNN and synergistically combining it with the batch normalization, we provide a close form formulation for the unbiased estimator of the prediction error that can be minimized for neural network training beyond the label. Experimental results show that the resulting algorithm, what we call Noise2Boosting, provides consistent improvement in various inverse problems under both supervised and unsupervised learning setting.

We prove a structure theorem for the Gromov-Witten invariants of compact Kahler surfaces with geometric genus $p_g>0$. Under the technical assumption that there is a canonical divisor that is a disjoint union of smooth components, the theorem shows that the GW invariants are universal functions determined by the genus of this canonical divisor components and the holomorphic Euler characteristic of the surface. We compute special cases of these universal functions., 27 pages

On a compact K\"{a}hler manifold $X$ with a holomorphic 2-form $\a$, there is an almost complex structure associated with $\a$. We show how this implies vanishing theorems for the Gromov-Witten invariants of $X$. This extends the approach, used in \cite{lp} for K\"{a}hler surfaces, to higher dimensions.

This article describes the use of symplectic cut-and-paste methods to compute Gromov-Witten invariants. Our focus is on recent advances extending these methods to Kahler surfaces with geometric genus p_g>0, for which the usual GW invariants vanish for most homology classes. This involves extending the Splitting Formula and the Symplectic Sum Formula to the family GW invariants introduced by the first author. We present applications to the invariants of elliptic surfaces and to the Yau-Zaslow Conjecture. In both cases the results agree with the conjectures of algebraic geometers and yield a proof, to appear in [LL1], of previously unproved cases of the Yau-Zaslow Conjecture., 26 pages, 7 figures. Survey paper to appear in the Proceedings of the Fields-McMaster Conference on the Geometry and Topology of Manifolds

The origins of the high device performance and degradation in the air are the greatest issues for commercialization of perovskite solar cells. Here this study investigates the possible origins of the mixed perovskite cells by monitoring defect states and compositional changes of the perovskite layer over the time. The results of deep-level transient spectroscopy analysis reveal that a newly identified defect formed by Br atoms exists at deep levels of the mixed perovskite film, and its defect state shifts when the film is aged in the air. The change of the defect state is originated from loss of the methylammonium molecules of the perovskite layer, which results in decreased J(SC), deterioration of the power conversion efficiency and long-term stability of perovskite solar cells. The results provide a powerful strategy to diagnose and manage the efficiency and stability of perovskite solar cells.