Norm compliance affects perceptual decisions through modulation of a starting point bias

Kurvers RHJ. A Cognitive Computational Approach to Social and Collective Decision-Making

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

Earlier social information has a stronger influence on judgments

People's decisions are often informed by the choices of others. Evidence accumulation models provide a mechanistic account of how such social information enters the choice process. Previous research taking this approach has suggested two fundamentally different cognitive mechanisms by which people incorporate social information. On the one hand, individuals may update their evidence level instantaneously when observing social information. On the other hand, they may gradually integrate social information over time. These accounts make different predictions on how the timing of social information impacts its influence. The former predicts that timing has no impact on social information uptake. The latter predicts that social information which arrives earlier has a stronger impact because its impact increases over time. We tested both predictions in two studies in which participants first observed a perceptual stimulus. They then entered a deliberation phase in which social information arrived either early or late before reporting their judgment. In Experiment 1, early social information remained visible until the end and was thus displayed for longer than late social information. In Experiment 2, which was preregistered, early and late social information were displayed for an equal duration. In both studies, early social information had a larger impact on individuals' judgments. Further, an evidence accumulation analysis found that social information integration was best explained by both an immediate update of evidence and continuous integration over time. Because in social systems, timing plays a key role (e.g., propagation of information in social networks), our findings inform theories explaining the temporal evolution of social impact and the emergent social dynamics.

Social norms shape visual appearance: Taking a closer look at the link between social norm learning and perceptual decision-making

One of the most fundamental questions in social psychology is whether norms can change individuals' minds by shaping the visual appearance of stimuli. This question was first raised by Muzafer Sherif (1935). Drawing on the extended social reinforcement account (Germar and Mojzisch, 2019), we aimed to provide a rigorous test of the hypothesis that norm learning leads to a persistent perceptual bias and, hence, to a change in the visual appearance of stimuli. From a methodological perspective, we used both a diffusion model approach and the method of adjustment, a well-established technique from psychophysics and vision research. The results of Experiments 1-3 show that norm effects on perceptual decision-making are robustly replicable, and are due to genuine social influence, that is, they cannot be explained by non-social priming, contingency learning effects (Experiments 1 and 2) or anchoring effects (Experiment 3). Most importantly, by using a psychophysical approach, Experiment 4 shows, for the first time, that social norm learning alters individuals' point of subjective equality and, hence, the visual appearance of stimuli.

Can monetary incentives overturn fairness-based decisions?

Fairness norms and resulting behaviours are an important prerequisite for cooperation in human societies. At the same time, financial incentives are commonly used to motivate social behaviours, yet it remains unclear how financial incentives affect fairness-based behaviours. Combining a decision paradigm from behavioural economics with hierarchical drift-diffusion modelling, we investigated the effect of different financial incentives on two types of fairness-based decisions in four experimental groups. In two groups, participants divided points between themselves and a disadvantaged person, inciting fairness-based compensation behaviour, in two other groups they divided points between themselves and a fairness violator, inciting fairness-based punishment behaviour. In addition, each group received financial incentives that were either aligned or in conflict with the respective fairness-based behaviour. This design allowed us to directly investigate how different incentives shape the cognitive mechanism of fairness-based decisions and whether these effects are comparable across different fairness domains (fairness-based punishment versus fairness-based compensation). Results showed that offering conflicting incentives diminished fairness-congruent decision behaviour and rendered the fairness-congruent decision process less efficient. These findings demonstrate that financial incentives can undermine fairness-based behaviour, and thus are relevant for the development of incentive schemes aimed at fostering cooperative behaviour.

The neural computation of human prosocial choices in complex motivational states

Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants' choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors.

Collective rule-breaking

Rules form an important part of our everyday lives. Here we explore the role of social influence in rule-breaking. In particular, we identify some of the cognitive mechanisms underlying rule-breaking and propose approaches for how they can be scaled up to the level of groups or crowds to better understand the emergence of collective rule-breaking. Social contagion plays an important role in such processes and different dynamics such as linear or rapid nonlinear spreading can have important consequences for interventions in rule-breaking. A closer integration of cognitive psychology, microsociology and mathematical modelling will be key to a deeper understanding of collective rule-breaking to turn this field of research into a predictive science.

Dangerous liaisons: an online experiment on the role of scientific experts and politicians in ensuring public support for anti-COVID measures

The effectiveness of public health measures to prevent COVID-19 contagion has required less vulnerable citizens to pay an individual cost in terms of personal liberty infringement to protect more vulnerable groups. However, the close relationship between scientific experts and politicians in providing information on COVID-19 measures makes it difficult to understand which communication source was more effective in increasing pro-social behaviour. Here, we present an online experiment performed in May 2020, during the first wave of the pandemic on 1131 adult residents in Lombardy, Italy, one of the world's hardest hit regions. Results showed that when scientific experts recommended anti-contagion measures, participants were more sensitive to pro-social motivations, unlike whenever these measures were recommended by politicians and scientific experts together. Our findings suggest the importance of trusted sources in public communication during a pandemic.

How do Muslim community members perceive Covid-19 risk reduction recommendations - a UK qualitative study?

Introduction

The evidence is now unequivocal that people from Black and Minority Ethnic Backgrounds (BAME) living in the UK are disproportionately affected by covid-19. There is growing evidence that the reasons for this difference are multi-factorial and need further exploration.

Aim

The aim of this study was to understand better, perceptions of risk and responses to covid-19 of members of the Muslim community living in the North West of England, and to understand the facilitators and barriers to adherence to restrictions and guidance measures.

Method

A total of 47 participants took part in 25 in-depth qualitative interviews and four focus groups (n=22) that explored perceptions of risk and responses to risk from covid-19. Data were analysed thematically.

Findings

Participants were aware of the mechanism of transmission of covid-19 and took steps to mitigate risk of transmission including, observing a range of hygiene practices and following social distancing guidance. Increased risk of covid-19 for BAME populations was explained largely in terms of exposure to the virus due to the types of employment people from BAME populations are employed in. Limitations both within the working environment and more generally in public spaces, was identified as problematic for effective social distancing. The closure of mosques sent out a strong message about the seriousness of the virus and religious teachings reinforced hygiene and social distancing guidelines.

Conclusion

Across society there are people that adhere to restrictions and guidelines and those that do not. Improving local information provision and communication pathways during times of the pandemic, could aid understanding of risk and promote adherence to social distancing restrictions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10506-4.

Wise or mad crowds? The cognitive mechanisms underlying information cascades

Whether getting vaccinated, buying stocks, or crossing streets, people rarely make decisions alone. Rather, multiple people decide sequentially, setting the stage for information cascades whereby early-deciding individuals can influence others' choices. To understand how information cascades through social systems, it is essential to capture the dynamics of the decision-making process. We introduce the social drift-diffusion model to capture these dynamics. We tested our model using a sequential choice task. The model was able to recover the dynamics of the social decision-making process, accurately capturing how individuals integrate personal and social information dynamically over time and when their decisions were timed. Our results show the importance of the interrelationships between accuracy, confidence, and response time in shaping the quality of information cascades. The model reveals the importance of capturing the dynamics of decision processes to understand how information cascades in social systems, paving the way for applications in other social systems.