ObjectiveThis study aims to identify the factors associated with decision regret among colorectal cancer patients undergoing chemotherapy in China and to determine whether decision self-efficacy mediates the relationship between social support and decision regret.MethodsA cross-sectional study was conducted using convenience sampling with 243 colorectal cancer patients receiving chemotherapy. From 29 July 2025–2 October 2025., all participants were recruited from a tertiary hospital in Inner Mongolia, China. Data were collected through a sociodemographic and clinical questionnaire, the Social Support Scale, the Decision Self-Efficacy Scale, and the Three-Dimensional Decision Regret Scale. Non-parametric tests were employed to analyze associated factors, and mediation analysis was performed using SPSS and AMOS.ResultsEducational attainment, occupational status, and monthly income were significantly associated with decision regret among patients. Further analysis revealed a negative association between social support and decision regret in individuals undergoing chemotherapy for colorectal cancer (β = –0.306, p < 0.001). Path estimates showed that social support was positively associated with decision self-efficacy (β = 0.471, p < 0.001), while decision self-efficacy was negatively associated with decision regret (β = –0.581, p < 0.001). Decision self-efficacy functioned as a mediator linking social support to decision regret, producing an indirect effect of –0.273, which accounted for 47.24% of the total effect.ConclusionNotable interactions were observed among social support, decision self-efficacy, and decision regret, with decision self-efficacy serving as the mediating mechanism. Clinicians are encouraged to prioritize strengthening both external social support and patients’ internal decision self-efficacy, which were associated with lower decision regret and better quality of life. Additionally, particular attention should be directed toward individuals with lower income, limited employment, or lower educational attainment, as they demonstrate a heightened vulnerability to experiencing decision regret.

Group dynamics is a sub-field of social psychology that explores both intragroup and intergroup processes and how they affect attitudes and behaviours. As a psychology-based lens for understanding cyber security beliefs and behaviours, the group dynamics literature offers largely untapped insights. Its neglect is partly due to the inadequacies of many existing quantitative tools used in cyber security practice when it comes to capturing group processes. This paper explores some ways in which the group dynamics literature can be leveraged to illuminate end-user cyber security beliefs and behaviours in organisations, focusing on research about social identity, intergroup relations and group decision making. Practical implications of this body of work are discussed in relation to prominent and topical behavioural cyber security challenges such as the use of phishing simulations, red teaming, punishment regimes, communications and training, and hybrid working. This article is also included in The Business &amp; Management Collection which can be accessed at https://hstalks.com/business/.

Jointly made decisions are a common feature across economic and social systems. Various decision rules can be used to identify a jointly negotiated offer. One such rule ( MaxJoint ) seeks to maximise the joint payoffs of the two parties and is related to the objective of efficiency. Another rule ( MinDiff ) seeks to minimise the difference in payoff between two parties and is related to the objective of fairness. These two objectives are often in tension, resulting in an efficiency–fairness trade-off. This article explores the dynamics of these and other joint decision rules within the context of negotiation, a form of social decision-making in which parties with conflicting views attempt to reach a jointly agreed-upon settlement. Through Monte Carlo simulation of an illustrative two-party multi-issue negotiation, the existence of this trade-off is confirmed and the price of fairness, that is, the reduction in efficiency by the imposition of a fairness constraint, is quantified. JEL Codes: C78, C63, B410

Borderline personality disorder (BPD) is characterized by instability in interpersonal relationships, self-image, and affect. Dysregulated negative emotional processing involving prefrontal and limbic circuits is considered a neural basis of BPD. However, it remains unclear how prefrontal modulation of social decision-making in BPD differs from non-psychiatric controls. To investigate social decision-making in response to unfairness, we conducted an fMRI study involving adults with a diagnosis of BPD (n=77) and healthy controls (HC; n=60). Using an inequality aversion model, we derived parameters of social norm adaptation and inequality sensitivity from behavioral data during a modified ultimatum game designed to measure responses to offer norm shifts. Valence and salience signal-processing models isolated prefrontal activations related specifically to social norm prediction error (NPE). Cumulative rejection rates indicated that individuals diagnosed with BPD exhibited consistent differences in overall offer rejection rates but similar adaptation to HC when responding to norm shifts. Preservation of normative social decision-making in BPD (no significant difference vs. HC) was evident in regression analyses of rejection rates and in reinforcement learning models, with no group differences observed in Rescorla-Wagner parameters. Furthermore, we detected no significant neural activation differences between groups, although ventral regions of the medial prefrontal cortex were preferentially involved in valence-related rather than salience-related polynomial modulation. Contrary to our hypotheses, neither behavioral nor neural responses to economic norm violations differed significantly between BPD and HC groups across one-shot games involving unknown partners. Future research could explore whether more personally relevant or higher-stress social contexts elicit differences not observed here.

