Algorithm-mediated social learning in online social networks.

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or 'PRIME' information) to sustain users' attention and maximize engagement. Here, we synthesize emerging insights into 'algorithm-mediated social learning' and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human-algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

Inferences about interdependence shape cooperation.

During social interactions in daily life, people possess imperfect knowledge of their interdependence (i.e., how behaviors affect each person's outcomes), and what people infer about their interdependence can shape their behaviors. We review theory and research that suggests people can infer their interdependence with others along several dimensions, including mutual dependence, power, and corresponding-versus-conflicting interests. We discuss how perceptions of interdependence affect how people cooperate and punish others' defection in everyday life. We propose that people understand their interdependence with others through knowledge of the action space, cues during social interactions (e.g., partner behaviors), and priors based on experience. Finally, we describe how learning interdependence could occur through domain-specific and domain-general mechanisms.

Learning from Ingroup Experiences Changes Intergroup Impressions.

Humans form impressions toward individuals of their own social groups (ingroup members) and of different social groups (outgroup members). Outgroup-focused theories predict that intergroup impressions are mainly shaped by experiences with outgroup individuals, while ingroup-focused theories predict that ingroup experiences play a dominant role. Here we test predictions from these two psychological theories by estimating how intergroup impressions are dynamically shaped when people learn from both ingroup and outgroup experiences. While undergoing fMRI, male participants had identical experiences with different ingroup or outgroup members and rated their social closeness and impressions toward the ingroup and the outgroup. Behavioral results showed an initial ingroup bias in impression ratings which was significantly reduced over the course of learning, with larger effects in individuals with stronger ingroup identification. Computational learning models revealed that these changes in intergroup impressions were predicted by the weight given to ingroup prediction errors. Neurally, the individual weight for ingroup prediction errors was related to the coupling between the left inferior parietal lobule and the left anterior insula, which, in turn, predicted learning-related changes in intergroup impressions. Our findings provide computational and neural evidence for ingroup-focused theories, highlighting the importance of ingroup experiences in shaping social impressions in intergroup settings.Significance Statement:Living in multicultural societies, humans interact with individuals of their own social groups (ingroup members) and of different social groups (outgroup members). However, little is known about how people learn from the mixture of ingroup and outgroup interactions, the most natural experiences in current societies. Here, participants had identical, intermixed experiences with different ingroup and outgroup individuals and rated their closeness and impressions toward the ingroup and the outgroup. Combining computational models and fMRI, we find that the weight given to ingroup experiences (ingroup prediction errors) is the main source of intergroup impression change, captured by changes in connectivity between the parietal lobe and insula. These findings highlight the importance of ingroup experiences in shaping intergroup impressions in complex social environments.

Alcohol Use Disorder Displays Trait-Related Reductions in Prosocial Decision Making.

BACKGROUND: Alcohol use disorder (AUD) is associated with deficits in social cognition, but the relationship between harmful alcohol use and the processes underlying interactive social behavior is still unknown. We hypothesized that prosocial decision making is reduced in AUD and that individual differences in the underlying processes are key to better understanding these reductions. METHODS: In one laboratory study (Swedish participants, n = 240) and one confirmatory online study (American participants, n = 260), we compared young adults with AUD with age-, gender-, and education-matched healthy control subjects on 6 facets of prosocial decision making. We used standardized behavioral economic tasks, namely the dictator game, ultimatum game, trust game, and third-party game. To better understand the expected differences in prosociality, we evaluated attention by tracking eye gaze, decision response time, clinical symptoms, and social cognition. RESULTS: Altruism (lab study: p = .007; online study: p < .001), fairness (lab study: p = .003; online study: p = .007), and reciprocal trust (lab study: p = .007; online study: p = .039) were reduced in individuals with AUD compared with healthy control subjects, whereas trust and third-party punishment and compensation were comparable in both studies. Reduced prosociality was associated with attending to the selfish response option, faster response time, and moral attitudes, while being dissociated from both psychiatric symptoms and drinking history in AUD. CONCLUSIONS: Individuals with AUD have trait-related reductions in prosocial decision making that do not vary with drinking history or psychiatric symptom load. These reductions were confined to one-to-one interactions accompanied by differences in attention, decision time, and moral attitudes.

Borrelia Ocular Infection: A Case Report and a Systematic Review of Published Cases.

