Lie detection algorithms disrupt the social dynamics of accusation behavior.

Humans, aware of the social costs associated with false accusations, are generally hesitant to accuse others of lying. Our study shows how lie detection algorithms disrupt this social dynamic. We develop a supervised machine-learning classifier that surpasses human accuracy and conduct a large-scale incentivized experiment manipulating the availability of this lie-detection algorithm. In the absence of algorithmic support, people are reluctant to accuse others of lying, but when the algorithm becomes available, a minority actively seeks its prediction and consistently relies on it for accusations. Although those who request machine predictions are not inherently more prone to accuse, they more willingly follow predictions that suggest accusation than those who receive such predictions without actively seeking them.

Integrating gender analysis into research: reflections from the Gender-Net Plus workshop.

Gender equality has been a crosscutting issue in Horizon 2020 with three objectives: gender balance in decision-making, gender balance and equal opportunities in project teams at all levels, and inclusion of the gender dimension in research and innovation content. Between 2017 and 2022, the EU funded, in collaboration with national agencies, 13 transnational projects under "GENDER-NET Plus" that explored how to best integrate both sex and gender into studies ranging from social sciences, humanities, and health research. As the projects neared completion, forty researchers from these interdisciplinary teams met in November 2022 to share experiences, discuss challenges, and consider the best ways forward to incorporate sex and gender in research. Here, we summarize the reflections from this workshop and provide some recommendations for i) how to plan the studies (e.g., how to define sex and/or gender and their dimensions, rationale for the hypotheses, identification of data that can best answer the research question), ii) how to conduct them (e.g., adjust definitions and dimensions, perform pilot studies to ensure proper use of terminology and revise until consensus is achieved), and iii) how to analyze and report the findings being mindful of any real-world impact.

Assessing interconnectedness and systemic importance of Chinese financial institutions.

This study proposes a directed acyclic graph (DAG)-based framework for generalized variance decomposition for investigating the heterogeneous return spillovers in financial system and measuring the systemic importance of financial institutions among 34 listed Chinese financial institutions from 2011 to 2023. Findings indicate pronounced information spillovers among institutions within the same sector due to contemporaneous causal relationships. Both static and dynamic financial network analyses highlight the significance of the securities sector. Dynamic structural characteristics align with macroeconomic development and are sensitive to internal and external shocks. Systemic importance assessment reveals that market size alone doesn't determine importance, with notable disparities between banking and non-banking sectors. State-owned and joint-stock commercial banks play a vital role in banking, while local government and private capital-controlled institutions are crucial in the securities sector. This research aids regulatory efforts in maintaining a balanced regulatory environment, ensuring market efficiency, and reducing operational costs.

Nepotism mediates enforced cooperation in asymmetric negotiations.

In cooperative societies, group members typically exchange different commodities among each other, which involves an incessant negotiation process. How is the conflict of fitness interests resolved in this continual bargaining process between unequal partners, so that maintaining the cooperative interaction is the best option for all parties involved? Theory predicts that relatedness between group members may alleviate the conflict of fitness interests, thereby promoting the evolution of cooperation. To evaluate the relative importance of relatedness and direct fitness effects in the negotiation process, we experimentally manipulated both the relatedness and mutual behavioral responses of dominant breeders and subordinate helpers in the cooperatively breeding cichlid fish Neolamprologus pulcher. Results show that coercion by breeders is crucial for the performance of alloparental egg care by helpers, but that kinship significantly decreases the need for coercion as predicted by theory. This illustrates the relative importance of kinship and enforcement in the bargaining process.

Integration of behavioral, social, and humanities sciences into healthcare and education and their alignment with medical education programs.

There has been an increasing acknowledgment of the intricacies inherent in health and healthcare processes, leading to a shift in medical education. This change underscores multidimensional, thorough, reflective, and contextual approaches characterized by mutual interaction and change. The perception of health/well-being and illness is transitioning toward acknowledging them as outcomes arising from the complex interplay of individual, social, and environmental/ecosystemic factors. This includes biological, genetic, behavioral, sociocultural, and environmental influences. In line with this changing perspective, the purpose of this article is to provide a general framework for the integration of behavioral, social, and human sciences into Medical Education Programs in Healthcare and Training Processes. The framework presented is based on the following three conceptual and theoretical basis: (1) Complex systems thinking and reflective, contextual healthcare and education practices, (2) Health systems and socio-economic-political framework, (3) Ecosystem framework in health and disease. Our aim in this article is to provide a guide for the integration of Behavioral, Social, and Humanity Sciences into medical education programs and to present examples from around the world.