As digital technologies continue to advance in our lives, the author raises questions about the challenges and vulnerabilities that we might face in our future social and political interactions and decision making. The article explores the impact of digital technology on political life focusing on the politics of perception control, the creation of echo chamber bubbles, and the dangers of fragmentation and polarization. The presidential election in Romania offers an opportunity to look at the impact of the digital on the political in the context of the resurgence of an autocratic nationalistic narrative with a fascist flavor. The author explores the cognitive and emotional vulnerabilities we might encounter in an emergent digital world with a focus on social cognition.

No effect of glucocorticoid and noradrenergic activity on consistency in prosocial choice.

Training and oversight of algorithms in social decision-making: Algorithms with prescribed selfish defaults breed selfish decisions

In the present era, parenting styles play a crucial role in the development of students' personality, behavior, and decision-making abilities. The decisions students make in their lives—such as educational, vocational, and social decisions—depend on their self-confidence, experiences, and family environment. Parents' disciplinary, permissive, or neglectful styles directly or indirectly influence students' thought processes and decision-making. The main objective of this study is to examine the impact of parenting styles on the decision-making abilities of higher secondary students. This study attempts to determine whether different parenting styles have a significant impact on students' decision-making abilities.

Decisions made by social work professionals determine the scope and nature of the help clients receive. These decisions rest on professional judgement, making it essential to understand the foundations of such judgement to grasp social work practice. This qualitative study explores professional decision-making and practice among Danish addiction counsellors working in outpatient treatment for alcohol use disorders. The aim was to examine what the counsellors base their professional judgements on and the reasoning that informs their decisions. Utilizing the vignette method, we conducted semi-structured interviews with 12 addiction counsellors. Interviews were analyzed using thematic analysis. Our findings show how the counsellors base their professional judgements and decisions on knowledge and values, respectively. We identified differences among counsellors in how values were understood and applied. Several values lacked clear definitions and appeared in varying or subtly distinct forms. The study underscores the need to clarify the values guiding social workers' judgements and decisions. Values shape how knowledge is used, making their clarification essential for evidence-based practice. Without such clarity, value-based practice becomes meaningless, and values cannot effectively guide professionals in their clinical work.

Families of young children with medical complexities experience barriers related to their children’s disability, which are exacerbated for families with additional marginalized identities. An intersectional lens is important for understanding how a family’s multiple and overlapping identities shape their experiences. In this case study, we explored the ways in which one family’s intersectionality resulted in issues of power within their daily life and early intervention services using multiple sources of qualitative data. Our findings include five patches that illustrate the family’s intersectional experiences and highlight issues of power, bias, and oppression. The patches include (1) the child’s birth and neonatal experience, (2) evicted to unhoused, (3) disruptions caused by social services, (4) disruptions and decisions about EI, and (5) providers’ passivity and insensitivity to intersectional identities. We share implications for how EI providers, systems leaders, and researchers can use an intersectional lens in their work.

This study investigates the key factors that influence marriage decision among Chinese youth using machine learning techniques. Using data from the China Family Panel Studies (2018-2020), we extracted 1700 samples and filtered 26 significant variables. Seven machine learning algorithms were evaluated, with CatBoost emerging as the most effective. SHAP (SHapley Additive exPlanations) analysis revealed that work-related variables were the most strongly associated with predictions, accounting for 30% of the predictive power, followed by other factors such as demographic and education. Notably, we found that commute time and working hours exceeding 50 min/hours were negatively associated with marriage likelihood, while job satisfactions showed a non-linear relationship with marriage decision. The findings highlight the determinant of work-life balance in marriage decision and the complexity and nonlinear relationship in social decision-making. The objective of this study is to provide scientific data support for policy makers in an era of declining marriage rates in China. This study not only reveals the key factors affecting marriage decision but also provides critical evidence-based support for policymakers to prioritize resource allocation and formulate targeted policies amid declining marriage rates in China.