INTRODUCTION: Lyme borreliosis can cause many diverse manifestations, also ocular disease where the diagnosis of ocular borreliosis is challenging. The primary aim was to report on the evidence of Borrelia spirochetes in the ocular tissue in presumed ocular borreliosis. METHODS: A systematic review of pathological eye conditions was performed where Borrelia has been suspected in relevant ocular tissue, together with a case report of diagnosed uveitis with polymerase chain reaction (PCR)-confirmed Borrelia afzelii in the vitreous. The evidence for clinical and laboratory diagnosis was evaluated systematically. As a secondary aim, the treatment of ocular Borrelia infection was also evaluated for confirmed cases. RESULTS: Thirteen includable studies were found, and after the removal of case duplicates, eleven unique cases were extracted. Apart from the present case report, 4 other cases reported strong evidence for the detection of B. spirochetes in ocular tissue. Four cases presented reasonable evidence for assumed detected Borrelia, while three additional cases showed only weak diagnostic credibility that Borrelia was detected. CONCLUSION: This systematic review, including all reported cases and our case report, supports evidence of ocular infection of Borrelia species. Furthermore, in case of suspicion of infection and seronegativity, it is justified to look for Borrelia in eye tissue samples. In addition, microscopy without using PCR is not sufficient to confirm the diagnosis of borreliosis on ocular tissue. In the articles studied, there was no unambiguous recommendation of treatment.

Partial Occlusion of a Third Nerve Palsy, a Shortcut through the Swedish Legal Vision Requirements for Driving.

In this report, we present a case of a young woman with a traumatic third nerve palsy. A substantial practical dilemma for her was that according to the Swedish vision requirements for driving she was not allowed to drive due to diplopia within central 30 degrees of gaze. If she occluded the paretic eye to avoid diplopia, she would need to constantly occlude the eye for an adaptation period of six months before she would be allowed to drive with monocular vision. To get by this problem, the eyeglass of the paretic eye was partially occluded. In her case, this eliminated all diplopia within the central 30 degrees of gaze while she was still able to have binocular vision for primary gaze and horizontal gaze. This solution was accepted by the Swedish Transport Agency and she was allowed to drive without an adaptation period of 6 months. To our knowledge, this is the first time this solution is used for driving in Sweden.

A computational reward learning account of social media engagement.

Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.

The neural and computational systems of social learning.

Learning the value of stimuli and actions from others - social learning - adaptively contributes to individual survival and plays a key role in cultural evolution. We review research across species targeting the neural and computational systems of social learning in both the aversive and appetitive domains. Social learning generally follows the same principles as self-experienced value-based learning, including computations of prediction errors and is implemented in brain circuits activated across task domains together with regions processing social information. We integrate neural and computational perspectives of social learning with an understanding of behaviour of varying complexity, from basic threat avoidance to complex social learning strategies and cultural phenomena.

Model-Based and Model-Free Social Cognition: Investigating the Role of Habit in Social Attitude Formation and Choice.

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions - computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each type of learning was expressed in both advisor choices and post-task self-reported liking of advisors. Specifically, participants preferred advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Although participants relied more heavily on model-based learning overall, they varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

Treatment of Urethral Pain Syndrome (UPS) in Sweden.

BACKGROUND: Urethral Pain Syndrome (UPS) in women is a recurrent urethral pain without any proven infection or other obvious pathology. There are few studies on UPS, and evidence-based treatment is lacking. The primary aim was to study what treatments are used, and to compare the treatment tradition of UPS in Sweden in 2018, with what was used in 2006. METHODS: A questionnaire on the treatment of women with UPS was sent to all public gynecology, urology, gynecologic oncology and venereology clinics, and one public general practice in each county in Sweden in 2018. Private practice clinics in gynecology responded to the survey in 2017. Comparisons were made with the same survey sent to gynecology and urology clinics in 2006. FINDINGS: Of 137 invited clinics in 2018, 99 (72.3%) responded to the survey. Seventy-seven (77.8%) of them saw women with UPS and 79.2% (61/77) of these clinics treated the patients using 19 different treatment methods. Local corticosteroids and local estrogens were the methods most used. Treatments were similar in gynecology and urology clinics in 2006 and 2018, although strong corticosteroids had increased in use in the treatment regimens of 2018. More than half of the clinics used antibiotics. INTERPRETATION: Since there is no evidence-based treatment of UPS, a wide spectrum of treatments is used, and different specialties use different treatment strategies. Despite the lack of proven infection, a large number of clinics also treated the syndrome with antibiotics. There is thus a need for well-designed randomized controlled clinical trials to find evidence-based treatments of UPS.

