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Observed individual behavior in the presence of ambiguity shows insufficient responsiveness to changes in subjective likelihoods. Despite being integral to theoretical models and relevant in many domains, evidence on the causes and determining factors of such likelihood insensitive behavior is scarce. This paper investigates the role of beliefs in the form of ambiguity perception – the extent to which a decision-maker has difficulties assigning a single probability to each possible event – as a potential determinant. Using an experiment, I elicit measures of ambiguity perception and likelihood insensitivity and exogenously vary the level of perceived ambiguity. The results provide strong support for a perception-based explanation of likelihood insensitivity. The two measures are highly correlated at the individual level, and exogenously increasing ambiguity perception increases insensitivity, suggesting a causal relationship. In contrast, ambiguity perception is unrelated to ambiguity aversion – the extent to which a decision-maker dislikes the presence of ambiguity. [ABSTRACT FROM AUTHOR]

[Display omitted] • Detailed discussion of HSQC-COSY implementations in NMR supersequences. • Comparison of HSQC-COSY with HSQC-TOCSY. • Sensitivity analyses of modules within typical NMR supersequences involving HSQC-COSY experiments. NMR supersequences, as exemplified by the NOAH (NMR by Ordered Acquisition using 1H detection) technique, are a powerful way of acquiring multiple 2D data sets in much shorter durations. This is accomplished through targeted excitation and detection of the magnetisation belonging to specific isotopologues ('magnetisation pools'). Separately, the HSQC-COSY experiment has recently seen an increase in popularity due to the high signal dispersion in the indirect dimension and the removal of ambiguity traditionally associated with HSQC-TOCSY experiments. Here, we describe how the HSQC-COSY experiment can be integrated as a 'module' within NOAH supersequences. The benefits and drawbacks of several different pulse sequence implementations are discussed, with a particular focus on how sensitivities of other modules in the same supersequence are affected. [ABSTRACT FROM AUTHOR]

• People experience greater confidence when they make good as opposed to inaccurate perceptual decisions. • This is known as perceptual metacognition. • Current gold standard approaches to measuring perceptual metacognition are subject to known interpretive ambiguities. • The authors describe a better measure of metacognitive sensitivity, and provide scripts to implement this new approach. Humans experience feelings of confidence in their decisions. In perception, these feelings are typically accurate – we tend to feel more confident about correct decisions. The degree of insight people have into the accuracy of their decisions is known as metacognitive sensitivity. Currently popular methods of estimating metacognitive sensitivity are subject to interpretive ambiguities because they assume people have normally shaped distributions of different experiences when they are repeatedly exposed to a single input. If this normality assumption is violated, calculations can erroneously underestimate metacognitive sensitivity. Here, we describe a means of estimating metacognitive sensitivity that is more robust to violations of the normality assumption. This improved method can easily be added to standard behavioral experiments, and the authors provide Matlab code to help researchers implement these analyses and experimental procedures. [ABSTRACT FROM AUTHOR]

• The findings highlight the semantic relation and grammatical factor in the brain's representation of lexical ambiguities. • Compared to unambiguous words, homonyms and polysemous activate the inferior frontal gyri and posterior temporal region. • Compared to polysemous words, homonyms elicit increased activity in dorsal prefrontal and posterior parietal cortices. • Meanings of homonyms with different syntactic categories are represented distinctively in frontal and temporal cortices. In human languages, it is a common phenomenon for a single word to have multiple meanings. This study used fMRI to investigate how the brain processed different types of lexical ambiguity, and how it differentiated the meanings of ambiguous words. We focused on homonyms and polysemy that differed in the relatedness among multiple meanings. Participants (N = 35) performed a prime-target semantic relatedness task, where a specific meaning of an ambiguous word was primed. Results showed that homonyms elicited greater activation in bilateral dorsal prefrontal and posterior parietal cortices than polysemous words, suggesting that these regions may be more engaged in cognitive control when the meanings of ambiguous words are unrelated. Multivariate pattern analysis further revealed that meanings of homonyms with different syntactic categories were represented differently in the frontal and temporal cortices. The findings highlighted the importance of semantic relations and grammatical factors in the brain's representation of lexical ambiguities. [ABSTRACT FROM AUTHOR]

• The lack of a consensus definition for character strengths creates ambiguity. • Twelve criteria have been proposed for defining character strengths. • Using just one or two of these can define character strengths with 74% precision. • Yet, they fall short in differentiating character strengths from personality. • We recommend using one of our five working definitions for character strengths. Character strengths lack a consensual definition, creating ambiguity in psychological research. This exploratory study tests whether previously discussed ideas about character strengths' shared qualities (e.g., fulfilling, morally valued, traitlike) distinguish them from related concepts, such as the Big Five. Ten laypersons rated 12 of these "criteria" across 80 character strengths adjectives and 170 that pertained to the Big Five, emotions, anatomy, and bodily states. Results indicate that one or two criteria suffice to define character strengths with about 74% accuracy. However, these criteria could not distinguish character strengths from the Big Five, suggesting they may belong to the same conceptual category. We recommend adopting one of five working definitions based on these findings when introducing character strengths in future research. [ABSTRACT FROM AUTHOR]

