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In human interactions, the facial expression of a bargaining partner may contain relevant information that affects prosocial decisions. We were interested in whether facial expressions of the recipient in the dictator game influence dictators´ ehavior. To test this, we conducted an online study (n = 106) based on a modified version of a dictator game. The dictators allocated money between themselves and another person (recipient), who had no possibility to respond to the dictator.
Importantly, before the allocation decision, the dictator was presented with the facial expression of the recipient (angry, disgusted, sad, smiling, or neutral). The results showed that dictators sent more money to recipients with sad or smiling facial expressions and less to recipients with angry or disgusted facial expressions compared with a neutral facial expression. Moreover, based on the sequential analysis of the decision and the interaction partner in the preceding trial, we found that decision-making depends upon previous interactions.
Forests are increasingly affected by natural disturbances. Subsequent salvage logging, a widespread management practice conducted predominantly to recover economic capital, produces further disturbance and impacts biodiversity worldwide. Hence, naturally disturbed forests are among the most threatened habitats in the world, with consequences for their associated biodiversity. However, there are no evidence-based benchmarks for the proportion of area of naturally disturbed forests to be excluded from salvage logging to conserve biodiversity. We apply a mixed rarefaction/extrapolation approach to a global multi-taxa dataset from disturbed forests, including birds, plants, insects and fungi, to close this gap. We find that 757% (mean +/- SD) of a naturally disturbed area of a forest needs to be left unlogged to maintain 90% richness of its unique species, whereas retaining 50% of a naturally disturbed forest unlogged maintains 73 +/- 12% of its unique species richness. These values do not change with the time elapsed since disturbance but vary considerably among taxonomic groups. Salvage logging has become a common practice to gain economic returns from naturally disturbed forests, but it could have considerable negative effects on biodiversity. Here the authors use a recently developed statistical method to estimate that ca. 75% of the naturally disturbed forest should be left unlogged to maintain 90% of the species unique to the area.
Background
Over the past two decades, there has been a rising trend in malignant melanoma incidence worldwide. In 2008, Germany introduced a nationwide skin cancer screening program starting at age 35. The aims of this study were to analyse the distribution of malignant melanoma tumour stages over time, as well as demographic and regional differences in stage distribution and survival of melanoma patients.
Methods
Pooled data from 61 895 malignant melanoma patients diagnosed between 2002 and 2011 and documented in 28 German population-based and hospital-based clinical cancer registries were analysed using descriptive methods, joinpoint regression, logistic regression and relative survival.
Results
The number of annually documented cases increased by 53.2% between 2002 (N = 4 779) and 2011 (N = 7 320). There was a statistically significant continuous positive trend in the proportion of stage UICC I cases diagnosed between 2002 and 2011, compared to a negative trend for stage UICC II. No trends were found for stages UICC III and IV respectively. Age (OR 0.97, 95% CI 0.97–0.97), sex (OR 1.18, 95% CI 1.11–1.25), date of diagnosis (OR 1.05, 95% CI 1.04–1.06), ‘diagnosis during screening’ (OR 3.24, 95% CI 2.50–4.19) and place of residence (OR 1.23, 95% CI 1.16–1.30) had a statistically significant influence on the tumour stage at diagnosis. The overall 5-year relative survival for invasive cases was 83.4% (95% CI 82.8–83.9%).
Conclusions
No distinct changes in the distribution of malignant melanoma tumour stages among those aged 35 and older were seen that could be directly attributed to the introduction of skin cancer screening in 2008.
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We investigated the influence of social status on behavior in a modified dictator game (DG). Since the DG contains an inherent dominance gradient, we examined the relationship between dictator decisions and recipient status, which was operationalized by three social identities and an artificial intelligence (AI). Additionally, we examined the predictive value of social dominance orientation (SDO) on the behavior of dictators toward the different social and non-social hierarchical recipients. A multilevel model analysis showed that recipients with the same status as the dictator benefited the most and the artificial intelligence the least. Furthermore, SDO, regardless of social status, predicted behavior toward recipients in such a way that higher dominance was associated with lower dictator offers. In summary, participants treated other persons of higher and lower status equally, those of equal status better and, above all, an algorithm worst. The large proportion of female participants and the limited variance of SDO should be taken into account with regard to the results of individual differences in SDO.
Background: Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers’ degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies.
New Method: With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig.
Results: Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included.
Comparison with existing methods: This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches.
Conclusion: The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research.
The present dissertation aims to shed light on different mechanisms of socio-emotional feedback in social decision-making situations. The objective is to evaluate emotional facial expressions as feedback stimuli, i.e., responses of interaction partners to certain social decisions. In addition to human faces, artificial emojis are also examined due to their relevance for modern digital communication. Previous research on the influence of emotional feedback suggests that a person's behavior can be effectively reinforced by rewarding stimuli. In the context of this dissertation, the differences in the feedback processing of human photographs and emojis, but also the evaluation of socially expected versus socially unexpected feedback were examined in detail in four studies. In addition to behavioral data, we used the electroencephalogram (EEG) in all studies to investigate neural correlates of social decision-making and emotional feedback.
As the central paradigm, all studies were based on a modified ultimatum game. The game is structured as follows: there is a so-called proposer who holds a specific amount of money (e.g., 10 cents) and offers the responder a certain amount (e.g., 3 cents). The responder then decides whether to accept or reject the offer. In the version of the ultimatum game presented here, different types of proposers are introduced. After the participants have accepted or rejected in the role of the responder, the different proposers react to the participant’s decision with specific emotional facial expressions. Different feedback patterns are used for the individual experiments conducted in the course of this dissertation.
