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Artificial intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist human decision-makers. However, the full realization of the potential of human-AI collaboration continues to face several challenges. First, the conditions that support complementarity (i.e., situations in which the performance of a human with AI assistance exceeds the performance of an unassisted human or the AI in isolation) must be understood. This task requires humans to be able to recognize situations in which the AI should be leveraged and to develop new AI systems that can learn to complement the human decision-maker. Second, human mental models of the AI, which contain both expectations of the AI and reliance strategies, must be accurately assessed. Third, the effects of different design choices for human-AI interaction must be understood, including both the timing of AI assistance and the amount of model information that should be presented to the human decision-maker to avoid cognitive overload and ineffective reliance strategies. In response to each of these three challenges, we present an interdisciplinary perspective based on recent empirical and theoretical findings and discuss new research directions.

Diffusion of responsibility is typically defined as the effect by which people feel less responsible for outcomes of their actions when they act as a member of a group than when they act individually. The research reported here extends the concept of diffusion of responsibility to contexts in which the actor has received advice. Responsibility when using advice and when acting contrary to advice are compared to each other, as well as to responsibility when acting alone or as part of a group. To provide a more complete picture, this research consolidates disparate concepts from previous work on diffusion of responsibility, including felt, judged, and anticipated responsibility assessments; distributive and case‐based models of responsibility; and positive and negative outcomes. Across three experiments, using advice conveyed less responsibility than either acting alone or acting contrary to advice, with greater use of advice further reducing responsibility. The magnitude of diffusion was influenced by the task outcome valence in ways consistent with self‐serving bias when acting alone and other‐serving bias when using advice. Diffusion was greater with distributive than case‐based responsibility models. The results were generally consistent across felt, judged, and anticipated responsibility, as well as with choice and judgment tasks. Implications and future research possibilities are discussed. [ABSTRACT FROM AUTHOR]

Advice taking is a crucial part of decision-making and has attracted the interest of scholars across the world. Laboratory research on advice taking has revealed several robust phenomena, such as sensitivity to advice quality or a tendency to underutilize advice. Despite extensive investigations in different countries, cultural differences in advice taking remain understudied. Knowing whether such cultural differences exist would not only be interesting from an academic standpoint but might also have consequences for multinational organizations and businesses. Here, we argue that prior laboratory research on cultural differences in advice taking is hindered by confounding factors, particularly the confound between participants’ cultural background and task difficulty. To draw a valid conclusion about cultural differences in advice taking, it is vital to develop a decision task devoid of this confound. Here, we develop such a judgment task and demonstrate that the core phenomena of advice taking manifest in a sample of German participants. We then use this task in a cross-national comparison of German and Chinese participants. While the core phenomena of advice taking consistently manifested in both samples, some differences emerged. Most notably, Chinese participants were more receptive of advice, even though they still underutilized it. This greater reliance on advice was driven by Chinese participants’ greater preference for averaging their own and the advisor’s judgments. We discuss how our findings extend current understanding of the nuanced interplay between cultural values and the dynamics of advice taking.

In previous research on advice taking, researchers have mainly focused on certain types of advice. However, in practice, when people give advice to others, there is often a degree of uncertainty (e.g. I think that the distance between Beijing and Shanghai is between 800 and 1200 km). To date, only a few studies have examined the impact of uncertain advice on advice taking. Through two studies, the present research explores the influence of advice uncertainty and individuals' regulatory mode predominance on advice taking and the mediating mechanism. In Study 1, the participants' chronic regulatory mode was measured by a questionnaire, and in Study 2, we induced the predominance of the participants' situational regulatory mode using a recall task. We found that people are more likely to adopt advice with low uncertainty. The moderating effect of participants' regulatory mode on the impact of advice uncertainty on advice taking occurs only when the regulatory mode is induced by the situation. For the assessment‐predominant group, there was a significant difference between the no‐uncertainty group and the high‐uncertainty group, while for the locomotion‐predominant group, this difference was not significant. Additionally, our study revealed the mediating role of advice reliability, which existed only when the participants were able to compare low‐ and no‐uncertainty advice in a within‐participant design. That is, when decision makers adopt uncertainty advice within ranges, they not only consider reliability but also weigh multiple factors. Our findings contribute to understanding the mechanisms underlying individuals' preferences for uncertain advice and reasoning about individual differences. [ABSTRACT FROM AUTHOR]

