Social information use is widespread in the animal kingdom, helping individuals rapidly acquire useful knowledge and adjust to novel circumstances. In humans, the highly interconnected world provides ample opportunities to benefit from social information but also requires navigating complex social environments with people holding disparate or conflicting views. It is, however, still largely unclear how people integrate information from multiple social sources that (dis)agree with them, and among each other. We address this issue in three steps. First, we present a judgement task in which participants could adjust their judgements after observing the judgements of three peers. We experimentally varied the distribution of this social information, systematically manipulating its variance (extent of agreement among peers) and its skewness (peer judgements clustering either near or far from the participant's judgement). As expected, higher variance among peers reduced their impact on behaviour. Importantly, observing a single peer confirming a participant's own judgement markedly decreased the influence of other—more distant—peers. Second, we develop a framework for modelling the cognitive processes underlying the integration of disparate social information, combining Bayesian updating with simple heuristics. Our model accurately accounts for observed adjustment strategies and reveals that people particularly heed social information that confirms personal judgements. Moreover, the model exposes strong inter-individual differences in strategy use. Third, using simulations, we explore the possible implications of the observed strategies for belief updating. These simulations show how confirmation-based weighting can hamper the influence of disparate social information, exacerbate filter bubble effects and deepen group polarization. Overall, our results clarify what aspects of the social environment are, and are not, conducive to changing people's minds.
Our increasingly interconnected world provides virtually unlimited opportunities to observe the behavior of others. This affords abundant useful information but also requires navigating complex social environments with people holding disparate or conflicting views. It is, however, still largely unclear how people integrate information from multiple social sources that (dis)agree with them, and among each other. We address this issue in three steps. First, we present a judgment task in which participants could adjust their judgments after observing the judgments of three peers. We experimentally varied the distribution of this social information, systematically manipulating its variance (extent of agreement among peers) and its skewness (peer judgments clustering either near or far from the participant’s). As expected, higher variance among peers reduced their impact on behavior. Importantly, observing a single peer confirming an individual’s judgment markedly decreased the influence of other—more distant—peers. Second, we develop a framework for modelling the cognitive processes underlying the integration of disparate social information, combining Bayesian updating with simple heuristics. Our model accurately accounts for observed adjustment strategies and reveals that people particularly heed social information that confirms personal judgments. Moreover, the model exposes strong inter-individual differences in strategy use. Third, using simulations, we explore the possible implications of identified strategies for belief updating more broadly. They show how confirmation effects can hamper the influence of disparate social information, exacerbate filter bubble effects and worsen group polarization. Overall, our results clarify what aspects of the social environment are, and are not, conducive to changing people’s minds.
Social learning can help individuals to efficiently acquire knowledge and skills. In the classroom, social learning often takes place in structured settings in which peers help, support, and tutor each other. Several protocols have been developed to make peer-assisted learning (PAL) more efficient. However, little attention has been devoted to how the transfer of knowledge is shaped by the social relationship between peers, and their relative positions in the social network. To address this gap, we combined social network analysis with an experimental social learning task, in which pupils (N = 135; aged 11-19) could use social information from their peers to improve their performance. We show that pupils' tendencies to use social information substantially decrease with the peer's distance in the social network. This effect is mediated by subjective closeness: pupils report feeling much closer to their friends than to their non-friends, and closeness strongly enhances social learning. Our results further show that, above and beyond these effects of network distance, social information use increases with the peer's social status (network centrality) and perceived smartness. Our results provide empirical evidence in a naturalistic setting for the role of specific network attributes in shaping pupils' willingness to learn from their peers. These findings illustrate the value of a social network approach for understanding knowledge transfer in the classroom and can be used to structure more effective peer learning. Impact and ImplicationsThis study shows how social network analyses can help teachers and practitioners boost learning outcomes. Social network data can inform matching of pupils for peer learning, and can help cultivating social cohesion in the classroom. Collecting social network data is a fast and cheap procedure, and may be easily incorporated in existing protocols aimed at promoting peer learning, knowledge transfer and positive school culture.
Humans live in a fundamentally social world. The behavior and judgments from friends, colleagues, television hosts and social media feeds drive our purchase decisions, induce lifestyle changes, and determine voting preferences. People tend to be more sensitive to social information when they are unsure about their own beliefs, and assign more weight to social information when its source is highly confident. However, little is known about the relative impact of the confidence of self and others on social information use, and how they jointly shape social transmission. Here we show with two incentivized decision-making experiments that the confidence of others had a substantially larger impact on social information use than people’s own confidence. In tasks involving perceptual decisions (experiment 1; N=203) and US election predictions (experiment 2; N=213), participants could adjust their initial judgments upon observing judgments of others and were rewarded for accuracy. Adjustments were most strongly impacted by the confidence of others, particularly when participants’ own confidence was low. Furthermore, confidence also affected adjustment heuristics: confident others prompted participants to compromise more often, rather than to stick with their initial judgments. Our results highlight how giving more weight on the confidence of others can be a double-edged sword: it can accelerate learning when confidence is related to accuracy, but it also leaves people vulnerable to sources who confidently share misinformation.
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