Social threat learning transfers to decision making in humans.

In today's world, mass-media and online social networks present us with unprecedented exposure to second-hand, vicarious experiences and thereby the chance of forming associations between previously innocuous events (e.g., being in a subway station) and aversive outcomes (e.g., footage or verbal reports from a violent terrorist attack) without direct experience. Such social threat, or fear, learning can have dramatic consequences, as manifested in acute stress symptoms and maladaptive fears. However, most research has so far focused on socially acquired threat responses that are expressed as increased arousal rather than active behavior. In three experiments (n = 120), we examined the effect of indirect experiences on behaviors by establishing a link between social threat learning and instrumental decision making. We contrasted learning from direct experience (i.e., Pavlovian conditioning) (experiment 1) against two common forms of social threat learning-social observation (experiment 2) and verbal instruction (experiment 3)-and how this learning transferred to subsequent instrumental decision making using behavioral experiments and computational modeling. We found that both types of social threat learning transfer to decision making in a strong and surprisingly inflexible manner. Notably, computational modeling indicated that the transfer of observational and instructed threat learning involved different computational mechanisms. Our results demonstrate the strong influence of others' expressions of fear on one's own decisions and have important implications for understanding both healthy and pathological human behaviors resulting from the indirect exposure to threatening events.

Incidental ostracism emerges from simple learning mechanisms.

Ostracism, or social exclusion, is widespread and associated with a range of detrimental psychological and social outcomes. Ostracism is typically explained as instrumental punishment of free-riders or deviants. However, this instrumental account fails to explain many of the features of real-world ostracism, including its prevalence. Here we hypothesized that ostracism can emerge incidentally (non-instrumentally) when people choose partners in social interactions, and that this process is driven by simple learning mechanisms. We tested this hypothesis in four experiments (n = 456) with economic games in dynamic social networks. Contrary to the instrumental account of ostracism, we find that the targets of ostracism are not primarily free-riders. Instead, incidental initial variability in choosing partners for social interactions predicts later ostracism better than the instrumental account. Using computational modelling, we show that simple reinforcement learning mechanisms explain the incidental emergence of ostracism, and that they do so better than a formalization of the instrumental account. Finally, we leveraged these reinforcement learning mechanisms to experimentally reduce incidental ostracism. Our results demonstrate that ostracism is more incidental than previously assumed and can arise from basic forms of learning. They also show that the same mechanisms that result in incidental ostracism can help to reduce its emergence.

The role of a "common is moral" heuristic in the stability and change of moral norms.

Moral norms are fundamental for virtually all social interactions, including cooperation. Moral norms develop and change, but the mechanisms underlying when, and how, such changes occur are not well-described by theories of moral psychology. We tested, and confirmed, the hypothesis that the commonness of an observed behavior consistently influences its moral status, which we refer to as the common is moral (CIM) heuristic. In 9 experiments, we used an experimental model of dynamic social interaction that manipulated the commonness of altruistic and selfish behaviors to examine the change of peoples' moral judgments. We found that both altruistic and selfish behaviors were judged as more moral, and less deserving of punishment, when common than when rare, which could be explained by a classical formal model (social impact theory) of behavioral conformity. Furthermore, judgments of common versus rare behaviors were faster, indicating that they were computationally more efficient. Finally, we used agent-based computer simulations to investigate the endogenous population dynamics predicted to emerge if individuals use the CIM heuristic, and found that the CIM heuristic is sufficient for producing 2 hallmarks of real moral norms; stability and sudden changes. Our results demonstrate that commonness shapes our moral psychology through mechanisms similar to behavioral conformity with wide implications for understanding the stability and change of moral norms. (PsycINFO Database Record

A common neural network differentially mediates direct and social fear learning.

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders.

Co-Evolution of Social Learning and Evolutionary Preparedness in Dangerous Environments.