Beyond human-likeness: Socialness is more influential when attributing mental states to robots.

We sought to replicate and expand previous work showing that the more human-like a robot appears, the more willing people are to attribute mind-like capabilities and socially engage with it. Forty-two participants played games against a human, a humanoid robot, a mechanoid robot, and a computer algorithm while undergoing functional neuroimaging. We confirmed that the more human-like the agent, the more participants attributed a mind to them. However, exploratory analyses revealed that the perceived socialness of an agent appeared to be as, if not more, important for mind attribution. Our findings suggest top-down knowledge cues may be equally or possibly more influential than bottom-up stimulus cues when exploring mind attribution in non-human agents. While further work is now required to test this hypothesis directly, these preliminary findings hold important implications for robotic design and to understand and test the flexibility of human social cognition when people engage with artificial agents.

Balancing long-term and short-term strategies in a sustainability game.

Our society is marked by a tension between short-term objectives, such as economic growth, and long-term sustainability goals, including mitigating resource depletion. In such a competitive setting, it is crucial to ascertain whether a system can maintain long-term viability and, if so, how. This article aims to enhance the understanding of this issue by analyzing how sustainability concerns change over time by means of a game, and the effect of this variation on the final status of a system. Leveraging insights from the game, we implement an agent-based model to elicit the tension between short-term objectives and sustainability, emphasizing the influence of individual actions on the overall system. The simulation results suggest that the likelihood of a collapse is contingent upon the availability of resources and the manner in which information regarding these resources is gathered and utilized. Finally, the paper proposes practical suggestions for managing this kind of system.

Algorithmic bias: Social science research integration through the 3-D Dependable AI Framework.

Algorithmic bias has emerged as a critical challenge in the age of responsible production of artificial intelligence (AI). This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents and consequences of algorithmic bias. Through the lens of the 3-D Dependable AI Framework, this article explores how social science disciplines, such as psychology, can contribute to identifying and mitigating bias at the Design, Develop, and Deploy stages of the AI life cycle. Finally, we propose future research directions to further address the complexities of algorithmic bias and its societal implications.

Cooperation and competition have same benefits but different costs.

Cooperation and competition shape everyday human interactions and impact individuals' chances of success in different domains. Using a virtual Stroop test, classically employed to assess general cognitive interference, we examined the impact of social context (cooperation and competition) and other's ability (higher and lower performers) on performance, perceived stress, and autonomic activity. In Experiment 1, we found that both cooperation with a lower performer and competition with a higher performer led to similar enhancement in performance. However, only competition with a more skilled opponent induced an increase in perceived stress and physiological activity. Experiment 2 further demonstrated that these effects persisted even with prolonged exposure to these contexts. In summary, cooperation can be just as effective as competition in improving individuals' performance. However, cooperation does not carry the same level of stress and physiological burden as the competitive context, representing a healthier and more optimal way to boost individual performance.

Fertility resilience varies by socioeconomic status and sex: Historical trends in childlessness across 150 years.

Fertility dynamics are key drivers of demographic change in a population. Fertility resilience is likely to vary by socioeconomic class, yet little investigated. Using a unique dataset tracking the reproduction of family lineages for 150 years, we explored childlessness by socioeconomic status and sex during the demographic transition and recurring societal and economic disturbances in Finland. Lifetime childlessness doubled from the 1800 birth cohort to the 1945-1949 cohort. Higher socioeconomic status (SES) indicated higher lifetime likelihood to reproduce. The fluctuations in childlessness over time appeared to be driven by the low socioeconomic group, showing low fertility resilience. In contrast, a steady increase was seen in high and moderate SES. Our findings suggest that the family formation of lower socioeconomic groups suffers the most during crises and does not necessarily recuperate. Preventing inequalities in family formation and reproduction should be recognized as a key challenge for population resilience to crises.

Path of excellence: A co-authorship network analysis of European Research Council grant winners in social sciences.

