Social influence and perceptual decision making: a diffusion model analysis

Dynamics of sensory and decisional biases in perceptual decision making: Insights from the face distortion illusion

Bias in perceptual decision making can have both sensory and decisional origins. These distinct sources of bias are typically seen as static and stable over time. However, human behavior is dynamic and constantly adapting. Yet it remains unclear how sensory and decisional biases progress in distinct ways over time. We addressed this question by tracking the dynamics of sensory and decisional biases during a task that involves a visual illusion. Observers saw multiple pairs of peripherally presented faces that induce a strong illusion making the faces appear distorted and grotesque. The task was to judge whether one of the last two faces had true physical distortion (experimentally introduced in half of the trials). Initially, participants classified most faces as distorted as exemplified by a liberal response bias. However, over the course of the experiment, this response bias gradually disappeared even though the distortion illusion remained equally strong, as demonstrated by a separate subjective rating task without artificially distorted faces. The results suggest that the sensory bias was progressively countered by an opposite decisional bias. This transition was accompanied by an increase in reaction times and a decrease in confidence relative to a condition that does not induce the visual illusion. All results were replicated in a second experiment with inverted faces. These findings demonstrate that participants dynamically adjust their decisional bias to compensate for sensory biases, and that these two biases together determine how humans make perceptual decisions.

Computational modeling of social evaluative decision-making elucidates individual differences in adolescent anxiety

Adolescents experience significant developmental changes during a time of heightened sensitivity to social cues, particularly rejection by peers, which can be especially overwhelming for those with elevated levels of social anxiety. Social evaluative decision-making tasks have been useful in uncovering the neural correlates of information processing biases; however, linking youths' task-based performance to individual differences in psychopathology (e.g., anxiety symptoms) has proven more elusive. Here, we address this weakness with drift diffusion modeling to decompose youths' performance on the social judgment paradigm (SJP) to determine if this approach is useful in discovering individual differences in anxiety symptoms, as well as puberty, age, and sex. A sample of 103 adolescents (55 males, Mage = 14.49, SD = 1.69) completed the SJP and self-report measures of anxiety, as well as self- and parent-reported measures of puberty. The decision threshold parameter, reflecting the amount of evidence needed to make a social evaluative decision, predicted youth self-reported anxiety, above and beyond typical metrics of SJP performance. Our results highlight the potential advantage of parsing task performance according to the underlying cognitive processes. Future research would likely benefit from applying computational modeling approaches to social judgment tasks when attempting to uncover performance-based individual differences in psychopathology.

Social influence and social identity: A diffusion model analysis

Building on the seminal studies of Solomon Asch and Muzafer Sherif, recent research has advanced our understanding of the mechanisms underlying social influence by applying a diffusion model analysis. Here, we combined the social identity approach to social influence with a diffusion model analysis to unravel the mechanisms underlying social influence. In particular, we aimed to disentangle whether the difference between in-group and out-group influence on perceptual decision-making is driven by a judgmental bias (i.e., changes in decision criteria) or a perceptual bias (i.e., changes in the uptake of sensory information). Preregistered analyses indicated that in-groups exerted stronger social influence than out-groups because in-groups induced a stronger perceptual bias than out-groups. This finding is in line with the single process assumption of the social identity approach because it implicates that the single process driving social influence (i.e., self-categorisation) translates into a change in a single subprocess of decision-making (i.e., biased information uptake). In conclusion, our results highlight that our theoretical understanding of social influence can be expanded by integrating the social identity approach with a diffusion model analysis.

What can evidence accumulation modelling tell us about human social cognition?

