bluuebunny/crossref_metadata_2025_split huggingface dataset:
The role of actively open-minded thinking in information acquisition, accuracy, and calibration
Authors: Haran Uriel , Ritov Ilana , Mellers Barbara A. 
Published: January 2023

AbstractErrors in estimating and forecasting often result from the failure to collect and consider enough relevant information. We examine whether attributes associated with persistence in information acquisition can predict performance in an estimation task. We focus on actively open-minded thinking (AOT), need for cognition, grit, and the tendency to maximize or satisfice when making decisions. In three studies, participants made estimates and predictions of uncertain quantities, with varying levels of control over the amount of information they could collect before estimating. Only AOT predicted performance. This relationship was mediated by information acquisition: AOT predicted the tendency to collect information, and information acquisition predicted performance. To the extent that available information is predictive of future outcomes, actively open-minded thinkers are more likely than others to make accurate forecasts.
Enactment of one-to-many communication may induce self-focused attention that leads to diminished perspective taking: The case of Facebook
Authors: Chiou Wen-Bin , Lee Chun-Chia 
Published: January 2023

AbstractSocial networking sites (SNSs) provide users with an efficient interface for distributing information, such as photos or wall posts, to many others simultaneously. We demonstrated experimentally that this type of indiscriminate one-to-many (i.e., monologue) communication may induce self-focused attention and thereby impair perspective taking. The present study used multiple paradigms to explore the link between engaging in online one-to-many communication and a decrease in perspective taking. Experiment 1 revealed that Facebookers who published a personal photo to the public or their friends were less likely to adopt another person’s visual perspective than were those in the control group. Experiment 2 showed that Facebookers who engaged in indiscriminate one-to-many wall posting were more likely than those in the control group to rely heavily on their own perspectives. A state of self-focus, as measured by greater Stroop interference in naming the color of self-relevant versus neutral words, mediated the detrimental effect of indiscriminate one-to-many communication on cognitive perspective taking. These findings suggest that indiscriminate one-to-many communication on SNSs may promote public self-focus, leading to self-referential processing when making social judgments. Online monologue communication may be more harmful to perspective taking than previously understood.
The value of a smile: Facial expression affects ultimatum-game responses
Authors: Mussel Patrick , Göritz Anja S. , Hewig Johannes 
Published: January 2023

AbstractIn social interaction, the facial expression of an opponent contains information that may influence the interaction. We asked whether facial expression affects decision-making in the ultimatum game. In this two-person game, the proposer divides a sum of money into two parts, one for each player, and then the responder decides whether to accept the offer or reject it. Rejection means that neither player gets any money. Results of a large-sample study support our hypothesis that offers from proposers with a smiling facial expression are more often accepted, compared to a neutral facial expression. Moreover, we found lower acceptance rates for offers from proposers with an angry facial expression.
Revealed strength of preference: Inference from response times
Authors: Konovalov Arkady , Krajbich Ian 
Published: January 2023

AbstractRevealed preference is the dominant approach for inferring preferences, but it is limited in that it relies solely on discrete choice data. When a person chooses one alternative over another, we cannot infer the strength of their preference or predict how likely they will be to make the same choice again. However, the choice process also produces response times (RTs), which are continuous and easily observable. It has been shown that RTs often decrease with strength-of-preference. This is a basic property of sequential sampling models such as the drift diffusion model. What remains unclear is whether this relationship is sufficiently strong, relative to the other factors that affect RTs, to allow us to reliably infer strength-of-preference across individuals. Using several experiments, we show that even when every subject chooses the same alternative, we can still rank them based on their RTs and predict their behavior on other choice problems. We can also use RTs to predict whether a subject will repeat or reverse their decision when presented with the same choice problem a second time. Finally, as a proof-of-concept, we demonstrate that it is also possible to recover individual preference parameters from RTs alone. These results demonstrate that it is indeed possible to use RTs to infer preferences.
A universal method for evaluating the quality of aggregators
Authors: Han Ying , Budescu David 
Published: January 2023

