Frequent winners explain apparent skewness preferences in experience-based decisions.

Do people's attitudes toward the (a)symmetry of an outcome distribution affect their choices? Financial investors seek return distributions with frequent small returns but few large ones, consistent with leading models of choice in economics and finance that assume right-skewed preferences. In contrast, many experiments in which decision-makers learn about choice options through experience find the opposite choice tendency, in favor of left-skewed options. To reconcile these seemingly contradicting findings, the present work investigates the effect of skewness on choices in experience-based decisions. Across seven studies, we show that apparent preferences for left-skewed outcome distributions are a consequence of those distributions having a higher value in most direct outcome comparisons, a "frequent-winner effect." By manipulating which option is the frequent winner, we show that choice tendencies for frequent winners can be obtained even with identical outcome distributions. Moreover, systematic choice tendencies in favor of right- or left-skewed options can be obtained by manipulating which option is experienced as the frequent winner. We also find evidence for an intrinsic preference for right-skewed outcome distributions. The frequent-winner phenomenon is robust to variations in outcome distributions and experimental paradigms. These findings are confirmed by computational analyses in which a reinforcement-learning model capturing frequent winning and intrinsic skewness preferences provides the best account of the data. Our work reconciles conflicting findings of aggregated behavior in financial markets and experiments and highlights the need for theories of decision-making sensitive to joint outcome distributions of the available options.

Uncovering the semantics of concepts using GPT-4.

The ability of recent Large Language Models (LLMs) such as GPT-3.5 and GPT-4 to generate human-like texts suggests that social scientists could use these LLMs to construct measures of semantic similarity that match human judgment. In this article, we provide an empirical test of this intuition. We use GPT-4 to construct a measure of typicality-the similarity of a text document to a concept. We evaluate its performance against other model-based typicality measures in terms of the correlation with human typicality ratings. We conduct this comparative analysis in two domains: the typicality of books in literary genres (using an existing dataset of book descriptions) and the typicality of tweets authored by US Congress members in the Democratic and Republican parties (using a novel dataset). The typicality measure produced with GPT-4 meets or exceeds the performance of the previous state-of-the art typicality measure we introduced in a recent paper [G. Le Mens, B. Kovács, M. T. Hannan, G. Pros Rius, Sociol. Sci. 2023, 82-117 (2023)]. It accomplishes this without any training with the research data (it is zero-shot learning). This is a breakthrough because the previous state-of-the-art measure required fine-tuning an LLM on hundreds of thousands of text documents to achieve its performance.

How Endogenous Crowd Formation Undermines the Wisdom of the Crowd in Online Ratings.

People frequently consult average ratings on online recommendation platforms before making consumption decisions. Research on the wisdom-of-the-crowd phenomenon suggests that average ratings provide unbiased quality estimates. Yet we argue that the process by which average ratings are updated creates a systematic bias. In analyses of more than 80 million online ratings, we found that items with high average ratings tend to attract more additional ratings than items with low average ratings. We call this asymmetry in how average ratings are updated endogenous crowd formation. Using computer simulations, we showed that it implies the emergence of a negative bias in average ratings. This bias affects items with few ratings particularly strongly, which leads to ranking mistakes. The average-rating rankings of items with few ratings are worse than their quality rankings. We found evidence for the predicted pattern of biases in an experiment and in analyses of large online-rating data sets.

The Evaluative Advantage of Novel Alternatives: An Information-Sampling Account.

New products, services, and ideas are often evaluated more favorably than similar but older ones. Although several explanations of this phenomenon have been proposed, we identify an overlooked asymmetry in information about new and old items that emerges when people seek positive experiences and learn about the qualities of (noisy) alternatives by experiencing them. The reason for the asymmetry is that people avoid rechoosing alternatives that previously led to poor outcomes; hence, additional feedback on their qualities is precluded. Negative quality estimates, even when caused by noise, thus tend to persist. This negative bias takes time to develop, and affects old alternatives more strongly than similar but newer alternatives. We analyze a simple learning model and demonstrate the process by which people would tend to evaluate a new alternative more positively than an older alternative with the same payoff distribution. The results from two experimental studies (Ns = 769 and 805) support the predictions of our model.

Keeping conceptual boundaries distinct between decision making and learning is necessary to understand social influence.

Bentley et al. make the deliberate choice to blur the distinction between learning and decision making. This obscures the social influence mechanisms that operate in the various empirical settings that their map aims to categorize. Useful policy prescriptions, however, require an accurate understanding of the social influence mechanisms that underlie the dynamics of popularity.