ABSTRACT Following the Green Revolution, development narratives have prioritized capital-reliant, input-intensive crop production among smallholders in the Global South. With the right incentives, smallholders will supposedly intensify production by aligning their social relations, economic decisions, and labor with state strategies to optimize inputs and outputs. Yet in the Philippines, experience with input-intensive rice production shows that rising costs, soil degradation, poor prices, and extreme weather have stalled output, forcing many smallholders to reconsider rice farming. These pressures have made the Philippines the world’s largest rice importer, prompting renewed state policies to push for self-sufficiency through new yield-intensification programs. Such policies presume that smallholders, given access to modern inputs and capital, will maximize yields. Drawing on qualitative fieldwork with smallholder farmers in Narra, Palawan, this paper examines how these assumptions overlook smallholder adaptation and diversified production within informal economies. Rather than pursuing profit or yield maximization alone, smallholders navigate rice production as part of broader livelihood strategies. They construct coping economies captured by the Filipino term hanapbuhay: securing a living by balancing constraints, diversifying income, and spreading risk. Embedding policy in these everyday realities enables a more grounded approach that moves beyond capital-intensive, yield-centric models toward resilience, equity, and food security.

ABSTRACT People may infer others' social class based on signals such as clothing, accents, university attended, or leisure activities. In recruitment and selection, such incidental social class signaling by candidates may occur, as information like this is often shared by candidates in application materials. Whether or how the visibility of candidates' social class information influences candidate evaluations, however, remains poorly understood. Building on signaling theory, we propose that social class level influence hiring decisions most when they contain job‐relevant content, particularly in roles that directly influence an organization's image to external stakeholders. We followed a three‐step, multi‐study approach: (1) developing stimulus materials in the form of interview response transcripts; (2) conducting two validation studies ( N = 100 and N = 108, respectively), and (3) conducting a pre‐registered online vignette main study ( N = 226). Results reveal that candidates with higher‐class signals were favored only when the class signals conveyed job‐relevant content. This class effect was most pronounced in roles that directly influence an organization's image, operationalized as high customer contact positions. Moreover, our findings show that perceptions of job fit, rather than cultural fit with the organization, drove these biased hiring evaluations. This study enhances our understanding of job‐level variations of social class bias in recruitment and underscores the importance of aligning job requirements closely with the role‐relevant core competencies to foster more equitable hiring practices.

Recent advances in deep brain stimulation (DBS) of the subcallosal cingulate (SCC) show promise in mitigating the symptoms of treatment-resistant depression (TRD) in humans1–3. Monoamines, such as dopamine and serotonin, mediate the effects of pharmacological treatments of depression. However, their roles in recovery following DBS remain elusive, largely due to technical limitations of measuring these neurotransmitters in the living human brain. Here, by leveraging machine learning-enhanced electrochemistry4–7, we show that dopamine and serotonin signaling following DBS to the SCC predicted later depressive symptom relief in humans with TRD. We found that both dopamine and serotonin levels increased following subtherapeutic intraoperative SCC stimulation, with each neurotransmitter showing selective responses to distinct decision-making tasks. Furthermore, acute dopamine increases predicted later mood improvements during a social decision-making task, while serotonin enhancement predicted faster responses during a non-social learning task longitudinally. Critically, changes in dopamine and serotonin levels during the social decision-making task jointly predicted depressive symptom remission at 6-month follow-up. These findings illustrate the contribution of both dopamine and serotonin signaling in predicting behavioral improvement and depressive symptom remission in humans with TRD. Such neurochemical plasticity may serve as potential mechanistic biomarkers for SCC DBS mechanism and TRD treatment response.Significance statementDopamine and serotonin levels increased following acute DBS to the SCC in humans.Acute dopamine and serotonin changes predicted later mood and response speed changes.Sustained TRD recovery was predicted by acute increases in both dopamine and serotonin estimates.