Advice taking and related research is dominated by deterministic weighting indices, specifically ratio‐of‐differences‐based formulas for investigating informational influence. Their arithmetic is intuitively simple, but they pose several measurement problems and restrict research to a particular paradigmatic approach. As a solution, we propose to specify how strongly peoples' judgments are influenced by externally provided evidence by fitting corresponding mixed‐effects regression models. Our approach explicitly distinguishes between endogenous components, such as updated beliefs, and exogenous components, such as independent initial judgments and advice. Crucially, mixed‐effects regression coefficients of various exogenous sources of information also reflect individual weighting but are based on a conceptually consistent representation of the endogenous judgment process. The formal derivation of the proposed weighting measures is accompanied by a detailed elaboration on their most important technical and statistical subtleties. We use this modeling approach to revisit empirical findings from several paradigms investigating algorithm aversion, sequential collaboration, and advice taking. In summary, we replicate and extend the original finding of algorithm appreciation and initially demonstrate a lack of evidence for both systematic order effects in sequential collaboration as well as differential weighting of multiple pieces of advice. In addition to opening new avenues for innovative research, appropriate modeling of information sampling and utilization has the potential to increase the reproducibility and replicability of behavioral science. Furthermore, the proposed method is relevant beyond advice taking, as mixed‐effects regression weights can also inform research on related cognitive phenomena such as multidimensional belief updating, anchoring effects, hindsight bias, or attitude change. [ABSTRACT FROM AUTHOR]

Selection decisions are often affected by irrelevant variables such as gender or race. People can discount this irrelevant information by adjusting their predictions accordingly, yet they fail to do so intuitively. In five online studies (N = 1077), participants were asked to make selection decisions in which the selection test was affected by irrelevant attributes. We examined whether in such decisions people are willing to be advised by algorithms, human advisors or prefer to decide without advice. We found that people fail to adjust for irrelevant information by themselves, and those who received advice from an algorithm or human advisor made better decisions. Interestingly, although most participants stated they prefer advice from human advisors, they tend to rely equally on algorithms in actual selection tasks. The sole exception is when they are forced to choose between an algorithm and a human advisor. In that case, they pick human advisors. We conclude that while algorithms may not be people's preferred source of advice in selection decisions, they are equally useful and can be implemented. [ABSTRACT FROM AUTHOR]

Three experiments were designed to investigate the effect of regulatory fit based on regulatory mode on advice taking. Experiments 1a and 1b used the integral manipulation method to explore whether the fit of participants' regulatory mode with advisors' advice-giving strategies improves participants' willingness to accept advice. Experiment 2 used the incidental manipulation method to explore whether regulatory fit has a delayed effect and can cause decision-makers to accept low-quality advice. The results showed the following: (1) Decision-makers are more willing to take advice when they have regulatory fit with advisors' advice-deriving strategies compared with nonfit conditions. (2) Regulatory fit has a delayed effect on advice taking, and it significantly improves the degree of advice taking even in the context of low-quality advice. [ABSTRACT FROM AUTHOR]

Conspiracy believers often claim to be critical thinkers their 'own research' instead of relying on others' testimony. In two preregistered behavioural studies conducted in the United Kingdom and Pakistan (Nparticipants = 864, Ntrials = 5408), we test whether conspiracy believers have a general tendency to discount social information (in favour of their own opinions and intuitions). We found that conspiracy mentality is not associated with social information use in text‐based (Study 1) and image‐based (Study 2) advice‐taking tasks. Yet, we found discrepancies between self‐reported and actual social information use. Conspiracy believers were more likely to report relying less on social information than actually relying less on social information in the behavioural tasks. Our results suggest that the scepticism of conspiracy believers towards epistemic authorities is unlikely to be the manifestation of a general tendency to discount social information. Conspiracy believers may be more permeable to social influence than they sometimes claim. [ABSTRACT FROM AUTHOR]

Many deviations from rational choice imply the neglect of important evidence and suggest the use of simple heuristics. In contrast, other deviations imply sensitivity to irrelevant evidence and suggest the use of overly complex rules. The current analysis takes two steps toward identifying the conditions that trigger these contradictory deviations from efficient reasoning. The first step involves a theoretical analysis. It shows that the contradictory deviations can be captured without assuming the use of rules of different complexity in different settings. Both deviations can be the product of a reliance on small samples of similar past experiences. This reliance on small samples triggers apparent overcomplexity when the optimal rule is simple, but more complex rules yield better outcomes in most cases; the opposite tendency, oversimplification, emerges when the optimal rule is complex, and simple rules yield better outcomes in most cases. The second step involves a preregistered experiment with 325 participants (Mechanical Turk workers). The experiment shows that human decision makers exhibit the pattern predicted by the reliance‐on‐small‐samples assumption. In the experiment, participants chose between the status quo and a risky alternative in a multi‐attribute decision with three binary cues. They used uninformative cues when this strategy was best in most cases yet ignored two informative cues when this strategy was best in most cases. In addition, describing the cues as recommendations given by three experts increased the tendency to follow the modal recommendation (even when reliance on only one of the experts was optimal), but people still behaved as though they relied on a small sample of past experiences. [ABSTRACT FROM AUTHOR]