Evidence accumulation models are a series of computational models that provide an account for speeded decision-making. These models have been used extensively within the cognitive psychology literature to great success, allowing inferences to be drawn about the psychological processes that underlie cognition that are sometimes not available in a traditional analysis of accuracy or reaction time (RT). Despite this, there have been only a few applications of these models within the domain of social cognition. In this article, we explore several ways in which the study of human social information processing would benefit from application of evidence accumulation modelling. We begin first with a brief overview of the evidence accumulation modelling framework and their past success within the domain of cognitive psychology. We then highlight five ways in which social cognitive research would benefit from an evidence accumulation approach. This includes (1) greater specification of assumptions, (2) unambiguous comparisons across blocked task conditions, (3) quantifying and comparing the magnitude of effects in standardised measures, (4) a novel approach for studying individual differences, and (5) improved reproducibility and accessibility. These points are illustrated using examples from the domain of social attention. Finally, we outline several methodological and practical considerations, which should help researchers use evidence accumulation models productively. Ultimately, it will be seen that evidence accumulation modelling offers a well-developed, accessible, and commonly understood framework that can reveal inferences about cognition that may otherwise be out of reach in a traditional analysis of accuracy and RT. This approach, therefore, has the potential to substantially revise our understanding of social cognition.

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.

Situational social influence leading to non-compliance with conservation rules

It is well established that the decisions that we make can be strongly influenced by the behaviour of others. However, testing how social influence can lead to non-compliance with conservation rules during an individual's decision-making process has received little research attention. We synthesise advances in understanding of conformity and rule-breaking in individuals and in groups, and take a situational approach to studying the social dynamics and ensuing social identity changes that can lead to non-compliant decision-making. We focus on situational social influence contagion that are copresent (i.e., same space and same time) or trace-based (i.e., behavioural traces in the same space). We then suggest approaches for testing how situational social influence can lead to certain behaviours in non-compliance with conservation rules.

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.

Social norm learning from non-human agents can induce a persistent perceptual bias: A diffusion model approach

Seminal studies on social influence (Asch, 1956; Sherif, 1935) were based on face-to-face interactions between humans. Nowadays, computer-mediated communication is steadily becoming ubiquitous, and we are increasingly influenced by non-human agents, such as algorithms, robots, and chatbots. The present research aimed to answer two important questions: Can non-human agents exert social influence in a persistent manner and, thus, contribute to the emergence of social norms? And if this is the case, is social influence exerted by non-human agents mediated by the same or by different cognitive mechanisms as social influence exerted by human agents? To answer these questions, we used an online version of an established paradigm in research on social norm learning. To examine the cognitive underpinnings of social influence, we used a diffusion model approach. Our results provide strong evidence for the notion that non-human agents can induce persistent social influence outside an immediate group context and, hence, can contribute to the emergence of social norms. Furthermore, results from our diffusion model analyses support the notion that social influence exerted by non-human agents is mainly mediated by the same cognitive mechanisms as social influence exerted by human agents.

Distinct neurocomputational mechanisms support informational and socially normative conformity

A change of mind in response to social influence could be driven by informational conformity to increase accuracy, or by normative conformity to comply with social norms such as reciprocity. Disentangling the behavioural, cognitive, and neurobiological underpinnings of informational and normative conformity have proven elusive. Here, participants underwent fMRI while performing a perceptual task that involved both advice-taking and advice-giving to human and computer partners. The concurrent inclusion of 2 different social roles and 2 different social partners revealed distinct behavioural and neural markers for informational and normative conformity. Dorsal anterior cingulate cortex (dACC) BOLD response tracked informational conformity towards both human and computer but tracked normative conformity only when interacting with humans. A network of brain areas (dorsomedial prefrontal cortex (dmPFC) and temporoparietal junction (TPJ)) that tracked normative conformity increased their functional coupling with the dACC when interacting with humans. These findings enable differentiating the neural mechanisms by which different types of conformity shape social changes of mind.