The international scientific community puts an ever-growing emphasis on research excellence and performance evaluation. So does the European Union with its flagship research excellence grant scheme organised by the European Research Council. This paper aims to provide an in-depth analysis of one of the ERC's thematic panels within the social sciences, namely the SH2 "Political Science" panel. The analysis is based on empirical, statistical methods, and network analysis tools to gain insights about the grant winners' publication patterns and their coauthor networks. The results draw up an academic career track of the grantees based on quantitative publication patterns and performance. Besides, a change in authorship can be observed, which is proven by the formation of new groups and intensifying intra-group collaboration patterns in the case of all three grant types. However, the ERC grant serves different functions for the winners of three different categories: for the Starting Grant winners, it offers the possibility to kick off and establish their research group, for the Consolidator Grant winners, it opens up new opportunities to extend their co-authorship network, and for the Advanced Grant winners, it offers the chance to start a new collaboration.

Consensus and contention in beauty judgment.

Variance across participants is at the heart of the centuries-old debate about the universality of beauty. Beauty's belonging to the eye of the beholder implies large interindividual variance, while beauty as a universal object property implies the opposite. To characterize the variance at the center of this debate, we selected two quartets with either high- or low-variance images with high typicality and a given mean beauty. The quartets have high or low variance across 50 participants (group variance) and correspondingly high or low variance across images of a quartet for each participant (quartet variance). We asked 52 new participants to estimate their own mean and quartet variance. Participants successfully predicted their quartet mean but failed to predict their quartet variance. Though invisible, beauty variance is essential to prediction, both in theory and in practice. The quartets show that mean beauty is not the whole story - beauty variance is heterogeneous.

Advancing health equity in the aftermath of COVID-19: Confronting intensifying racial disparities.

The COVID-19 pandemic has exposed and exacerbated the persistent racial and ethnic health disparities in the United States. The pandemic has also had profound spillover effects on other aspects of health and wellbeing, such as mental health, chronic diseases, education, and income, for marginalized groups. In this article, we provide a thorough analysis of the pandemic's impact on racial and ethnic health disproportionalities, highlighting the multifaceted and interrelated factors that contribute to these inequities. We also argue for a renewed focus on health equity in healthcare policy and practice, emphasizing the need for systemic changes that address both the immediate and long-term consequences of these imbalances. We propose a framework for achieving health equity that involves creating equitable systems, care, and outcomes for all individuals, regardless of their race or ethnicity.

Fine-grained knowledge about manipulable objects is well-predicted by contrastive language image pre-training.

Object recognition is an important ability that relies on distinguishing between similar objects (e.g., deciding which utensil(s) to use at different stages of meal preparation). Recent work describes the fine-grained organization of knowledge about manipulable objects via the study of the constituent dimensions that are most relevant to human behavior, for example, vision, manipulation, and function-based properties. A logical extension of this work concerns whether or not these dimensions are uniquely human, or can be approximated by deep learning. Here, we show that behavioral dimensions are generally well-predicted by CLIP-ViT - a multimodal network trained on a large and diverse set of image-text pairs. Moreover, this model outperforms comparison networks pre-trained on smaller, image-only datasets. These results demonstrate the impressive capacity of CLIP-ViT to approximate fine-grained object knowledge. We discuss the possible sources of this benefit relative to other models (e.g., multimodal vs. image-only pre-training, dataset size, architecture).

Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain.

Recent human brain imaging studies have identified widely distributed cortical areas that represent information about the meaning of language. Yet, the dynamic nature of widespread neural activity as a correlate of the semantic information processing remains poorly explored. Our state space analysis of electroencephalograms (EEGs) recorded during semantic match-to-category task show that depending on the semantic category and decision path chosen by participants, whole-brain delta-band dynamics follow distinct trajectories that are correlated with participants' response time on a trial-by-trial basis. Especially, the proximity of the neural trajectory to category decision-specific region in the state space was predictive of participants' decision-making reaction times. We also found that posterolateral regions primarily encoded word categories while postero-central regions encoded category decisions. Our results demonstrate the role of neural dynamics embedded in the evolving multivariate delta-band activity patterns in processing the semantic relatedness of words and the semantic category-based decision-making.

Striking the Balance: Embracing Technology While Upholding Humanistic Principles in Neurorehabilitation.