We examine the impact of advice use on perceptions of competence. We propose that advice use sends opposing signals to an advisor regarding the advisee's competence. Greater advice use signals respect for the advisor, which is reciprocated by enhancing competence perceptions. However, greater advice use also indicates a lack of independence in judgment, reducing perceptions of competence. As a result, as advice use increases (i.e., gets closer to the exact advice provided), perceptions of competence first increase but then decrease. We further argue that the impact of advice use on competence is influenced by perceptions of information accessibility, such that when advisor and advisee have access to the same information, lower reliance on advice is more tolerated and less impactful on competence. We show that this effect is conceptually and empirically distinct from advisor's confidence and subsequent preference for advice use. [ABSTRACT FROM AUTHOR]

Financial advisors rely on accurate measures of investor risk preferences. This study compares different risk elicitation methods (REMs) in terms of their perceived suitability and impact on financial advice taking. The results suggest that the perceived suitability of the suggested risk profile strongly predicts delegation to an advisory tool. REMs differ in terms of their perceived process similarity with the investor, which positively affects suitability (and thus, delegation) directly and through its positive effect on source credibility. Differences were also found with regards to the perceived complexity of the risk profiling task, which is positively related to suitability. In summary, the findings imply that applying suitable REMs matters not only because it avoids misrepresentation of an investor's true risk preferences, but because it directly affects the propensity to delegate financial decision-making. [ABSTRACT FROM AUTHOR]

Research suggests that patients with borderline personality disorder (BPD) share a range of cognitive biases with patients with psychosis. As the disorder often manifests in dysfunctional social interactions, we assumed associated reasoning styles would be exaggerated in a social setting. For the present study, we applied the Judge-Advisor System by asking participants to provide initial estimates of a person's age and presumed hostility based on a portrait photo. Afterwards, we presented additional cues/advice in the form of responses by anonymous previous respondents. Participants could revise their estimate, seek additional advice, or make a decision. Contrary to our preregistered hypothesis, patients with BPD (n = 38) performed similarly to healthy controls (n = 30). Patients sought the same number of pieces of advice, were equally confident, and used advice in similar ways to revise their estimates. Thus, patients with BPD did trust advice. However, patients gave higher hostility ratings to the portrayed persons. In conclusion, patients with BPD showed no cognitive biases in seeking, evaluating, and integrating socially provided information. While the study implies emotional rather than cognitive biases in the disorder, cognitive biases may still prove to be useful treatment targets in order to encourage delaying and reflecting on extreme emotional responses in social interactions. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Companies frequently offer product recommendations to customers, according to various algorithms. This research explores how companies should frame the methods they use to derive their recommendations, in an attempt to maximize click-through rates. Two common framings—user-based and item-based—might describe the same recommendation. User-based framing emphasizes the similarity between customers (e.g., "People who like this also like..."); item-based framing instead emphasizes similarities between products (e.g., "Similar to this item"). Six experiments, including two field experiments within a mobile app, show that framing the same recommendation as user-based (vs. item-based) can increase recommendation click-through rates. The findings suggest that user-based (vs. item-based) framing informs customers that the recommendation is based on not just product matching but also taste matching with other customers. Three theoretically derived and practically relevant boundary conditions related to the recommendation recipient, the products, and other users also offer practical guidance for managers regarding how to leverage recommendation framings to increase recommendation click-throughs. [ABSTRACT FROM AUTHOR]

Expertise is a reliable cue for accuracy – experts are often correct in their judgments and opinions. However, the opposite is not necessarily the case – ignorant judges are not guaranteed to err. Specifically, in a question with a dichotomous response option, an ignorant responder has a 50% chance of being correct. In five studies, we show that people fail to understand this, and that they overgeneralize a sound heuristic (expertise signals accuracy) to cases where it does not apply (lack of expertise does not imply error). These studies show that people 1) tend to think that the responses of an ignorant person to dichotomous-response questions are more likely to be incorrect than correct, and 2) they tend to respond the opposite of what the ignorant person responded. This research also shows that this bias is at least partially intuitive in nature, as it manifests more clearly in quick gut responses than in slow careful responses. Still, it is not completely corrected upon careful deliberation. Implications are discussed for rationality and epistemic vigilance.