Confidence Modulates the Conformity Behavior of the Investors and Neural Responses of Social Influence in Crowdfunding

The decision about whether to invest can be affected by the choices or opinions of others known as a form of social influence. People make decisions with fluctuating confidence, which plays an important role in the decision process. However, it remains a fair amount of confusion regarding the effect of confidence on the social influence as well as the underlying neural mechanism. The current study applied a willingness-to-invest task with the event-related potentials method to examine the behavioral and neural manifestations of social influence and its interaction with confidence in the context of crowdfunding investment. The behavioral results demonstrate that the conformity tendency of the people increased when their willingness-to-invest deviated far from the group. Besides, when the people felt less confident about their initial judgment, they were more likely to follow the herd. In conjunction with the behavioral findings, the neural results of the social information processing indicate different susceptibilities to small and big conflicts between the own willingness of the people and the group, with small conflict evoked less negative feedback-related negativity (FRN) and more positive late positive potential (LPP). Moreover, confidence only modulated the later neural processing by eliciting larger LPP in the low confidence, implying more reliance on social information. These results corroborate previous findings regarding the conformity effect and its neural mechanism in investment decision and meanwhile extend the existing works of literature through providing behavioral and neural evidence to the effect of confidence on the social influence in the crowdfunding marketplace.

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.

How peer influence shapes value computation in moral decision-making

Moral behavior is susceptible to peer influence. How does information from peers influence moral preferences? We used drift-diffusion modeling to show that peer influence changes the value of moral behavior by prioritizing the choice attributes that align with peers' goals. Study 1 (N = 100; preregistered) showed that participants accurately inferred the goals of prosocial and antisocial peers when observing their moral decisions. In Study 2 (N = 68), participants made moral decisions before and after observing the decisions of a prosocial or antisocial peer. Peer observation caused participants' own preferences to resemble those of their peers. This peer influence effect on value computation manifested as an increased weight on choice attributes promoting the peers' goals that occurred independently from peer influence on initial choice bias. Participants' self-reported awareness of influence tracked more closely with computational measures of prosocial than antisocial influence. Our findings have implications for bolstering and blocking the effects of prosocial and antisocial influence on moral behavior.

Electronic consenting for conducting research remotely: A review of current practice and key recommendations for using e-consenting

BACKGROUND

Electronic approaches are becoming more widely used to obtain informed consent for research participation. Electronic consent (e-consent) provides an accessible and versatile approach to the consenting process, which can be enhanced with audio-visual and interactive features to improve participant engagement and comprehension of study procedures. Best practice guidance underpinned by ethical principles is required to ensure effective implementation of e-consent for use in research.

AIM

To identify the key considerations for successful and ethical implementation of e-consent in the recruitment of participants to research projects which are conducted remotely.

METHODS

Electronic database searches of CINAHL, Medline, Embase, DARE, HTA, PubMed, the Cochrane Library, Scopus, Web of Science, NHS Evidence, and hand-searches of reference lists were performed. Primary research studies of adult (≥ 18 years old) research participants using e-consent, published in English language, peer-reviewed journals between 2010-2020 were eligible for inclusion.

RESULTS

Of the initial 665 identified studies, 18 met the inclusion criteria: 6 cohort studies, 5 qualitative studies, 4 randomised control trials, 2 mixed-methods studies and one case-control study. Critical appraisal of included studies using Critical Appraisal Skills Program (CASP) tools suggested a low to moderate risk of bias in most studies (n = 15). Key practice recommendations for researchers using e-consent were identified around five primary themes: 1) accessibility and user-friendliness of e-consent, 2) user engagement and comprehension, 3) customisability to participant preferences and demographics, 4) data security and 5) impact on research teams.

CONCLUSION

E-consenting approaches are generally well received by participants, with most studies reporting user-friendly interfaces and sufficient participant comprehension of consenting documentation.

IMPLICATIONS FOR PRACTICE

E-consent may facilitate remotely-conducted research by offering a feasible and robust alternative to face-to-face consenting approaches, however paper-based options should still be offered, based on participant preference. Customising e-consenting platforms may improve accessibility for individuals with specific needs, and increase engagement with study information. Research teams must offer prospective participants opportunities to discuss study information in real-time.