Shadow detection is significant for scene understanding. Ambiguities in a shadow image, such as shadow-like non-shadow regions and shadow regions with non-shadow patterns, are still very challenging for prevalent CNN-based methods. This work attempts to alleviate this problem from a new perspective of shape semantics, and then proposes a Semantic-aware Transformer (SaT) in a multi-task learning manner. Concretely, we first propose a shadow detection network based on the recent progress of Transformer architecture, allowing us to capture significant global interactions between contexts. Next, we design a multi-task learning framework, combining shadow supervision and semantic supervision to perform a semantic-aware shadow detection. Finally, we introduce a simple yet effective information buffer unit to overcome the gradient signal conflict from multi-task learning. Experimental results on three public benchmark datasets (i.e., ISTD, SBU, and UCF) show that our SaT can effectively detect ambiguous cases and achieve state-of-the-art results. • We propose a shadow detection method using shape semantics. • We design an efficient shadow detection network based on Transformer. • We construct a multi-task learning framework combining shadow and semantic supervision. • An information buffer unit is proposed to coordinate different supervised tasks. [ABSTRACT FROM AUTHOR]

Ambiguity is an important notion in sociology, denoting situations where social actors and groups carry on without shared meaning. The current article applies this concept to the context of religiosity during people's upbringing, recognizing that multiple factors make family-level religion a complex experience. Indeed, though recent research portrays household religiosity in childhood as a sociocultural exposure with long-term implications for well-being, existing studies have yet to incorporate multiple inputs to consider the cohesiveness of that exposure. Using twin data from a national sample, we investigate whether consistency in recalled household religiosity is associated with mid-life flourishing. Multi-level linear regression models reveal that similarity in twin reports matter, above and beyond the actual level of religiosity individuals report and net of dis/similarity across other childhood recollections. We conclude that coherence in religious upbringing—whether religion was understood to be important or not—is a key ingredient for thriving later in life and then reflect more broadly on manifestations of sociocultural ambiguity in families and in larger social units. [ABSTRACT FROM AUTHOR]

The global imperative to mitigate carbon emissions for sustainable development has spurred extensive research into economic, social, and energy-related factors. However, prior studies present a complex landscape, yielding mixed conclusions regarding the influence of geopolitical risk, natural resource rents, corrupt governance, and energy intensity. To untangle this ambiguity, we construct a research model grounded in the Environmental Kuznets Curve, employing panel data from 38 countries spanning 2002 to 2020. Employing panel quantile regression models, we directly assess the impact of identified factors. Our findings affirm the alignment between economic growth and carbon emissions, supporting the Environmental Kuznets Curve hypothesis. Notably, increased geopolitical risk and energy intensity correlate with heightened carbon emissions over time, while corruption governance and natural resource rents exhibit a mitigating effect. Additionally, our study explores the indirect impact of these factors using a panel threshold regression model. Results indicate a diminishing influence of economic growth on carbon emissions. Intriguingly, natural resource rents initially curtail, then amplify the connection between economic growth and carbon emissions. Conversely, rising energy intensity magnifies the relationship between economic expansion and carbon emissions. • Linear and nonlinear models were employed to explore the carbon emission influencing factors. • Explored the direct and indirect effects of geopolitical risk, natural resource rents, corruption and energy intensity on carbon emissions. • Natural resource rents, corruption governance in favour of lower carbon emissions, geopolitical risk and energy intensity in contrast. • Natural resource rents, corruption and geopolitical risks affect the relationship between economic growth and carbon emissions. [ABSTRACT FROM AUTHOR]

A growing literature demonstrates reluctant giving: Many people who voluntarily give to charity no longer do so when they have an excuse not to give. The mechanisms of reluctance, however, remain unclear. Consistent with this literature, we found that injecting attributional ambiguity into a real charitable decision significantly reduces donations. Participants in our studies (N = 2147) faced a binary choice between options for distributing money between themselves and a charity, with one option giving more to a charity and the other leaving more for themselves. Borrowing from a classic attributional ambiguity paradigm, we manipulated whether the charity involved was the same for both options or different, giving participants the possible excuse of keeping more money due to preferring one charity over another. Participants indeed kept more for themselves when there were two different charities, regardless of which charity was associated with the more self-beneficial option, ostensibly revealing a hidden preference for selfishness. Using incentive compatible elications, we found no evidence that participants used the excuse of preferring one charity to another to justify their choices. Instead, we find that attributional ambiguity weakened perceptions that there is a norm against keeping more money in the task, both among decision makers and disinterested third parties. We conclude that attributional ambiguity lowers donations by relieving internalized social pressure to give. [ABSTRACT FROM AUTHOR]

Current non-stationary load models based on the evolutionary power spectral density (EPSD) may lead to overestimation and ambiguity of structural responses. The quasi-stationary harmonizable process with its Wigner-Ville spectrum (WVS) and Loève spectrum, which do not suffer from the deficiencies of EPSD, is suitable for modeling non-stationary loads and analyzing their induced structural responses. In this study, the multi-taper S-transform (MTST) method for estimating WVS and Loève spectrum of multi-variate quasi-stationary harmonizable processes is presented. The analytical biases and variances of the WVS, Loève spectrum, and time-invariant and time-varying coherence estimators from the MTST method are provided under the assumption that the target multi-variate harmonizable process is Gaussian. Using a numerical case of a bivariate harmonizable wind speed process, the superiority and reliability of the MTST method are demonstrated through comparisons with several existing methods for the WVS and Loève spectrum estimations. Finally, the MTST method is applied to two pieces of ground motion acceleration records measured during the Turkey earthquake in 2023. [ABSTRACT FROM AUTHOR]