Although new information technologies and social networks make a wide variety of opinions and advice easily accessible, one can never be sure to get support on a focal judgment task. Nevertheless, participants in traditional advice taking studies are by default informed in advance about the opportunity to revise their judgment in the light of advice. The expectation of advice, however, may affect the weight assigned to it. The present research therefore investigates whether the advice taking process depends on the expectation of advice in the judge-advisor system (JAS). Five preregistered experiments (total N = 2019) compared low and high levels of advice expectation. While there was no evidence for expectation effects in three experiments with block-wise structure, we obtained support for a positive influence of advice expectation on advice weighting in two experiments implementing sequential advice taking. The paradigmatic disclosure of the full procedure to participants thus constitutes an important boundary condition for the ecological study of advice taking behavior. The results suggest that the conventional JAS procedure fails to capture a class of judgment processes where advice is unexpected and therefore weighted less.

Past research has found that people treat advice differently depending on its source. In many cases, people seem to prefer human advice to algorithms, but in others, there is a reversal, and people seem to prefer algorithmic advice. Across two studies, we examine the persuasiveness of, and judges' preferences for, advice from different sources when forecasting geopolitical events. We find that judges report domain‐specific preferences, preferring human advice in the domain of politics and algorithmic advice in the domain of economics. In Study 2, participants report a preference for hybrid advice, that combines human and algorithmic sources, to either one on it's own regardless of domain. More importantly, we find that these preferences did not affect persuasiveness of advice from these different sources, regardless of domain. Judges were primarily sensitive to quantitative features pertaining to the similarity between their initial beliefs and the advice they were offered, such as the distance between them and the relative advisor confidence, when deciding whether to revise their initial beliefs in light of advice, rather than the source that generated the advice. [ABSTRACT FROM AUTHOR]

Phatic communion is a type of speech that is used to facilitate social relationships without informational value, and its influence on advice taking is underexplored. Therefore, we conducted a pilot study and two main studies to explore the influence of phatic communion on advice taking. In the pilot study, we asked participants to evaluate experimental material stating phatic communion, which confirmed that the manipulation for phatic communion was effective. Study 1 explored the main effect of phatic communion on advice taking using a quantitative estimation task. Study 2 explored whether interpersonal familiarity had a moderating effect on the indirect relationship between phatic communion and advice taking via positive face threats. Overall, our results showed that advice given with phatic communion decreased (positive) face threats, and in turn, increased advice taking, but only when the relationship between the advisee and the advisor was unfamiliar. [ABSTRACT FROM AUTHOR]

Expertise is a reliable cue for accuracy -- experts are often correct in their judgments and opinions. However, the opposite is not necessarily the case -- ignorant judges are not guaranteed to err. Specifically, in a question with a dichotomous response option, an ignorant responder has a 50% chance of being correct. In five studies, we show that people fail to understand this, and that they overgeneralize a sound heuristic (expertise signals accuracy) to cases where it does not apply (lack of expertise does not imply error). These studies show that people 1) tend to think that the responses of an ignorant person to dichotomous-response questions are more likely to be incorrect than correct, and 2) they tend to respond the opposite of what the ignorant person responded. This research also shows that this bias is at least partially intuitive in nature, as it manifests more clearly in quick gut responses than in slow careful responses. Still, it is not completely corrected upon careful deliberation. Implications are discussed for rationality and epistemic vigilance. [ABSTRACT FROM AUTHOR]

We investigated how people would change and vary inaccepting advice when the effectiveness of advice was unclear.In each trial, participants estimated a monthly rent of anapartment room based on the attribute list. Then, anotherestimate by a real-estate agent was given as advice.Participants made a final estimation, either by taking theadvice fully, partially, or rejecting it totally. They repeated 48estimations without feedback. The weight of advice index,representing how much each participant weighed a givenadvice, gradually decreased as the number of trials increased.Interestingly, the gradual reduction of acceptance was notobserved in participants with high empathy and lowdepressive scores; they kept accepting advice even when theeffectiveness of advice was unclear. These results suggest thatthe willingness of accepting and using advice depends onhistory of advice taking, the individual traits, and mood.