Danger is a fundamental aspect of the lives of most animals. Adaptive behavior therefore requires avoiding actions, objects, and environments associated with danger. Previous research has shown that humans and non-human animals can avoid such dangers through two types of behavioral adaptions, (i) genetic preparedness to avoid certain stimuli or actions, and (ii) social learning. These adaptive mechanisms reduce the fitness costs associated with danger but still allow flexible behavior. Despite the empirical prevalence and importance of both these mechanisms, it is unclear when they evolve and how they interact. We used evolutionary agent-based simulations, incorporating empirically based learning mechanisms, to clarify if preparedness and social learning typically both evolve in dangerous environments, and if these mechanisms generally interact synergistically or antagonistically. Our simulations showed that preparedness and social learning often co-evolve because they provide complimentary benefits: genetic preparedness reduced foraging efficiency, but resulted in a higher rate of survival in dangerous environments, while social learning generally came to dominate the population, especially when the environment was stochastic. However, even in this case, genetic preparedness reliably evolved. Broadly, our results indicate that the relationship between preparedness and social learning is important as it can result in trade-offs between behavioral flexibility and safety, which can lead to seemingly suboptimal behavior if the evolutionary environment of the organism is not taken into account.

Aversive Learning and Trait Aggression Influence Retaliatory Behavior.

In two experiments (n = 35, n = 34), we used a modified fear-conditioning paradigm to investigate the role of aversive learning in retaliatory behavior in social context. Participants first completed an initial aversive learning phase in which the pairing of a neutral conditioned stimulus (CS; i.e., neutral face) with a naturally aversive unconditioned stimulus (US; electric shock) was learned. Then they were given an opportunity to interact (i.e., administer 0-2 shocks) with the same faces again, during a Test phase. In Experiment 2, we used the same paradigm with the addition of online trial-by-trial ratings (e.g., US expectancy and anger) to examine the role of aversive learning, anger, and the learned expectancy of receiving punishment more closely. Our results indicate that learned aversions influenced future retaliation in a social context. In both experiments, participants showed largest skin conductance responses (SCRs) to the faces paired with one or two shocks, demonstrating successful aversive learning. Importantly, participants administered more shocks to the faces paired with the most number of shocks when the opportunity was given during test. Also, our results revealed that aggressive traits (Buss and Perry Aggression scale) were associated with retaliation only toward CSs associated with aversive experiences. These two experiments show that aggressive traits, when paired with aversive learning experiences enhance the likelihood to act anti-socially toward others.

A clash of values: Fear-relevant stimuli can enhance or corrupt adaptive behavior through competition between Pavlovian and instrumental valuation systems.

Humans and nonhuman primates preferentially learn to fear and avoid archetypical fear-relevant stimuli. Yet how these learning biases influence adaptive behavior, the basic mechanistic underpinnings of these biases, and how they interact with learning experiences during the life span of an individual remain unknown. To study this, we investigated how 4 classes of fear-relevant stimuli (snakes, threatening in-group faces, racial out-group faces, and guns) influenced adaptive behavior. We showed that stimulus-driven biases have a dramatic influence that can either promote or corrupt adaptive behavior depending on how a bias relates to the environment. We quantified and compared the effects of different fear-relevant stimuli on instrumental behavior using a computational reinforcement learning model that formalized the idea that the bias reflects competition between an instrumental and a Pavlovian valuation system. These results were further clarified by 2 independent rating studies showing that perceived danger of the stimuli corresponded well with their influence on adaptive behavior.

Mechanisms of social avoidance learning can explain the emergence of adaptive and arbitrary behavioral traditions in humans.

Many nonhuman animals preferentially copy the actions of others when the environment contains predation risk or other types of danger. In humans, the role of social learning in avoidance of danger is still unknown, despite the fundamental importance of social learning for complex social behaviors. Critically, many social behaviors, such as cooperation and adherence to religious taboos, are maintained by threat of punishment. However, the psychological mechanisms allowing threat of punishment to generate such behaviors, even when actual punishment is rare or absent, are largely unknown. To address this, we used both computer simulations and behavioral experiments. First, we constructed a model where simulated agents interacted under threat of punishment and showed that mechanisms' (a) tendency to copy the actions of others through social learning, together with (b) the rewarding properties of avoiding a threatening punishment, could explain the emergence, maintenance, and transmission of large-scale behavioral traditions, both when punishment is common and when it is rare or nonexistent. To provide empirical support for our model, including the 2 mechanisms, we conducted 4 experiments, showing that humans, if threatened with punishment, are exceptionally prone to copy and transmit the behavior observed in others. Our results show that humans, similar to many nonhuman animals, use social learning if the environment is perceived as dangerous. We provide a novel psychological and computational basis for a range of human behaviors characterized by the threat of punishment, such as the adherence to cultural norms and religious taboos.