Altercentric Cognition: How Others Influence Our Cognitive Processing

Humans are ultrasocial, yet, theories of cognition have often been occupied with the solitary mind. Over the past decade, an increasing volume of work has revealed how individual cognition is influenced by the presence of others. Not only do we rapidly identify others in our environment, but we also align our attention with their attention, which influences what we perceive, represent, and remember, even when our immediate goals do not involve coordination. Here, we refer to the human sensitivity to others and to the targets and content of their attention as 'altercentrism'; and aim to bring seemingly disparate findings together, suggesting that they are all reflections of the altercentric nature of human cognition.

Modulating the Activity of vmPFC Regulates Informational Social Conformity: A tDCS Study

Social conformity has been evaluated in many different contexts, ranging from an emotional contagion in psychology, to speculative episodes in economics, to mass protests concerning politics. Previous neuroscience studies suggest that the ventromedial prefrontal cortex (vmPFC) participates in social conformity, especially when it comes to the value integration process, but the specific mechanism of vmPFC is still unclear. In this study, we aimed to identify a direct link between the vmPFC and conformity tendencies by means of transcranial direct current stimulation (tDCS). Conformity tendencies are measured by the probability that participants change their decisions when they observe the majority responses. In our experiment, subjects could make two decisions in each trial, once without social information and once with social information, which allowed us to directly observe the conformity tendency of subjects in different conditions. We found that cathodal stimulation of the vmPFC significantly increased conformity tendency and decreased response time when the initial decision of participants differs from the majority opinion. Based on the experimental results, our study suggests that the vmPFC mainly inhibits and regulates the informational conformity behavior. These findings complement investigations of the neural mechanism of conformity and the role of the vmPFC in the neural circuit behind conformity behavior.

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.

Crowdsourcing punishment: Individuals reference group preferences to inform their own punitive decisions

Justice systems delegate punishment decisions to groups in the belief that the aggregation of individuals' preferences facilitates judiciousness. However, group dynamics may also lead individuals to relinquish moral responsibility by conforming to the majority's preference for punishment. Across five experiments (N = 399), we find Victims and Jurors tasked with restoring justice become increasingly punitive (by as much as 40%) as groups express a desire to punish, with every additional punisher augmenting an individual's punishment rates. This influence is so potent that knowing about a past group's preference continues swaying decisions even when they cannot affect present outcomes. Using computational models of decision-making, we test long-standing theories of how groups influence choice. We find groups induce conformity by making individuals less cautious and more impulsive, and by amplifying the value of punishment. However, compared to Victims, Jurors are more sensitive to moral violation severity and less readily swayed by the group. Conformity to a group's punitive preference also extends to weightier moral violations such as assault and theft. Our results demonstrate that groups can powerfully shift an individual's punitive preference across a variety of contexts, while additionally revealing the cognitive mechanisms by which social influence alters moral values.

Developmental trajectory of social influence integration into perceptual decisions in children

The opinions of others have a profound influence on decision making in adults. The impact of social influence appears to change during childhood, but the underlying mechanisms and their development remain unclear. We tested 125 neurotypical children between the ages of 6 and 14 years on a perceptual decision task about 3D-motion figures under informational social influence. In these children, a systematic bias in favor of the response of another person emerged at around 12 years of age, regardless of whether the other person was an age-matched peer or an adult. Drift diffusion modeling indicated that this social influence effect in neurotypical children was due to changes in the integration of sensory information, rather than solely a change in decision behavior. When we tested a smaller cohort of 30 age- and IQ-matched autistic children on the same task, we found some early decision bias to social influence, but no evidence for the development of systematic integration of social influence into sensory processing for any age group. Our results suggest that by the early teens, typical neurodevelopment allows social influence to systematically bias perceptual processes in a visual task previously linked to the dorsal visual stream. That the same bias did not appear to emerge in autistic adolescents in this study may explain some of their difficulties in social interactions.

