Familial transmission of mental illnesses and health behaviors is well established. However, little research has examined familial transmission of mental health help-seeking behaviors despite social science theoretical traditions that support its occurrence including social learning theory and the network episode model. Among parent-adolescent dyads, extant literature supports consideration of adolescent-autonomy versus parent-gatekeeping according to whether or not parents recognize a mental health problem in their adolescent. Given this, we examined familial transmission of self-reported mental health help-seeking among parent-adolescent dyads over an 18-month period from a school-based study (N = 422; Texas, USA). Generalized estimating equations tested the effect of multiple forms of parent help-seeking on similar forms of adolescent help-seeking, controlling for personal/family characteristics. We also examined interaction by parent recognition of a mental health problem in their adolescent to discern unique intergenerational processes across these subgroups of parent-adolescent dyads. Owing to effect modification by parent problem recognition (p<0.01), two unique familial transmission of help-seeking pathways emerged. When parent problem recognition was present, parent self help-seeking history reduced adolescent help-seeking net of controls. In contrast, when parent problem recognition was absent, parent self help-seeking history increased adolescent help-seeking net of controls. Our findings provide evidence of familial transmission of mental health help-seeking behaviors, but the direction of influence fundamentally depends on parent recognition of a mental health problem in their adolescent in order to reveal intergenerationally transmitted processes. The findings support our hypotheses that familial transmission of help-seeking starts early in adolescence and is likely influenced by parent modeling and gatekeeping, though explanations for the patterns observed, such as short- and long-term positive and negative mixed impacts of past help-seeking experiences of parents, require further study to ascertain.

Social learning is learning from the observation of how others interact with the environment. However, in nature, individuals often need to process serial social information and may favour either the most recent information (recency bias), constantly updating knowledge to match the environment, or the information that appeared first in the series (primacy bias), which may slow down adjustment to environmental change. Mate-copying is a widespread form of social learning in a mate choice context related to conformity in mate choice, and where a naive individual develops a preference for a given mate (or mate phenotype) seen being chosen by conspecifics. Mate-copying is documented in most vertebrate taxa and in the fruit fly Drosophila melanogaster. Here, we tested experimentally whether female fruit flies show a primacy or a recency bias by presenting pictures of a female copulating with one of two contrastingly coloured male phenotypes. We found that after two sequential contradictory demonstrations, females show a tendency to prefer males of the phenotype preferred in the first demonstration, suggesting that mate-copying in D. melanogaster is not based on the most recently observed mating and may be influenced by a form of primacy bias.

Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. Recent studies using coupled behavior-disease models assumed homogeneous populations. However, heterogeneity in population, such as different social groups, cannot be ignored. In this study, we divided the population into social media users and non-users, and investigated the impact of homophily (the tendency for individuals to associate with others similar to themselves) and online events on disease dynamics. Our results reveal that homophily hinders the adoption of vaccinating strategies, hastening the approach to a tipping point after which the population converges to an endemic equilibrium with no vaccine uptake. Furthermore, we find that online events can significantly influence disease dynamics, with early discussions on social media platforms serving as an early warning signal of potential disease outbreaks. Our model provides insights into the mechanisms underlying these phenomena and underscores the importance of considering homophily in disease modeling and public health strategies.

BACKGROUND: Human learning unfolds under uncertainty. Uncertainty is heterogeneous with different forms exerting distinct influences on learning. While one can be uncertain about what to do to maximize rewarding outcomes, known as policy uncertainty, one can also be uncertain about general world knowledge, known as epistemic uncertainty. In complex and naturalistic environments such as the social world, adaptive learning may hinge on striking a balance between attending to and resolving each type of uncertainty. Prior work illustrates that people with anxiety-those with increased threat and uncertainty sensitivity-learn less from aversive outcomes, particularly as outcomes become more uncertain. How does a learner adaptively trade-off between attending to these distinct sources of uncertainty to successfully learn about their social environment?
METHODS: We developed a novel eye-tracking method to capture highly granular estimates of policy and epistemic uncertainty based on gaze patterns and pupil diameter (a physiological estimate of arousal) RESULTS: These empirically derived uncertainty measures reveal that humans (N = 94) flexibly switch between resolving policy and epistemic uncertainty to adaptively learn about which individuals can be trusted and which should be avoided. However, those with increased anxiety (N = 49) do not flexibly switch between resolving policy and epistemic uncertainty, and instead express less uncertainty overall CONCLUSIONS: Combining modeling and eye-tracking techniques, we show that altered learning in people with anxiety emerges from an insensitivity to policy uncertainty and rigid choice policies, leading to maladaptive behaviors with untrustworthy people.

Bees have been excellent model systems to study social learning - the ability of animals to change their behaviour based on observations of other individuals. Researchers have investigated several aspects of social learning in bees, including how it can lead to cultural traditions. A recent study also argues that bees have the capacity to socially learn behaviours that they could not innovate on their own. To understand these findings better, I review what we know about the mechanisms underlying social learning in bees and use these findings to compare social learning and culture in bees and humans. The findings suggest that the seemingly complex social behaviours of bees could arise from simple mechanisms underlying learning in general. I highlight the importance of investigating cognitive mechanisms and how they might differ across animals.

