Do we advise as one likes? The alignment bias in social advice giving

We often give advice to influence others, but could our own advice also be shaped by the very individuals we aim to influence (i.e., advisees)? This reverse flow of social influence-from those typically seen as being influenced to those who provide the influence-has been largely neglected, limiting our understanding of the reciprocal nature of human communications. Here, we conducted a series of experiments and applied computational modelling to systematically investigate how advisees' opinions shape the advice-giving process. In an investment game, participants (n = 346, across four studies) provided advice either independently or after observing advisees' opinions (Studies 1 & 2), with feedback on their advice (acceptance or rejection) provided by advisees (Studies 3 & 4). Our findings reveal that advisors tend to adjust their advice to align with the advisees' opinions (we refer to this as the alignment bias) (Study 1). This tendency, which reflects normative conformity, persists even when advisors were directly incentivized to provide accurate advice (Study 2). As feedback is introduced, advisors' behavior shifts in ways best captured by a reinforcement learning model, suggesting that advisees' feedback drives adaptations in advice giving that maximize acceptance and minimize rejection (Study 3). This adaptation persisted even when acceptance is rare, as bolstered by the model-based evidence (Study 4). Collectively, our findings highlight advisors' susceptibility to the consequence of giving advice, which can lead to counterproductive impacts on decision-making processes and misinformation exacerbation in social encounters.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-12-02
DOI
https://doi.org/10.1371/journal.pcbi.1013732
Primary Topic
Experimental Behavioral Economics Studies
Type
article
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article

Do we advise as one likes? The alignment bias in social advice giving

Yafeng Pan, Xitong Luo, Lei Zhang
PLoS Computational Biology
Experimental Behavioral Economics Studies
article

Do we advise as one likes? The alignment bias in social advice giving

Yafeng Pan, Xitong Luo, Lei Zhang
article en

Abstract

We often give advice to influence others, but could our own advice also be shaped by the very individuals we aim to influence (i.e., advisees)? This reverse flow of social influence-from those typically seen as being influenced to those who provide the influence-has been largely neglected, limiting our understanding of the reciprocal nature of human communications. Here, we conducted a series of experiments and applied computational modelling to systematically investigate how advisees' opinions shape the advice-giving process. In an investment game, participants (n = 346, across four studies) provided advice either independently or after observing advisees' opinions (Studies 1 & 2), with feedback on their advice (acceptance or rejection) provided by advisees (Studies 3 & 4). Our findings reveal that advisors tend to adjust their advice to align with the advisees' opinions (we refer to this as the alignment bias) (Study 1). This tendency, which reflects normative conformity, persists even when advisors were directly incentivized to provide accurate advice (Study 2). As feedback is introduced, advisors' behavior shifts in ways best captured by a reinforcement learning model, suggesting that advisees' feedback drives adaptations in advice giving that maximize acceptance and minimize rejection (Study 3). This adaptation persisted even when acceptance is rare, as bolstered by the model-based evidence (Study 4). Collectively, our findings highlight advisors' susceptibility to the consequence of giving advice, which can lead to counterproductive impacts on decision-making processes and misinformation exacerbation in social encounters.

PLoS Computational BiologyVol. 21(12)
Birmingham City University (GB), University College Birmingham (GB), Zhejiang University (CN), University of Birmingham (GB)
Openalex Percentile: Top 62%
Experimental Behavioral Economics Studies
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Do we advise as one likes? The alignment bias in social advice giving — Yafeng Pan, Xitong Luo, et al. · PLoS Computational Biology (2025) | TGRS Research Map | TGRS