Two types of backward crosstalk: Sequential modulations and evidence from the diffusion model

In multitasking, the backward crosstalk effect (BCE) means that Task 1 performance is influenced by characteristics of Task 2. For example, (1) RT1 is shorter when the two responses are given on the same (compatible trial) compared with opposite sides (incompatible conflict-trial; compatibility-based BCE), and (2) RT1 is longer when Task 2 is a no-go relative to a go task (no-go BCE). We investigated the impact of recently experienced trial and conflict history on the size of such BCEs. Similar to the Gratton effect in standard conflict tasks, clear sequential modulations were observed for the two kinds of BCEs, which were present following (1) compatible trials and (2) go-trials and inverted following (1) incompatible and (2) no-go trials. Furthermore, recent evidence from mental chronometry studies suggests that the compatibility-based BCE is located inside the response selection stage, while the no-go-based BCE arises in motor execution. Against this background, a diffusion model analysis was carried out to reveal the reason(s) for the sequential modulations. As expected, for the compatibility-based BCE, changes in drift rate explain the sequential modulations, but for the no-go BCE changes in non-decision time are important. The present results indicate that both BCEs not only differ fundamentally in their underlying processes, but also in the way cognitive control is adjusted.

Impact of Context Information on Metaphor Elaboration

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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

Adaptive decisions in social contexts depend on both perceptual information and social expectations or norms. These are potentially in conflict when certain choices are beneficial for an individual, but societal rules mandate a different course of action. To resolve such a conflict, the reliability of information has to be balanced against potentially deleterious effects of non-compliance such as ostracism. In this study, we systematically investigated how interactions between perceptual and social influences affect decision-relevant cognitive processes. In a direction-of-motion discrimination task, participants received perceptual information alongside information on other players' choices. In addition, we created conflict scenarios where players' choices affected other participants' monetary rewards dependent on whether their choices were in line or against the opinion of the other players. Importantly, we altered the strength of this manipulation in two separate experiments by contrasting motivations of either preventing harm or providing a benefit to others. Behavioural analyses and computational models of perceptual decisions showed that participants successfully integrated perceptual with social information. Participants' reliance on social information was effectively modulated in conflict situations. Critically, these effects were augmented when the strength of social norms was increased, indexing conditions under which social norms effectively influence decisions. These results inform theories of social influence by providing an account of how higher order goals like social norm compliance affect perceptual decisions.

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

Diffusion models (Ratcliff, 1978) make it possible to identify and separate different cognitive processes underlying responses in binary decision tasks (e.g., the speed of information accumulation vs. the degree of response conservatism). This becomes possible because of the high degree of information utilization involved. Not only mean response times or error rates are used for the parameter estimation, but also the response time distributions of both correct and error responses. In a series of simulation studies, the efficiency and robustness of parameter recovery were compared for models differing in complexity (i.e., in numbers of free parameters) and trial numbers (ranging from 24 to 5,000) using three different optimization criteria (maximum likelihood, Kolmogorov-Smirnov, and chi-square) that are all implemented in the latest version of fast-dm (Voss, Voss, & Lerche, 2015). The results revealed that maximum likelihood is superior for uncontaminated data, but in the presence of fast contaminants, Kolmogorov-Smirnov outperforms the other two methods. For most conditions, chi-square-based parameter estimations lead to less precise results than the other optimization criteria. The performance of the fast-dm methods was compared to the EZ approach (Wagenmakers, van der Maas, & Grasman, 2007) and to a Bayesian implementation (Wiecki, Sofer, & Frank, 2013). Recommendations for trial numbers are derived from the results for models of different complexities. Interestingly, under certain conditions even small numbers of trials (N < 100) are sufficient for robust parameter estimation.