In the first study, we investigated the influence of emotional feedback on decision-making in the modified version of the ultimatum game. We were able to show that a proposer who responds to the acceptance of an offer with a smiling face achieves more accepted offers overall than a control proposer who responds to both accepted and rejected offers with a neutral facial expression. Consequently, the smile served as a positive reinforcement. Similarly, a sad expression in response to a rejected offer also resulted in higher acceptance rates as compared to the control identity, which could be considered an expression of compassion for that proposer. On a neuronal level, we could show that there are differences between simply looking at negative emotional stimuli (i.e., sad and angry faces) and their appearance as feedback stimuli after rejected offers in the modified ultimatum game. The so-called feedback-related negativity was reduced (i.e., more positive) when negative emotions appeared as feedback from the proposers. We argued that these findings might show that the participants wanted to punish the proposers by rejecting an offer for its unfairness and therefore the negative feedback met their expectations. The altered processing of negative emotional facial expressions in the ultimatum game could therefore indicate that the punishment is interpreted as successful. This includes the expectation that the interaction partner will change his behavior in the future and eventually make fairer offers.
In the second study we wanted to show that smiling and sad emojis as feedback stimuli in the modified ultimatum game can also lead to increased acceptance rates. Contrary to our assumptions, this effect could not be observed. At the neural level as well, the findings did not correspond to our assumptions and differed strongly from those of the first study. One finding, however, was that the neural P3 component showed how the use of emojis as feedback stimuli particularly characterizes certain types of proposers. This is supported by the fact that the P3 is increased for the proposer who rewards an acceptance with a smile as well as for the proposer who reacts to rejection with a sad emoji compared to the neutral control proposer.
The third study examined the discrepancy between the findings of the first and second study. Accordingly, both humans and emojis representing the different proposers were presented in the ultimatum game. In addition, emojis were selected that showed a higher similarity to known emojis from common messenger services compared to the second study. We were able to replicate that the proposers in the ultimatum game, who reward an acceptance of the offer with a smile, led to an increased acceptance rate compared to the neutral control proposers. This difference is independent of whether the proposers are represented by emojis or human faces. With regard to the neural correlates, we were able to demonstrate that emojis and human faces differ strongly in their neural processing. Emojis showed stronger activation than human faces in the face-processing N170 component, the feedback-related negativity and the P3 component. We concluded that the results of the N170 and feedback-related negativity could indicate a signal for missing social information of emojis compared to faces. The increased P3 amplitude for emojis might imply that emojis appear unexpectedly as reward stimuli in a social decision task compared to human faces.
The last study of this project dealt with socially unexpected feedback. In comparison to the first three studies, new proposer identities were implemented. In particular, the focus was on a proposer who reacted to the rejection of an offer unexpectedly with a smile and to the acceptance with a neutral facial expression. According to the results, participants approach this unexpected smile through increased rejection, although it is accompanied by financial loss. In addition, as reported in studies one and three, we were able to show that proposers who respond to the acceptance of an offer with a smiling face and thus meet the expectations of the participants have higher offer acceptance rates than the control proposer. At the neuronal level, especially the feedback from the socially unexpected proposer led to an increased P3 amplitude, which indicates that smiling after rejection is attributed a special subjective importance.
The experiments provide new insights into the social influence through emotional feedback and the processing of relevant social cues. Due to the conceptual similarity of the studies, it was possible to differentiate between stable findings and potentially stimulus-dependent deviations, thus creating a well-founded contribution to the current research. Therefore, the novel paradigm presented here, and the knowledge gained from it could also play an important role in the future for clinical questions dealing with limited social competencies.
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
In everyday life, assumptions about our peers' as well as our own personality shape social interactions. We investigated whether self-rated personality and inferences drawn from partners' faces influence economic decisions. Participants (N = 285) played the trust game in the role of the trustor as well as the ultimatum game in the role of the proposer and interacted with trustees and receivers represented by prototypical personality faces. Participants also evaluated both their own traits and the personality of the faces. In the trust game, trustees represented by faces rated higher on agreeableness yielded higher transferred amounts. This effect was more pronounced for trustors low on dispositional trust, whereas trustors high on dispositional trust did not relate their decisions to the faces. Trustees represented by faces rated higher on conscientiousness yielded higher transferred amounts only for trustors high on dispositional anxiety. In the ultimatum game, receivers represented by faces rated higher on conscientiousness yielded lower offers only for proposers high on dispositional assertiveness. These results extend previous findings on the inferences drawn from facial features and the influence of personality on decision making. They highlight the importance of considering the personality of both interaction partner, as well as potential interactions of players' traits.
Despite its negative reputation, egoism – the excessive concern for one’s own welfare – can incite prosocial behavior. So far, however, egoism-based prosociality has received little attention. Here, we first provide an overview of the conditions under which egoism turns into a prosocial motive, review the benefits and limitations of egoism-based prosociality, and compare them with empathy-driven prosocial behavior. Second, we summarize studies investigating the neural processing of egoism-based prosocial decisions, studies investigating the neural processing of empathy-based prosocial decisions, and the small number of studies that compared the neural processing of prosocial decisions elicited by the different motives. We conclude that there is evidence for differential neural networks involved in egoism and empathy-based prosocial decisions. However, this evidence is not yet conclusive, because it is mainly based on the comparison of different experimental paradigms which may exaggerate or overshadow the effect of the different motivational states. Finally, we propose paradigms and research questions that should be tackled in future research that could help to specify how egoism can be used to enhance other prosocial behavior and motivation, and the how it could be tamed.