Much research on advice taking examines how people revise point estimates given input from others. This work has established that people often egocentrically discount advice. If they were to place more weight on advice, their point estimates would be more accurate. Yet the focus on point estimates and accuracy has resulted in a narrow conception of what it means to heed advice. We distinguish between revisions of point estimates and revisions of attendant probability distributions. Point estimates represent a single best guess; distributions represent the probabilities that people assign to all possible answers. A more complete picture of advice taking is provided by considering revisions of distributions, which reflect changes in both confidence and best guesses. We capture this using a new measure of advice utilization: the influence of advice. We observe that, when input from a high-quality advisor largely agrees with a person's initial opinion, it engenders little change in one's point estimate and, hence, little change in accuracy yet significantly increases confidence. This pattern suggests more advice taking than generally suspected. However, it is not necessarily beneficial. Because people are typically overconfident to begin with, receiving advice that agrees with their initial opinion can exacerbate overconfidence. In several experiments, we manipulate advisor quality and measure the extent to which advice agrees with a person's initial opinion. The results allow us to pinpoint circumstances in which heeding advice is beneficial, improving accuracy or reducing overconfidence, as well as circumstances in which it is harmful, hurting accuracy or exacerbating overconfidence. This paper was accepted by Yuval Rottenstreich, judgment and decision making. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to identify under what circumstances advisor gender and advice justification influence advice taking by managers. Design/methodology/approach – The authors designed a quasirational managerial decision experiment with both analytic and intuitive cues. The design was a 2 × 2 between-subjects factorial, in which gender (male/female) and advice justification (intuitive/analytic) were crossed. The experiment involved two independent samples, taken from Amazon Mechanical Turk workers and Brazilian professionals. Findings – Results suggest that, in general, analytic justification is more valued than intuitive justification. The findings also infer that depending on the advisees’ sample and providing that advice justification is analytic, quasirational scenarios seem to favor male advisors (MTurk sample) or both male and female advisors with “male values” (professional sample), as analysis is traditionally considered a “male value.” Practical implications – Analytic justification will likely lead to more advice utilization in quasirational managerial situations, as it may act as a safeguard for the accuracy of the offered advice. Social implications – The results might signal an ongoing, but slow, process leading to the mitigation of gender stereotypes, considering that the male gender stereotype was active in the MTurk sample, but not in the professional one. Originality/value – This study contributes to the advice-taking research field by showing the interplay between advisor gender and advice justification in a quasirational managerial decision setting with both analytic and intuitive cues. In advice-taking literature, observations are usually collected from students. However, as this study focused on managerial decisions, the authors collected independent samples from MTurk workers and Brazilian professionals.

We test the effects of advice and trust on risk-taking in threeonline experiments designed to elucidate under whatconditions financial advice may increase risk-taking,irrespective of advisor performance. In our study, investorsmade 100 decisions, selecting between one of two alternatives:risky or conservative. We manipulate the suggestion of anadvisor (risky vs. non-risky investments), the fee of the advice,as well as the trustworthiness of the advisor (by increasing thetransparency of the advice presented) to test the effect of theadvice on risk-taking. The results show that individualsasymmetrically follow the advice they received, with a biastowards following more risky than conservative advice.Moreover, trusted advice was more persuasive irrespective ofwhat the advisor suggested and even the fee is higher.

Decision‐makers often fail to seek or utilise advice. This study examined how the extent to which individuals perceive that they know themselves influences advice seeking and taking. Drawing from uncertainty‐identification theory, the heuristic‐systematic model, and insights from the literature on advice, we asked participants (n = 313) to rate their self‐concept clarity, and then 4 weeks later rate their confidence and completed decision scenarios in which they indicated their desire to seek out and take advice (71.6% female, M age = 20.52 years, SD = 1.50). Decision‐makers with an unclear self‐concept were more likely to seek and take advice from others, and this effect was mediated by low confidence. In addition, involvement with the decision context positively moderated the negative influence of self‐concept clarity on advice seeking and taking. That is, decision‐makers with a more unclear self‐concept sought and accepted more advice, and the effect was weaker in high‐involvement contexts. Results are explained in terms of need for information processing and sufficiency threshold within the heuristic‐systematic model. [ABSTRACT FROM AUTHOR]