AbstractWe propose a new method to facilitate comparison of aggregated forecasts based on different aggregation, elicitation and calibration methods. Aggregates are evaluated by their relative position on the cumulative distribution of the corresponding individual scores. This allows one to compare methods using different measures of quality that use different scales. We illustrate the use of the method by re-analyzing various estimates from Budescu and Du (Management Science, 2007).
Using metacognitive cues to infer others’ thinking
Authors: Mata André , Almeida Tiago 
Published: January 2023

AbstractThree studies tested whether people use cues about the way other people think—for example, whether others respond fast vs. slow—to infer what responses other people might give to reasoning problems. People who solve reasoning problems using deliberative thinking have better insight than intuitive problem-solvers into the responses that other people might give to the same problems. Presumably because deliberative responders think of intuitive responses before they think of deliberative responses, they are aware that others might respond intuitively, particularly in circumstances that hinder deliberative thinking (e.g., fast responding). Intuitive responders, on the other hand, are less aware of alternative responses to theirs, so they infer that other people respond as they do, regardless of the way others respond.
The relationship between crowd majority and accuracy for binary decisions
Authors: Lee Michael D. , Lee Megan N. 
Published: January 2023

AbstractWe consider the wisdom of the crowd situation in which individuals make binary decisions, and the majority answer is used as the group decision. Using data sets from nine different domains, we examine the relationship between the size of the majority and the accuracy of the crowd decisions. We find empirically that these calibration curves take many different forms for different domains, and the distribution of majority sizes over decisions in a domain also varies widely. We develop a growth model for inferring and interpreting the calibration curve in a domain, and apply it to the same nine data sets using Bayesian methods. The modeling approach is able to infer important qualitative properties of a domain, such as whether it involves decisions that have ground truths or are inherently uncertain. It is also able to make inferences about important quantitative properties of a domain, such as how quickly the crowd accuracy increases as the size of the majority increases. We discuss potential applications of the measurement model, and the need to develop a psychological account of the variety of calibration curves that evidently exist.
FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees
Authors: Phillips Nathaniel D. , Neth Hansjörg , Woike Jan K. , Gaissmaier Wolfgang 
Published: January 2023

AbstractFast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use.
The value of vulnerability: The transformative capacity of risky trust
Authors: Bruni Luigino , Tufano Fabio 
Published: January 2023

AbstractIn an experimental gift-exchange game, we explore the transformative capacity of vulnerable trust, which we define as trusting untrustworthy players when their untrustworthiness is common knowledge between co-players. In our experiment, there are two treatments: the “Information” treatment and the “No-Information” treatment in which we respectively disclose or not information about trustees’ trustworthiness. Our laboratory evidence consistently supports the transformative capacity of trustors’ vulnerable trust, which generates higher transfers, more trustworthiness and increased reciprocity by untrustworthy trustees.
Normative arguments from experts and peers reduce delay discounting
Authors: Senecal Nicole , Wang Teresa , Thompson Elizabeth , Kable Joseph W. 
Published: January 2023

AbstractWhen making decisions that involve tradeoffs between the quality and timing of desirable outcomes, people consistently discount the value of future outcomes. A puzzling finding regarding such decisions is the extremely high rate at which people discount future monetary outcomes. Most economists would argue that decision-makers should turn down only rates of return that are lower than those available to them elsewhere. Yet the vast majority of studies find discount rates that are significantly higher than market interest rates (Frederick et al., 2002). Here we ask whether a lack of knowledge about the normative strategy can explain high discount rates. In an initial experiment, nearly half of subjects did not spontaneously cite elements of the normative strategy when asked how people should make intertemporal monetary decisions. In two follow-up experiments, after subjects read a “financial guide” detailing the normative strategy, discount rates declined by up to 85%, but were still higher than market interest rates. This decline persisted, though attenuated, for at least one month. In a final experiment, peer-generated advice influenced discount rates in a similar manner to “expert” advice, and arguments focusing on normative considerations were at least as effective as others. These studies show that part of the explanation for high discount rates is a lack of knowledge regarding the normative strategy, and they quantify how much discount rates are reduced in response to normative arguments. Given the high level of discounting that remains, however, there are other contributing factors to high discount rates that remain to be quantified.
A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning
Authors: Kary Arthur , Hawkins Guy E. , Hayes Brett K. , Newell Ben R. 
Published: January 2023