Whose agent are you? Relational norms shape expectation from algorithmic and human advisors in social decisions

Abstract Social connection is a critical determinant of health, yet loneliness and social isolation remain pervasive challenges among older adults. Social prescribing—an evidence-based approach to matching individuals with social interventions tailored to their needs—holds promise in bridging the gap between healthcare and community-based services. However, there is a lack of structured, cross-sector tools to support this process. This presentation describes the initial steps of a community-engaged initiative to adapt and implement a Social Prescribing Decision Support Tool for use across diverse settings, including senior centers, health systems, and community-based organizations. Guided by a social-ecological conceptual framework, we plan to use qualitative methods and implementation science to adapt an existing Social Prescribing Decision Support Tool through an iterative process with feedback from older adults, community program staff, healthcare providers, and national experts. The resulting tool will account for intake and referral workflows, as well as the capacity to implement social prescribing in each setting. It will facilitate structured assessment and individualized matching of older adults to social interventions that address key dimensions of social connection (structure, function, and quality). We will present preliminary findings from the adaptation process, discuss lessons learned from community partnerships, and outline plans for a pilot implementation in two community-based organizations. This work aims to advance community-engaged scholarship by co-designing scalable, pragmatic solutions that enhance social prescribing efforts, promote interdisciplinary collaboration, and improve social well-being outcomes for older adults.

Neural representation of trustworthiness encoding and inference in crowds.

Strengthening Methods and International Evidence on Health Inequality Aversion.

Real-life social interactions often unfold continuously and involve dynamic cooperation and competition, yet most studies rely on discrete games that do not capture the adaptive and graded nature of continuous sensorimotor decisions. To address this gap, we developed the Cooperation-Competition Foraging game—an ecologically grounded paradigm in which pairs of participants (dyads) navigate a continuous shared space under face-to-face visibility, deciding in real-time to collect rewarded targets either individually or jointly. Dyads (n = 58, 116 participants) spontaneously converged on distinct stable strategies along the cooperation-competition spectrum, forming three groups: cooperative, intermediate, and competitive. Despite the behavioral complexity, our computational model, which incorporated travel path minimization, sensorimotor communication, and recent choice history, predicted dyadic decisions with 87% accuracy, and linked prediction certainty with ensuing dynamics of spatiotemporal coordination. Further modeling revealed how sensorimotor factors, such as movement speed and skill, shape distinct strategies and payoffs. Crucially, we quantify the cost of cooperation, demonstrating that in many dyads prosocial tendencies outweigh the individual benefits of exploiting skill advantages. Our versatile framework provides a predictive, mechanistic account of how social and embodied drivers promote the emergence of dynamic cooperation and competition, and offers rigorous metrics for investigating the neural basis of naturalistic social interactions, and for linking personality traits to distinct strategies.

Maternal diet and metabolic conditions, such as obesity and diabetes, are associated with consequences for offspring brain health, including effects on behaviour and an increased risk for neurodevelopmental disorders (NDDs). The extent and sequence of neuroanatomical changes to offspring brain development produced by dietary conditions have not yet been reported. In this study, we used a mouse model of parental high-fat or high-fat/high-sugar diet consumption to examine its effects on offspring brain development using longitudinal magnetic resonance imaging. We demonstrated that exposure to these parental diets through gestation and lactation resulted in offspring brain structure changes. Different temporal patterns of change were observed: some structures exhibited volume differences already at postnatal day 3; some of these early appearing changes diminished early in development while others were still present in adulthood; other structure changes were found to emerge later in the pubertal period. Brain changes in adulthood were present despite switching to a healthy diet at weaning. Brain regions impacted included the cingulate cortex, subregions of the hippocampus, and the orbitofrontal cortex, regions previously reported as affected in human NDD populations, with functional roles in reward processing and social cognition, memory, and decision making, respectively. Affected cortical regions, including the cingulate and orbitofrontal cortex, were found to be increased in volume relative to the whole brain, while affected subcortical regions were largely decreased in volume. Our data provide new insights into the long-term neuroanatomical impact of parental diets and how those diets may impact NDD risk. Future studies could use this model to evaluate preventative or ameliorative measures.