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers' own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors. Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Much research currently aims to improve AI technologies and debates their societal implications. Surprisingly little effort is spent on understanding the cognitive challenges of decision augmentation with AI-based systems although these systems make it more difficult for decision makers to evaluate the correctness of system advice and to decide whether to reject or accept it. As little is known about the cognitive mechanisms that underlie such evaluations, we take an inductive approach to understand how AI advice influences physicians' decision-making process. We conducted experiments with a total of 68 novice and 12 experienced physicians who diagnosed patient cases with an AI-based system that provided both correct and incorrect advice. Based on qualitative data from think-aloud protocols, interviews, and questionnaires, we elicit five decision-making patterns and develop a process model of medical diagnosis decision augmentation with AI advice. We show that physicians use second-order cognitive processes, namely metacognitions, to monitor and control their reasoning while assessing AI advice. These metacognitions determine whether physicians are able to reap the full benefits of AI or not. Specifically, wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers' own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial information search. Our findings provide a first perspective on the metacognitive mechanisms that decision makers use to evaluate system advice. Overall, our study sheds light on an overlooked facet of decision augmentation with AI, namely, the crucial role of human actors in compensating for technological errors. [ABSTRACT FROM AUTHOR]

In this meta-analysis, we investigated whether people with narcissistic traits are less likely than others to take advice. Additionally, we checked whether this would be independent of the advice giver's expertise and explored other potential moderators. Previously published and unpublished research has produced contradictory results, which motivated this pre-registered meta-analysis (k = 11, total number of participants = 2697) on the relation between narcissism and advice taking. We found a stable and robust, small, negative relation. Neither expertise of the advice giver nor other moderators were significant. We included robustness checks regarding participants, report characteristics, as well as operationalization of narcissism and advice taking. Theoretical, methodological and practical implications (for example, possible consequences of having people with narcissistic traits in positions of responsibility) are discussed and future research directions are outlined. • People high in narcissism take less advice than others. • The finding that narcissists take less advice than others is stable and robust. [ABSTRACT FROM AUTHOR]

Following up on a recent debate, we examined advice taking in dyads compared to individuals in a set of three studies (total N = 303 dyads and 194 individuals). Our first aim was to test the replicability of an important previous finding, namely that dyads heed advice less than individuals because they feel more confident in the accuracy of their initial judgments. Second, we aimed to explain dyads’ behavior based on three premises: first, that dyads understand that the added value of an outside opinion diminishes when the initial pre-advice judgment is made by two judges rather than one judge (given that the dyad members’ opinions are independent of each other); second, that they fail to recognize when the assumption of independence of opinions does not hold; and third, that the resistance to advice commonly observed in individuals persists in groups but is neither aggravated nor ameliorated by the group context. The results of our studies show consistently that previous findings on advice taking in dyads are replicable. They also support our hypothesis that groups exhibit a general tendency to heed advice less than individuals, irrespective of whether the accuracy of their initial judgments warrants this behavior. Finally, based on the three assumptions mentioned above, we were able to make accurate predictions about advice taking in dyads, prompting us to postulate a general model of advice taking in groups of arbitrary size.

Advice is a powerful means to improve peoples' judgments and decisions. Because advice quality is rarely apparent, decision‐makers must infer it from the characteristics of the advisor or the advice itself. Here, we focus on a largely neglected advice characteristic that should signal quality: advice precision. In a preregistered, high‐powered study (N = 195), we tested the effects of advice precision on advice taking. Drawing from past research and theorizing on anchor precision, we derived and tested two competing hypotheses for the relation of advice precision and advice taking—one predicting a monotone increase in advice taking when advice precision increases and the other predicting a backfiring effect of overly precise advice resulting in an inverted U‐shape. Our results support the notion of a monotone, albeit not a strong monotone, relationship. Higher perceived advice quality correlated with individuals' advice taking. Consistent with the idea that advice precision serves as a cue for advice quality, the effect of advice precision on advice taking was statistically mediated by perceived advice quality. Although the mediation analysis does not allow for causal interpretation because we did not manipulate the mediating variable, it shows that the effect of advice precision on advice taking is not merely a demand effect. Implications of our findings for theory and practice are discussed. [ABSTRACT FROM AUTHOR]

This paper investigates how communication influences people's judgment quality in simple estimation tasks. Except for an exchange of estimates, our design also allows the exchange of supportive evidence underlying the estimates in a controlled manner. Compared with the control treatment, the exchange of estimates and supportive evidence together improves judgment quality at both the individual level and the crowd level. On the other hand, the exchange of estimates or supportive evidence separately has no impact. [ABSTRACT FROM AUTHOR]