AbstractPeople often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to improve understanding of stock-flow systems have been limited by the lack of a principled approach to identifying and measuring individual differences in reasoning strategies. We present a principled inferential method known as Hierarchical Bayesian Latent Mixture Models (HBLMMs) to analyze stock-flow reasoning. HBLMMs use Bayesian inference to classify different patterns of responding as coming from multiple latent populations. We demonstrate the usefulness of this approach using a dataset from a stock-flow drawing task which compared performance in a problem presented in a climate change context, a problem in a financial context, and a problem in which the financial context was used as an analogy to assist understanding in the climate problem. The hierarchical Bayesian model showed that the proportion of responses consistent with the “correlation heuristic” was lower in the financial context and financial analogy context than in the pure climate context. We discuss the benefits of HBLMMs and implications for the role of contexts and analogy in improving stock-flow reasoning.
Valuing bets and hedges
Authors: Chatterjee Subimal , Mookherjee Satadruta 
Published: January 2023

AbstractTwo studies replicate the anomaly identified by Frederick, Meyer and Levis (2015) and Frederick, Levis, Malliaris and Meyer (2018). People show typical risk averse behavior by valuing risk below the focal lottery’s expected value, but they do not bid above its expected value for the hedge that eliminates the risk. Following the authors, we conduct finer analyses by separating participants into two groups – “experts” who understand that acquiring the hedge makes winning certain versus “novices” who do not understand the winning implications of acquiring the hedge. We find that (1) “experts” are more inclined to purchase the hedge compared to the “novices” and (2) unlike the “novices,” they value the hedge significantly more than the risk instrument, but only if they are given the risk instrument free of charge. However, even there, the hedge valuations are significantly less than the lottery’s expected value suggesting that the anomaly described in Frederick et al. (2015, 2018) is robust and likely to affect the way our discipline conceptualizes and models risk behavior.
Moderators of framing effects in variations of the Asian Disease problem: Time constraint, need, and disease type
Authors: Diederich Adele , Wyszynski Marc , Ritov Ilana 
Published: January 2023

AbstractThis study examined framing effects in decisions concerning public health. Tversky and Kahneman’s famous Asian Disease Problem served as experimental paradigm. Subjects chose between a sure and a risky option either presented as gains (saving lives) or as losses (dying). The amount of risk varied in terms of different probabilities. The number of affected people was either small (low need) or large (high need). Additionally, the decisions were linked to three different types of diseases (unusual infection, AIDS, leukemia). We also implemented two different time constraints during which the subjects had to give a response. Finally, we tested a within-subject design. The data analysis assuming a linear mixed effects model revealed significant effects of framing, probabilities, and need. Furthermore, the type of disease and time constraints were moderating the framing effect. Across the different diseases, framing effects were amplified when decision time was short.
Bayesian methods for analyzing true-and-error models
Authors: Lee Michael D. 
Published: January 2023