Experimental validation of the diffusion model based on a slow response time paradigm

The diffusion model (Ratcliff, Psychol Rev 85(2):59-108, 1978) is a stochastic model that is applied to response time (RT) data from binary decision tasks. The model is often used to disentangle different cognitive processes. The validity of the diffusion model parameters has, however, rarely been examined. Only few experimental paradigms have been analyzed with those being restricted to fast response time paradigms. This is attributable to a recommendation stated repeatedly in the diffusion model literature to restrict applications to fast RT paradigms (more specifically, to tasks with mean RTs below 1.5 s per trial). We conducted experimental validation studies in which we challenged the necessity of this restriction. We used a binary task that features RTs of several seconds per trial and experimentally examined the convergent and discriminant validity of the four main diffusion model parameters. More precisely, in three experiments, we selectively manipulated these parameters, using a difficulty manipulation (drift rate), speed-accuracy instructions (threshold separation), a more complex motoric task (non-decision time), and an asymmetric payoff matrix (starting point). The results were similar to the findings from experimental validation studies based on fast RT paradigms. Thus, our experiments support the validity of the parameters of the diffusion model and speak in favor of an extension of the model to paradigms based on slower RTs.

Motivated malleability: Frontal cortical asymmetry predicts the susceptibility to social influence

Humans, just as many other animals, regulate their behavior in terms of approaching stimuli associated with pleasure and avoiding stimuli linked to harm. A person's current and chronic motivational direction - that is, approach versus avoidance orientation - is reliably reflected in the asymmetry of frontal cortical low-frequency oscillations. Using resting electroencephalography (EEG), we show that frontal asymmetry is predictive of the tendency to yield to social influence: Stronger right- than left-side frontolateral activation during a resting-state session prior to the experiment was robustly associated with a stronger inclination to adopt a peer group's judgments during perceptual decision-making (Study 1). We posit that this reflects the role of a person's chronic avoidance orientation in socially adjusted behavior. This claim was strongly supported by additional survey investigations (Studies 2a, 2b, 2c), all of which consistently revealed that trait avoidance was positively linked to the susceptibility to social influence. The present contribution thus stresses the relevance of chronic avoidance orientation in social conformity, refining (yet not contradicting) the longstanding view that socially influenced behavior is motivated by approach-related goals. Moreover, our findings valuably underscore and extend our knowledge on the association between frontal cortical asymmetry and a variety of psychological variables.

Model Complexity in Diffusion Modeling: Benefits of Making the Model More Parsimonious

The diffusion model (Ratcliff, 1978) takes into account the reaction time distributions of both correct and erroneous responses from binary decision tasks. This high degree of information usage allows the estimation of different parameters mapping cognitive components such as speed of information accumulation or decision bias. For three of the four main parameters (drift rate, starting point, and non-decision time) trial-to-trial variability is allowed. We investigated the influence of these variability parameters both drawing on simulation studies and on data from an empirical test-retest study using different optimization criteria and different trial numbers. Our results suggest that less complex models (fixing intertrial variabilities of the drift rate and the starting point at zero) can improve the estimation of the psychologically most interesting parameters (drift rate, threshold separation, starting point, and non-decision time).

Social conformity is due to biased stimulus processing: electrophysiological and diffusion analyses

Hundreds of studies have found that humans' decisions are strongly influenced by the opinions of others, even when making simple perceptual decisions. In this study, we aimed to clarify whether this effect can be explained by social influence biasing (early) perceptual processes. We employed stimulus evoked potentials, lateralized readiness potentials (LRPs) and a diffusion model analysis of reaction time data to uncover the neurocognitive processes underlying social conformity in perceptual decision-making. The diffusion model analysis showed that social conformity was due to a biased uptake of stimulus information and accompanied by more careful stimulus processing. As indicated by larger N1-amplitudes, social influence increased early attentional resources for stimulus identification and discrimination. Furthermore, LRP analyses revealed that stimulus processing was biased even in cases of non-conformity. In conclusion, our results suggest that the opinion of others can cause individuals to selectively process stimulus information supporting this opinion, thereby inducing social conformity. This effect is present even when individuals do not blindly follow the majority but rather carefully process stimulus information.

