When Ignoring Negative Feedback Is Functional:Presenting a Model of Motivated Feedback Disengagement

Contrary to popular belief, negative feedback occasionally hinders performance improvements. Investigations targeting this feedback-performance gap usually rest on two assumptions: (a) Feedback recipients want to improve their performance (have an improvement goal), and (b) feedback recipients engage with the negative feedback. We argue that people sometimes disengage from negative feedback for hedonic-goal attainment (to feel good). To explain such functional feedback disengagement, we conceptualize feedback processing from an emotion-regulation perspective, the model of motivated feedback disengagement. We posit that feedback-induced negative affect may render hedonic goals more salient than improvement goals, motivating emotion regulation. After forming the intention to regulate their emotions, feedback recipients select and implement an emotion-regulation strategy. We consider two common engagement strategies (reappraisal and feedback focus) and two common disengagement strategies (distraction and feedback removal). These strategies differentially impact recipients’ affect and feedback processing. Strategy-, person-, and situation-related factors influence strategy choice. Feedback processing is cyclical and dynamically unfolds over time. The model provides novel directions for future investigations and practical implications for stakeholders in negative-feedback contexts

Face masks reduce emotion-recognition accuracy and perceived closeness

Face masks became the symbol of the global fight against the coronavirus. While face masks’ medical benefits are clear, little is known about their psychological consequences. Drawing on theories of the social functions of emotions and rapid trait impressions, we tested hypotheses on face masks’ effects on emotion-recognition accuracy and social judgments (perceived trustworthiness, likability, and closeness). Our preregistered study with 191 German adults revealed that face masks diminish people’s ability to accurately categorize an emotion expression and make target persons appear less close. Exploratory analyses further revealed that face masks buffered the negative effect of negative (vs. non-negative) emotion expressions on perceptions of trustworthiness, likability, and closeness. Associating face masks with the coronavirus’ dangers predicted higher perceptions of closeness for masked but not for unmasked faces. By highlighting face masks’ effects on social functioning, our findings inform policymaking and point at contexts where alternatives to face masks are needed

Grandiose narcissism shapes counterfactual thinking (and regret):Direct and indirect evidence

Little is known about how individuals high in grandiose narcissism think about what could have been. Across four studies (three online surveys and one online experiment; N = 801), we addressed this gap by examining the relationship between grandiose narcissism, its admiration and rivalry dimensions, and counterfactual thinking and regret. Unlike anticipated, high rivalry was associated with more rather than fewer upward counterfactuals in Study 1. Yet, high rivalry predicted an increased likelihood of generating a downward (vs. upward) counterfactual in a feedback situation (Study 3). Moreover, grandiose narcissism (preliminary study) and admiration (Study 2) negatively correlated with regret. Collectively, our findings stress the importance of considering grandiose narcissism’s dimensions separately and highlight a novel dispositional moderator of counterfactual thinking

The Role of Pubertal Timing and Heterosocial Involvement in Early Adolescents’ Media Internalization:A Moderated Moderation Analysis

The present three-wave panel study (N = 968, (Formula presented.) = 11.30, SD = 1.06) examines how developmental factors—pubertal timing and heterosocial involvement (i.e., one’s involvement in cross-sex activities)—influence early adolescents’ level of media internalization. We hypothesized that early pubertal timing positively moderates the association between sexualizing magazine reading and media internalization. Next, we argued that increased heterosocial involvement will weaken the amplifying influence of early pubertal timing on the aforementioned relationship. Both hypotheses were confirmed. For early adolescents who mature earlier than same-age/sex peers, reading sexualizing magazines resulted in more media internalization. Furthermore, our results showed that moderate to high cross-sex peer interactions can serve as a protective force against the negative influence of early pubertal timing. These results highlight the influential role of appearance-related developmental factors in the processing of sexualizing magazine content and point to the potential protective role of cross-sex peer interactions in media internalization

Empathising with masked targets:Limited side effects of face masks on empathy for dynamic, context-rich stimuli

Multiple studies revealed detrimental effects of face masks on communication, including reduced empathic accuracy and enhanced listening effort. Yet, extant research relied on artificial, decontextualised stimuli, which prevented assessing empathy under more ecologically valid conditions. In this preregistered online experiment (N = 272), we used film clips featuring targets reporting autobiographical events to address motivational mechanisms underlying face mask effects on cognitive (empathic accuracy) and emotional facets (emotional congruence, sympathy) of empathy. Surprisingly, targets whose faces were covered by a mask (or a black bar) elicited the same level of empathy motives (affiliation, cognitive effort), and accordingly, the same level of cognitive and emotional empathy compared to targets with uncovered faces. We only found a negative direct effect of face coverings on sympathy. Additional analyses revealed that older (compared to young) adults showed higher empathy, but age did not moderate face mask effects. Our findings speak against strong negative face mask effects on empathy when using dynamic, context-rich stimuli, yet support motivational mechanisms of empathy

Cross-Lingual Knowledge Transfer for Clinical Phenotyping

Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-of-the-art models are mostly applicable to clinical notes written in English. We therefore investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language and have a small amount of in-domain data available. We evaluate these strategies for a Greek and a Spanish clinic leveraging clinical notes from different clinical domains such as cardiology, oncology and the ICU. Our results reveal two strategies that outperform the state-of-the-art: Translation-based methods in combination with domain-specific encoders and cross-lingual encoders plus adapters. We find that these strategies perform especially well for classifying rare phenotypes and we advise on which method to prefer in which situation. Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness.Comment: LREC 2022 submmision: January 202

MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain

This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERTde are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community.Comment: Keno K. Bressem and Jens-Michalis Papaioannou and Paul Grundmann contributed equall

Multi-channel photodissociation and XUV-induced charge transfer dynamics in strong-field-ionized methyl iodide studied with time-resolved recoil-frame covariance imaging

The photodissociation dynamics of strong-field ionized methyl iodide (CH3I) were probed using intense extreme ultraviolet (XUV) radiation produced by the SPring-8 Angstrom Compact free electron LAser (SACLA). Strong-field ionization and subsequent fragmentation of CH3I was initiated by an intense femtosecond infrared (IR) pulse. The ensuing fragmentation and charge transfer processes following multiple ionization by the XUV pulse at a range of pump–probe delays were followed in a multi-mass ion velocity-map imaging (VMI) experiment. Simultaneous imaging of a wide range of resultant ions allowed for additional insight into the complex dynamics by elucidating correlations between the momenta of different fragment ions using time-resolved recoil-frame covariance imaging analysis. The comprehensive picture of the photodynamics that can be extracted provides promising evidence that the techniques described here could be applied to study ultrafast photochemistry in a range of molecular systems at high count rates using state-of-the-art advanced light sources.</p

Explainable AI under contract and tort law

This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the (legally required) trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification.Peer Reviewe