Owing to advancements in artificial intelligence (AI) and specifically in machine learning, information technology (IT) systems can support humans in an increasing number of tasks. Yet, previous research indicates that people often prefer human support to support by an IT system, even if the latter provides superior performance – a phenomenon called algorithm aversion. A possible cause of algorithm aversion put forward in literature is that users lose trust in IT systems they become familiar with and perceive to err, for example, making forecasts that turn out to deviate from the actual value. Therefore, this paper evaluates the effectiveness of demonstrating an AI-based system's ability to learn as a potential countermeasure against algorithm aversion in an incentive-compatible online experiment. The experiment reveals how the nature of an erring advisor (i.e., human vs. algorithmic), its familiarity to the user (i.e., unfamiliar vs. familiar), and its ability to learn (i.e., non-learning vs. learning) influence a decision maker's reliance on the advisor's judgement for an objective and non-personal decision task. The results reveal no difference in the reliance on unfamiliar human and algorithmic advisors, but differences in the reliance on familiar human and algorithmic advisors that err. Demonstrating an advisor's ability to learn, however, offsets the effect of familiarity. Therefore, this study contributes to an enhanced understanding of algorithm aversion and is one of the first to examine how users perceive whether an IT system is able to learn. The findings provide theoretical and practical implications for the employment and design of AI-based systems. [ABSTRACT FROM AUTHOR]

We examined how individuals and groups behave in making judgmental forecasts when they are given external forecast advice. We compare individual and group advice‐taking behavior under different conditions: (a) when advice quality is fixed, (b) when advice quality is randomly varied, and (c) when there is feedback on advice quality or not. Participants in Study 1 received fixed advice of either reasonable or unreasonable quality while making their decisions. Participants in Study 2 randomly received both reasonable and unreasonable advice. We found in both studies that groups feel more confident than individuals. This greater confidence decreased the groups' reliance on advice. We also found that groups are better than individuals at discerning the quality of advice. In the group treatment, the group's reliance on advice increased according to the degree of disagreement with the initial decisions of the group members. In Study 3, participants randomly received both reasonable and unreasonable advice, and in addition, they received feedback on actual realizations that enabled them to learn about the quality of advice. In the presence of feedback on random advice quality, groups are no longer less receptive to advice than individuals; with feedback, both individuals and groups discount advice more than they do without feedback. Nevertheless, groups are still better than individuals at discerning the quality of advice. We conclude that group forecasting is better than individual forecasting across various conditions that we investigate except when advice quality is known to be consistently reliable. [ABSTRACT FROM AUTHOR]

Jumping to conclusions and bias against disconfirmatory evidence are two cognitive biases common in people with psychotic-like experiences and psychosis. However, many participants show comprehension problems doing traditional tasks; new paradigms with additional applied scenarios are thus needed. A large MTurk community sample (N = 1422) was recruited and subdivided into participants with high levels of psychotic-like experiences (at least 2 SD above the mean, n = 79) and participants with low levels of psychotic-like experiences (maximum 0.5 SD above the mean, n = 1110), based on the positive subscale of the Community Assessment of Psychic Experiences (CAPE). In the context of a judge-advisor system, participants made an initial estimate and then received advice that was either confirmatory or disconfirmatory. Participants then gave a new, possibly revised estimate and were allowed to seek additional advice. Participants with high levels of psychotic-like experiences gave their final assessment after receiving significantly less advice and were significantly more confident in their decision than participants with low psychotic-like experiences, in line with previous studies on jumping to conclusions and overconfidence. Contrary to the hypothesis and earlier studies, however, no deficit in belief revision was found. In fact, participants with high psychotic-like experiences weighted advice significantly higher in the condition with disconfirmatory advice, but only for the first advice they received. The increased weighting of a single piece of disconfirmatory advice can be explained by the hypersalience of evidence-hypothesis matches theory, according to which more weight is attached to the most recently available information. [ABSTRACT FROM AUTHOR]

We investigated whether Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) influences social validation as measured by a Judge-Advisor task. In contrast to healthy controls and patients with their DBS OFF, patients with their stimulation switched on do not experience a gain of confidence after receiving competent advice. [ABSTRACT FROM AUTHOR]

Selecting good sources of information is a critical skill to navigate our highly social world. To evaluate the epistemic reputation of potential sources, the main criterion should be the relevance of the information they provide us. In two online experiments (N = 801), we found that receivers are more thankful toward, deem more competent, and are more likely to request information in the future from sources of more relevant messages—if they know the message to be accurate or deem it plausible. To prevent sources from presenting information as more relevant than it is in order to improve their reputation, receivers lower the reputation of sources sending messages that are more relevant-if-true, if they know the message to be inaccurate. Our research sheds light on the reputational trade-offs involved in choosing what information to communicate and helps explain transmission patterns such as rumors diffusion. [ABSTRACT FROM AUTHOR]