How social and non-social information influence classification decisions: A computational modelling approach

Social information such as observing others can improve performance in decision making. In particular, social information has been shown to be useful when finding the best solution on one's own is difficult, costly, or dangerous. However, past research suggests that when making decisions people do not always consider other people's behaviour when it is at odds with their own experiences. Furthermore, the cognitive processes guiding the integration of social information with individual experiences are still under debate. Here, we conducted two experiments to test whether information about other persons' behaviour influenced people's decisions in a classification task. Furthermore, we examined how social information is integrated with individual learning experiences by testing different computational models. Our results show that social information had a small but reliable influence on people's classifications. The best computational model suggests that in categorization people first make up their own mind based on the non-social information, which is then updated by the social information.

The effect of noise-induced variance on parameter recovery from reaction times

Background

Technical noise can compromise the precision and accuracy of the reaction times collected in psychological experiments, especially in the case of Internet-based studies. Although this noise seems to have only a small impact on traditional statistical analyses, its effects on model fit to reaction-time distributions remains unexplored.

Results

Across four simulations we study the impact of technical noise on parameter recovery from data generated from an ex-Gaussian distribution and from a Ratcliff Diffusion Model. Our results suggest that the impact of noise-induced variance tends to be limited to specific parameters and conditions.

Conclusions

Although we encourage researchers to adopt all measures to reduce the impact of noise on reaction-time experiments, we conclude that the typical amount of noise-induced variance found in these experiments does not pose substantial problems for statistical analyses based on model fitting.

Social proof in the human brain: Electrophysiological signatures of agreement and disagreement with the majority

Perceiving one's deviance from the majority usually instigates conformal adjustments of one's own behavior to that of the group. Using ERPs, we investigated the mechanisms by which agreeing and disagreeing with the majority are differentially represented in the human brain and affect subsequent cognitive processing. Replicating previous findings obtained in a slightly different paradigm, we found that learning about one's disagreement with the majority, as compared to learning about one's agreement with the majority, elicited a mediofrontal feedback negativity. Moreover, an enhanced posterior late positive complex was observed during the processing of agreement as compared to disagreement. Finally, when the to-be-judged faces were viewed for a second time, a stronger posterior P2 was observed for faces on whose judgment one had previously agreed with the majority than for those on which one had disagreed. We thus demonstrate that the brain places particular emphasis on the encoding of the rewarding experience of finding strong social proof for one's judgments. Likewise, having experienced agreement on the judgment of a certain item affects even the later reanalysis of this very item, as previous agreement increases early attention, as reflected in the P2. These findings corroborate and extend previous results and theories on the neurocognitive principles of social influence.

Assessing and correcting for regression toward the mean in deviance-induced social conformity

Our understanding of the mechanisms underlying social conformity has recently advanced due to the employment of neuroscience methodology and novel experimental approaches. Most prominently, several studies have demonstrated the role of neural reinforcement-learning processes in conformal adjustments using a specifically designed and frequently replicated paradigm. Only very recently, the validity of the critical behavioral effect in this very paradigm was seriously questioned, as it invites the unwanted contribution of regression toward the mean. Using a straightforward control-group design, we corroborate this recent finding and demonstrate the involvement of statistical distortions. Additionally, however, we provide conclusive evidence that the paradigm nevertheless captures behavioral effects that can only be attributed to social influence. Finally, we present a mathematical approach that allows to isolate and quantify the paradigm’s true conformity effect both at the group level and for each individual participant. These data as well as relevant theoretical considerations suggest that the groundbreaking findings regarding the brain mechanisms of social conformity that were obtained with this recently criticized paradigm were indeed valid. Moreover, we support earlier suggestions that distorted behavioral effects can be rectified by means of appropriate correction procedures.