Correction to "Evaluating categories from experience: The simple averaging heuristic" by Woiczyk and Le Mens (2021).

Reports an error in "Evaluating categories from experience: The simple averaging heuristic" by Thomas K. A. Woiczyk and Gaël Le Mens (Journal of Personality and Social Psychology, 2021[Oct], Vol 121[4], 747-773). There was an error in Figure 7. In the two plots of the second row, the data previously labeled as "Equal" correspond to "Natural" and the data previously labeled "Natural" correspond to "Equal." The online version of this article has been corrected. (The following abstract of the original article appeared in record 2021-89474-001.) We analyze how people form evaluative judgments about categories based on their experiences with category members. Prior research suggests that such evaluative judgments depend on some experience average but is unclear about the specific kind of average. We hypothesized that evaluations of categories could be driven either by the simple average of experiences with the category or by the member average (the average of the evaluations of the category members, where the evaluation of a category member is the average of experiences with this particular member). Understanding whether evaluations of categories are driven by the simple average or the member average is important in settings where people obtain unbalanced numbers of observations about category members such as when people form opinions about a social group and predominantly interact with just a few members of this group. Across nine studies (N = 1,966), we consistently found that evaluative judgments about categories were better explained by the simple average than by the member average. We call the underlying cognitive strategy the simple averaging heuristic. Collected evidence indicates that participants relied on simple averaging even in settings where normative principles required avoiding the use of this cognitive strategy, leading to systematic mistakes. Our findings contribute to several areas of social cognition such as research on redundancy biases, information aggregation, social sampling, and norm perceptions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Social media feedback and extreme opinion expression.

On popular social media platforms such as Twitter, Facebook, Instagram, or Tiktok, the quantitative feedback received by content producers is asymmetric: counts of positive reactions such as 'likes,' or 'retweets,' are easily observed but similar counts of negative reactions are not directly available. We study how this design feature of social media platforms affects the expression of extreme opinions. Using simulations of a learning model, we compare two feedback environments that differ in terms of the availability of negative reaction counts. We find that expressed opinions are generally more extreme when negative reaction counts are not available than when they are. We rely on analyses of Twitter data and several online experiments to provide empirical support for key model assumptions and test model predictions. Our findings suggest that a simple design change might limit, under certain conditions, the expression of extreme opinions on social media.

How adoption speed affects the abandonment of cultural tastes.

Products, styles, and social movements often catch on and become popular, but little is known about why such identity-relevant cultural tastes and practices die out. We demonstrate that the velocity of adoption may affect abandonment: Analysis of over 100 years of data on first-name adoption in both France and the United States illustrates that cultural tastes that have been adopted quickly die faster (i.e., are less likely to persist). Mirroring this aggregate pattern, at the individual level, expecting parents are more hesitant to adopt names that recently experienced sharper increases in adoption. Further analysis indicate that these effects are driven by concerns about symbolic value: Fads are perceived negatively, so people avoid identity-relevant items with sharply increasing popularity because they believe that they will be short lived. Ancillary analyses also indicate that, in contrast to conventional wisdom, identity-relevant cultural products that are adopted quickly tend to be less successful overall (i.e., reduced cumulative adoption). These results suggest a potential alternate way to explain diffusion patterns that are traditionally seen as driven by saturation of a pool of potential adopters. They also shed light on one factor that may lead cultural tastes to die out.

Evaluating categories from experience: The simple averaging heuristic.

We analyze how people form evaluative judgments about categories based on their experiences with category members. Prior research suggests that such evaluative judgments depend on some experience average but is unclear about the specific kind of average. We hypothesized that evaluations of categories could be driven either by the simple average of experiences with the category or by the member average (the average of the evaluations of the category members, where the evaluation of a category member is the average of experiences with this particular member). Understanding whether evaluations of categories are driven by the simple average or the member average is important in settings where people obtain unbalanced numbers of observations about category members such as when people form opinions about a social group and predominantly interact with just a few members of this group. Across nine studies (N = 1,966), we consistently found that evaluative judgments about categories were better explained by the simple average than by the member average. We call the underlying cognitive strategy the simple averaging heuristic. Collected evidence indicates that participants relied on simple averaging even in settings where normative principles required avoiding the use of this cognitive strategy, leading to systematic mistakes. Our findings contribute to several areas of social cognition such as research on redundancy biases, information aggregation, social sampling, and norm perceptions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

An information sampling explanation for the in-group heterogeneity effect.