Previous research on advice taking has explained the failure to exploit collective wisdom in terms of the egocentric underweighting of advice provided by independent others. The present research is concerned with an opposite and more radical source of irrational advice taking, namely, the failure to critically assess the validity of advice due to metacognitive myopia. Participants could use the advice of one or two experts when estimating health risks. They read sketches of the study samples that experts had drawn to estimate conditional probabilities (e.g., of HIV‐given drug addiction). Whether samples were valid or seriously biased, subsequent judgments were strongly affected by any advice (Experiment 1). Uncritical reliance on any advice persisted when participants were sensitized to the contrast of valid and invalid advice in a repeated measures design (Experiment 2), when participants themselves believed advice not to be valid (Experiment 3), and even after full debriefing about invalid advice (Experiment 4). Lay advice exerted a similar influence as expert advice (Experiment 5). Although these provocative results are independent of numeracy and consensus (Experiment 6), they highlight the impact of metacognitive myopia as an impediment of social rationality. [ABSTRACT FROM AUTHOR]

To use ERPs to investigate the impact of advice distance on advice taking in the Judge-Advisor System (JAS) paradigm, behavioural and ERP data were recorded from 20 subjects. The behavioural discrepancies and EEG characteristics of advice taking, as affected by the advice distances, were compared. The results showed that the relationship between the modification rate of a decision maker's initial estimation and the advice distance exhibited an inverse U-shaped curve. During the advice evaluation stage, different advice distances induced the feedback-related negativity (FRN) and subsequent P300 components. The amplitude of the FRN increased as the advice distance increased, while the amplitude of P300 in the zero distance condition was significantly larger than those in the intermediate and far distance conditions. These findings indicate that the advice evaluation process of a decision maker is related to conflict perception, prediction error and motivational or affective significance. • A inversely U-shaped relationship between advice distance and the degree of advice taking was found. • The amplitude of the FRN increased and that of the P300 gradually decreased as the advice distance increased. • Advice evaluation process of a decision maker is related to conflict perception, prediction error and motivational or affective significance. [ABSTRACT FROM AUTHOR]

Following up on a recent debate, we examined advice taking in dyads compared to individuals in a set of three studies (total N = 303 dyads and 194 individuals). Our first aim was to test the replicability of an important previous finding, namely that dyads heed advice less than individuals because they feel more confident in the accuracy of their initial judgments. Second, we aimed to explain dyads' behavior based on three premises: first, that dyads understand that the added value of an outside opinion diminishes when the initial pre-advice judgment is made by two judges rather than one judge (given that the dyad members' opinions are independent of each other); second, that they fail to recognize when the assumption of independence of opinions does not hold; and third, that the resistance to advice commonly observed in individuals persists in groups but is neither aggravated nor ameliorated by the group context. The results of our studies show consistently that previous findings on advice taking in dyads are replicable. They also support our hypothesis that groups exhibit a general tendency to heed advice less than individuals, irrespective of whether the accuracy of their initial judgments warrants this behavior. Finally, based on the three assumptions mentioned above, we were able to make accurate predictions about advice taking in dyads, prompting us to postulate a general model of advice taking in groups of arbitrary size. [ABSTRACT FROM AUTHOR]

Highlights • Adolescents are more easily influenced than children and adults. • Adolescents rely more strongly on their own experience and explore more. • Computational models reveal intricate interaction between advice and experience. Abstract Advice taking helps one to quickly acquire knowledge and make decisions. This age-comparative study (in children [8- to 10-year-olds], adolescents [13- to 15-year-olds], and adults [18- to 22-year-olds]) investigated developmental differences in how advice, experience, and exploration influence learning. The results showed that adolescents were initially easily swayed to follow peer advice but also switched more rapidly to exploring alternatives like children. Whereas adults stayed with the advice over the task, adolescents put more weight on their own experience compared with adults. A social learning model showed that although social influence most strongly affects adolescents' initial expectations (i.e., their priors), adolescents showed higher exploration and discovered the other good option in the current task. Thus, our model resolved the apparently conflicting findings of adolescents being more and less sensitive to peer influence and provides novel insights into the dynamic interaction between social and individual learning. [ABSTRACT FROM AUTHOR]