BACKGROUND: The rapid advancement of technology-focused strategies in neurorehabilitation has brought optimism to individuals with neurological disorders, caregivers, and physicians while reshaping medical practice and training. OBJECTIVES: We critically examine the implications of technology in neurorehabilitation, drawing on discussions from the 2021 and 2024 World Congress for NeuroRehabilitation. While acknowledging the value of technology, it highlights inherent limitations and ethical concerns, particularly regarding the potential overshadowing of humanistic approaches. The integration of technologies such as robotics, artificial intelligence, neuromodulation, and brain-computer interfaces enriches neurorehabilitation by offering interdisciplinary solutions. However, ethical considerations arise regarding the balance between compensation for deficits, accessibility of technologies, and their alignment with fundamental principles of care. Additionally, the pitfalls of relying solely on neuroimaging data are discussed, stressing the necessity for a more comprehensive understanding of individual variability and clinical skills in rehabilitation. RESULTS: From a clinical perspective, the article advocates for realistic solutions that prioritize individual needs, quality of life, and social inclusion over technological allure. It underscores the importance of modesty and honesty in responding to expectations while emphasizing the uniqueness of each individual's experience. Moreover, it argues for the preservation of human-centric approaches alongside technological advancements, recognizing the invaluable role of clinical observation and human interaction in rehabilitation. CONCLUSION: Ultimately, the article calls for a balanced attitude that integrates both scientific and humanistic perspectives in neurorehabilitation. It highlights the symbiotic relationship between the sciences and humanities, advocating for philosophical questioning to guide the ethical implementation of new technologies and foster interdisciplinary dialogue.

Professional opportunities, gender obstacles, and narrowed progression: The case of the first Social Science Research Council female fellows (1925-1934).

Between the years 1925 and 1934, the Social Science Research Council (SSRC) awarded 198 postdoctoral research fellowships to early-career social scientists, among which 29 were awarded to women. This article, which is based on the SSRC directory and Rockefeller institutions' records, examines the professional paths of these female fellows to shed light on the presence of women in the social sciences and to probe the peculiarities of their professional trajectories. The SSRC fellowships represented a significant professional prospect for brilliant young female graduates who were often denied similar opportunities in other fields. Nonetheless, they did not eradicate all gender discrimination that remained prevalent, not only in the vertical sense by preventing women from progressing in the academic hierarchy, but also in the horizontal sense by retaining them in designated spaces (specific disciplines or institutions) that were underrecognized. Ultimately, the analysis of women's professional paths underscores the importance of examining the private or intimate lives of scientists to gain a more in-depth understanding of the social structure of science and its impact on its protagonists.

Reactivating cue approached positive personality traits during sleep promotes positive self-referential processing.

People preferentially endorse positive personality traits as more self-descriptive than negative ones, a positivity self-referential bias. Here, we investigated how to enhance positive self-referential processing, integrating wakeful cue-approach training task (CAT) and sleep-based targeted memory reactivation (TMR). In the CAT, participants gave speeded motor responses to cued positive personality traits. In a subsequent nap, we unobtrusively re-played half of the trained positive traits during slow-wave sleep (TMR). Upon awakening, CAT+TMR facilitated participants' speed in endorsing positive traits in immediate tests, and rendered participants endorse more positive traits as self-descriptive after one week. Notably, these enhancements were associated with the directionality of cue-related 1-4 Hz slow traveling waves (STW) that propagate across brain regions. Specifically, anterior-to-posterior backward STW was positively associated with these benefits, whereas forward STW showed negative associations. These findings demonstrate the potential benefits of integrated wakeful cue-approach training and sleep-based memory reactivation in strengthening positive self-referential processing.

Pain cues override identity cues in baby cries.

Baby cries can convey both static information related to individual identity and dynamic information related to the baby's emotional and physiological state. How do these dimensions interact? Are they transmitted independently, or do they compete against one another? Here we show that the universal acoustic expression of pain in distress cries overrides individual differences at the expense of identity signaling. Our acoustic analysis show that pain cries, compared with discomfort cries, are characterized by a more unstable source, thus interfering with the production of identity cues. Machine learning analyses and psychoacoustic experiments reveal that while the baby's identity remains encoded in pain cries, it is considerably weaker than in discomfort cries. Our results are consistent with the prediction that the costs of failing to signal distress outweigh the cost of weakening cues to identity.