Social learning is a mechanism used by many species to efficiently gain information about their environment. Although many animals live in an environment where members of other species are present, little is known about interspecific social learning. Domesticated and urbanized species provide the opportunity to investigate whether nonhuman animals can learn from heterospecifics such as humans, who are integral parts of their social landscape. Although domestic dogs Canis familiaris have been intensively researched for their ability to learn from humans, most studies have focused on dogs living as pets. However, free-ranging dogs represent the majority of the world\'s dog population, they live alongside humans, scavenge on human refuse, and are subject to natural and sexual selection. Thus, free-ranging dogs with extensive exposure to humans and their artifacts provide the opportunity to investigate interspecific social learning in a naturalistic setting, where learning from humans might be a benefit for them. Here we tested individual free-ranging dogs in a between-subject design: Dogs in the control group could spontaneously choose between two novel and differently patterned food-delivering boxes. In the experimental group, instead, dogs could first observe an unfamiliar human approaching and eating from 1 of the 2 boxes. We provide the first evidence that free-ranging dogs match the choice of an unfamiliar human. These results show that at least simple forms of interspecific social learning might be involved in dogs\' success in living alongside humans in a complex urbanized environment.

For a reinforcement learning agent to finish trial-and-error in a realistic time duration, it is necessary to limit the scope of exploration during the learning process. However, limiting the exploration scope means limitation in optimality: the agent could fall into a suboptimal solution. This is the nature of local, bottom-up way of learning. An alternative way is to set a goal to be achieved, which is a more global, top-down way. The risk-sensitive satisficing (RS) value function incorporate, as a method of the latter way, the satisficing principle into reinforcement learning and enables agents to quickly converge to exploiting the optimal solution without falling into a suboptimal one, when an appropriate goal (aspiration level) is given. However, how best to determine the aspiration level is still an open problem. This study proposes social satisficing, a framework for multi-agent reinforcement learning which determines the aspiration level through information sharing among multiple agents. In order to verify the effectiveness of this novel method, we conducted simulations in a learning environment with many suboptimal goals (SuboptimaWorld). The results show that the proposed method, which converts the aspiration level at the episodic level into local (state-wise) aspiration levels, possesses a higher learning efficiency than any of the compared methods, and that the novel method has the ability to autonomously adjust exploration scope, while keeping the shared information minimal. This study provides a glimpse into an aspect of human and biological sociality which has been mentioned little in the context of artificial intelligence and machine learning.

Humans learn both directly, from own experience, and via social communication, from the experience of others. They also often integrate these two sources of knowledge to make predictions and choices. We hypothesized that when faced with the need to integrate communicated information into personal experience, people would represent the average of experienced exemplars with greater accuracy. In two experiments, Mturk users estimated the mean of consecutively and rapidly presented number sequences that represented bonuses ostensibly paid by different providers on a crowdsource platform. Participants who expected integrating these values with verbal information about possible change in bonuses were more accurate in extracting the means of the values compared to participants who did not have such expectation. While our study focused on socially communicated information, the observed effect may potentially extend to other forms of information integration. We suggest that expected integration of experience with additional information facilitates an abstract representation of personal experiences.

Learning theories provide explanations for domestic violence; they can also aid in our understanding of the relation between contact sports and domestic violence. Notably, language used during sporting events and athletes\' behaviors being rewarded both on and off the field can influence the behaviors of viewers. Adubato (2016) found a statistically significant relation between an increase in domestic violence arrests and the kick-off times of Philadelphia Eagles football games. Here, we replicate and extend this work to Chicago and Boston. We examine quantitative, hourly data from the Chicago and Boston Police Departments and compare mean domestic violence arrests in an 8-hour period from the average Bears\' and Patriots\' (football) kick-off times, respectively, to the same time period on non-football Sundays, major holidays, and days of rival Blackhawks\' and Bruins\' (hockey) games, respectively. Results show a significant difference in average domestic violence arrests between hockey games and non-football Sundays in Chicago only. This work highlights the importance of context in examining domestic violence nationally and has implications for domestic violence policies in professional sports\' organizations.

BACKGROUND: Public health education (PHE) in social environments plays a crucial role in mitigating the impact of public health events, especially with the recent surge in global incidents. Social learning theory (SLT) provides a strong theoretical foundation for implementing PHE. The objective of this study is to conduct a systematic scoping review of PHE using SLT, synthesizing the target populations, types of research, main findings, and future directions.
METHODS: The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review (PRISMA-ScR) guidelines. We conducted a comprehensive search of five electronic databases (Web of Science, Scopus, PubMed, ProQuest, and APA PsycInfo) for English articles related to PHE using SLT. Two reviewers independently screened the titles and abstracts. Descriptive statistics were utilized to analyze the characteristics of the articles included in the study, followed by a comprehensive narrative analysis of the results.
RESULTS: Research on PHE using SLT mainly focuses on adolescents, students, special patients, and vulnerable populations. The study sample includes seven research types and nine commonly used experimental methods. Four modes of PHE using SLT are identified, along with four types of summarized research results.
CONCLUSIONS: PHE research based on SLT can be prioritized for preventing widespread infectious diseases, spreading fundamental public health information, and assisting patients with particular illnesses. To enhance the implementation of PHE, researchers and policymakers should integrate online and offline health education resources, ensure the accessibility of up-to-date information, and leverage digital technologies in PHE. More highly interactive and participatory health education courses will be established in social learning environments to encourage public participation in PHE.