Informational and Normative Influences in Conformity from a Neurocomputational Perspective

Toelch, Ulf; Dolan, Raymond J.

doi:10.1016/j.tics.2015.07.007

Cited by 109 publications

(131 citation statements)

References 80 publications

(110 reference statements)

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“…Neuroimaging studies of perceptual decision-making provide evidence that an observer has a mental model to conduct Bayesian inferences to infer probable states of the world from their observations [32,33]. In the field of social decision-making, although recent theoretical models proposed that such a Bayesian framework may be applied to understand decision-making under social influence [23,34], empirical evidence for characterizing the neural signals of such social decisions is still lacking. Our Bayesian model assumes that people process their own judgments and the judgments of others as distinct probability distributions considering the likelihoods of the most appropriate judgment.…”

Section: Introductionmentioning

confidence: 99%

Integration of individual and social information for decision-making in groups of different sizes

Park¹,

Goïame²,

O’Connor³

et al. 2017

PLoS Biol

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When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with those of others (social information). Here, we investigated the neurocomputational mechanisms of how we adapt our judgments to those made by groups of different sizes, in the context of jury decisions for a criminal. By testing different theoretical models, we showed that a social Bayesian inference model captured changes in judgments better than 2 other models. Our results showed that participants updated their beliefs by appropriately weighting individual and social sources of information according to their respective credibility. When investigating 2 fundamental computations of Bayesian inference, belief updates and credibility estimates of social information, we found that the dorsal anterior cingulate cortex (dACC) computed the level of belief updates, while the bilateral frontopolar cortex (FPC) was more engaged in individuals who assigned a greater credibility to the judgments of a larger group. Moreover, increased functional connectivity between these 2 brain regions reflected a greater influence of group size on the relative credibility of social information. These results provide a mechanistic understanding of the computational roles of the FPC-dACC network in steering judgment adaptation to a group’s opinion. Taken together, these findings provide a computational account of how the human brain integrates individual and social information for decision-making in groups.

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Section: Introductionmentioning

confidence: 99%

Integration of individual and social information for decision-making in groups of different sizes

Park¹,

Goïame²,

O’Connor³

et al. 2017

PLoS Biol

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show abstract

“…This affects both big and small 32 decisions alike: we care about what our family and friends think of which major we choose in 33 college, and we also monitor other peoples' choices at the lunch counter in order to obtain some 34 guidance for our own menu selection. Behavioral studies have examined social influence as 35 expressed by conformity (Asch, 1956) and have classified two major sources of social influence: 36 normative and informational influence (Cialdini and Goldstein, 2004;Toelch and Dolan, 2015; 37 Fehr and Schurtenberger, 2018). Normative influence leads to public compliance, but individuals 38 may maintain private beliefs, whereas informational influence hypothesizes that social information 39 is integrated into the own valuation process.…”

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confidence: 99%

A brain network supporting social influences in human decision-making

Zhang

Gläscher

2019

Preprint

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12Social Decision-making is driven by normative influence (leading to public compliance) and 13 informational influence (overwriting private beliefs), but how the brain encodes these modulating 14 forces in probabilistic environments remains unanswered. Using a novel goal-directed learning 15 paradigm in 185 participants, we observed opposite effects of group consensus on choice and 16 confidence: people succumbed to the group when confronted with dissenting information, but 17 increased their confidence when observing confirming information. Leveraging computational 18 modeling and functional neuroimaging we captured the nuanced distinction between normative 19 and informational influence, and identified their unique but interacting neural representations in 20 the right temporoparietal junction (processing social information) and in prefrontal cortices 21 (representing value computations), whose functional coupling instantiates a reward prediction 22 error and a novel social prediction error that modulate behavioral adjustment. These results suggest 23 that a closed-loop network between the brain's reward hub and social hub supports social influence 24 in human decision-making. 25 26 Keywords: 27 Social influence, reinforcement learning, social learning, prediction error, decision neuroscience, 28 model-based fMRI, computational modeling, hierarchical Bayesian analysis 29 30 31Most of our everyday decisions are made in a social context. This affects both big and small 32 decisions alike: we care about what our family and friends think of which major we choose in 33 college, and we also monitor other peoples' choices at the lunch counter in order to obtain some 34 guidance for our own menu selection. Behavioral studies have examined social influence as 35 expressed by conformity (Asch, 1956) and have classified two major sources of social influence: 36 normative and informational influence (Cialdini and Goldstein, 2004;Toelch and Dolan, 2015; 37 Fehr and Schurtenberger, 2018). Normative influence leads to public compliance, but individuals 38 may maintain private beliefs, whereas informational influence hypothesizes that social information 39 is integrated into the own valuation process. Neuroscience studies have recently attempted to 40 assess the neurobiological underpinnings of both two types of influence (Klucharev et al., 2009; 41 Campbell-Meiklejohn et al., 2010;Edelson et al., 2011;Zaki et al., 2011; Izuma and Adolphs, 42 2013;Campbell-Meiklejohn et al., 2017;De Martino et al., 2017;Park et al., 2017). However, 43 results are controversial (Toelch and Dolan, 2015), and more importantly, none of them have 44 addressed the neurocomputational distinction and interaction between normative and 45 informational influence in conjunction with individuals' own valuation processes. This is largely 46 due to the challenge that most studies (Klucharev et al., 2009;Campbell-Meiklejohn et al., 2010; 47 Zaki et al., 2011; Izuma and Adolphs, 2013) relied on p...

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“…To investigate this, we applied an analysis used in our prior work [22], as well as other recent conformity research [24]. We grouped each trial into one of three feedback bins: Peers Higher (25.8% of all trials), Peers Lower (31.4% of all trials), and Peers Same (42.8% of all trials).…”

Section: Resultsmentioning

confidence: 99%

Social Norms Shift Preferences for Healthy and Unhealthy Foods

2016

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This research investigated whether people change their food preferences and eating behavior in response to health-based social norms. One hundred twenty participants rated a series of healthy and unhealthy food images. After each rating, participants sometimes viewed a rating that ostensibly represented the average rating of previous participants. In fact, these average ratings were manipulated to convey a particular social norm. Participants either saw average ratings that favored healthy foods, favored unhealthy foods, or did not see any average ratings. Participants then re-rated those same food images after approximately ten minutes and again three days later. After the norm manipulation, participants were given the chance to take as many M&Ms as they wanted. Participants exposed to a healthy social norm consistently reported lower preferences for unhealthy foods as compared to participants in the other two conditions. This preference difference persisted three days after the social norm manipulation. However, health-based social norm manipulations did not influence the amount of M&Ms participants took. Although health-based social norm manipulations can influence stated food preferences, in this case they did not influence subsequent eating behavior.

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Informational and Normative Influences in Conformity from a Neurocomputational Perspective

Cited by 109 publications

References 80 publications

Integration of individual and social information for decision-making in groups of different sizes

Integration of individual and social information for decision-making in groups of different sizes

A brain network supporting social influences in human decision-making

Social Norms Shift Preferences for Healthy and Unhealthy Foods

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