People often perceive their in-groups as more heterogeneous than their out-groups. We propose an information sampling explanation for this in-group heterogeneity effect. We note that people frequently obtain larger samples of information about in-groups than about out-groups. Using computer simulations, we show that this asymmetry in sample sizes implies the in-group heterogeneity effect under a wide range of assumptions about how experience affects perceived variability. This is the case even when perceived variability is the outcome of rational information processing, implying that the structure of the environment is sufficient to explain the emergence of the in-group heterogeneity effect. A key assumption of our explanation is that perceived group variability depends on the size of the sample observed about this group. We provide evidence in support for this assumption in two experiments. Our results considerably expand the scope and relevance of a prior sampling explanation proposed by Linville, Fischer, and Salovey (1989). They also complement other explanations that proposed that information about in-groups and out-groups is processed differently. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Information Sampling, Judgment, and the Environment: Application to the Effect of Popularity on Evaluations.

If people avoid alternatives they dislike, a negative evaluative bias emerges because errors of under-evaluation are unlikely to be corrected. Prior work that analyzed this mechanism has shown that when the social environment exposes people to avoided alternatives (i.e., it makes them resample them), then evaluations can become systematically more positive. In this paper, we clarify the conditions under which this happens. By analyzing a simple learning model, we show that whether additional exposures induced by the social environment lead to more positive or more negative evaluations depends on how prior evaluations and the social environment interact in driving resampling. We apply these insights to the study of the effect of popularity on evaluations. We show theoretically that increased popularity leads to more positive evaluations when popularity mainly increases the chances of resampling for individuals with low current evaluations. Data on repeat stays at hotels are consistent with this condition: The popularity of a hotel mainly impacts the chances of a repeat stay for individuals with low satisfaction scores. Our results illustrate how a sampling approach can help to explain when and why people tend to like popular alternatives. They also shed new light on the polarization of attitudes across social groups.

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

Interdependent sampling and social influence.

Most explanations of social influence focus on why individuals might want to agree with the opinions or attitudes of others. The authors propose a different explanation that assumes the attitudes of others influence only the activities and objects individuals are exposed to. For example, individuals are likely to be exposed to activities that their friends enjoy. The authors demonstrate that such influence over sampling behavior is sufficient to produce a social influence effect when individuals form attitudes by learning from experience. Even if the experiences of 2 individuals, when they sample an object or event, are independent random variables, their attitudes will become positively correlated if their sampling processes are interdependent. Interdependent sampling of activities thus provides a different explanation of social influence with distinct empirical and theoretical implications.

Seeking positive experiences can produce illusory correlations.

Individuals tend to select again alternatives about which they have positive impressions and to avoid alternatives about which they have negative impressions. Here we show how this sequential sampling feature of the information acquisition process leads to the emergence of an illusory correlation between estimates of the attributes of multi-attribute alternatives. The sign of the illusory correlation depends on how the decision maker combines estimates in making her sampling decisions. A positive illusory correlation emerges when evaluations are compensatory or disjunctive and a negative illusory correlation can emerge when evaluations are conjunctive. Our theory provides an alternative explanation for illusory correlations that does not rely on biased information processing nor selective attention to different pieces of information. It provides a new perspective on several well-established empirical phenomena such as the 'Halo' effect in personality perception, the relation between proximity and attitudes, and the in-group out-group bias in stereotype formation.

Rational learning and information sampling: on the "naivety" assumption in sampling explanations of judgment biases.

Recent research has argued that several well-known judgment biases may be due to biases in the available information sample rather than to biased information processing. Most of these sample-based explanations assume that decision makers are "naive": They are not aware of the biases in the available information sample and do not correct for them. Here, we show that this "naivety" assumption is not necessary. Systematically biased judgments can emerge even when decision makers process available information perfectly and are also aware of how the information sample has been generated. Specifically, we develop a rational analysis of Denrell's (2005) experience sampling model, and we prove that when information search is interested rather than disinterested, even rational information sampling and processing can give rise to systematic patterns of errors in judgments. Our results illustrate that a tendency to favor alternatives for which outcome information is more accessible can be consistent with rational behavior. The model offers a rational explanation for behaviors that had previously been attributed to cognitive and motivational biases, such as the in-group bias or the tendency to prefer popular alternatives.