AbstractBirnbaum and Quispe-Torreblanca (2018) evaluated a set of six models developed under true-and-error theory against data in which people made choices in repeated gambles. They concluded the three models based on expected utility theory were inadequate accounts of the behavioral data, and argued in favor of the simplest of the remaining three more general models. To reach these conclusions, they used non-Bayesian statistical methods: frequentist point estimation of parameters, bootstrapped confidence intervals of parameters, and null hypothesis significance testing of models. We address the same research goals, based on the same models and the same data, using Bayesian methods. We implement the models as graphical models in JAGS to allow for computational Bayesian analysis. Our results are based on posterior distribution of parameters, posterior predictive checks of descriptive adequacy, and Bayes factors for model comparison. We compare the Bayesian results with those of Birnbaum and Quispe-Torreblanca (2018). We conclude that, while the very general conclusions of the two approaches agree, the Bayesian approach offers better detailed answers, especially for the key question of the evidence the data provide for and against the competing models. Finally, we discuss the conceptual and practical advantages of using Bayesian methods in judgment and decision making research highlighted by this case study.
Numerosity and allocation behavior: Insights using the dictator game
Authors: Shrivastava Sunaina , Jain Gaurav , Nayakankuppam Dhananjay , Gaeth Gary J. , Levin Irwin P 
Published: January 2023

AbstractThis paper investigates how the numerosity bias influences individuals’ allocation of resources between themselves and others, using the backdrop of the traditional dictator game. Across four studies including both hypothetical and real exchanges of money, we show that the form of the numerical value representing the quantity of the resource (e.g., $20 vs 2000 cents) systematically biases the decision-maker to perceive the quantity s/he is thinking of allocating as being “less than adequate” or “more than adequate”. Essentially, such a biased perception of adequacy with respect to the quantity of the resource consequently influences the decision-maker’s final allocation decision. We attribute this systematic bias to the “numerosity” of the resource. We find that bigger numerical values representing quantity (e.g., 2000 cents) bias decision-makers to over-infer the quantity, thus inducing them to allocate less to the entities they are focusing on.
Further evidence for the memory state heuristic: Recognition latency predictions for binary inferences
Authors: Castela Marta , Erdfelder Edgar 
Published: January 2023

AbstractAccording to the recognition heuristic (RH), for decision domains where recognition is a valid predictor of a choice criterion, recognition alone is used to make inferences whenever one object is recognized and the other is not, irrespective of further knowledge. Erdfelder, Küpper-Tetzel, and Mattern (2011) questioned whether the recognition judgment itself affects decisions or rather the memory strength underlying it. Specifically, they proposed to extend the RH to the memory state heuristic (MSH), which assumes a third memory state of uncertainty in addition to recognition certainty and rejection certainty. While the MSH already gathered significant support, one of its basic and more counterintuitive predictions has not been tested so far: In guessing pairs (none of the objects recognized), the object more slowly judged as unrecognized should be preferred, since it is more likely to be in a higher memory state. In this paper, we test this prediction along with other recognition latency predictions of the MSH, thereby adding to the body of research supporting the MSH.
Ambiguity and expectation-neglect in dilemmas of interpersonal trust
Authors: Evans Anthony M. , Krueger Joachim I. 
Published: January 2023

AbstractRecent research suggests that people discount or neglect expectations of reciprocity in trust dilemmas. We examine the underlying processes and boundary conditions of this effect, finding that expectations have stronger effects on trust when they are made accessible and when they are provided as objective probabilities (Study 1). Objective expectations have stronger effects when they are based on precise, rather than ambiguous, probabilities (Study 2). We also find that trust decisions differ from individual risk-taking decisions: people are more willing to trust, and expectations have stronger effects on trusting behavior (Study 2). These results show that the availability and ambiguity of expectations shape trust decisions, and that people differentially weight expectations in dilemmas of trust and individual risk-taking.
Numeric and graphic risk information processing of high and low numerates in the intuitive and deliberative decision modes: An eye-tracker study
Authors: Keller Carmen , Kreuzmair Christina , Leins-Hess Rebecca , Siegrist Michael 
Published: January 2023

AbstractThe influence of numeracy on information processing of two risk communication formats (percentage and pictograph) was examined using an eye tracker. A sample from the general population (N = 159) was used. In intuitive and deliberative decision conditions, the participants were presented with a hypothetical scenario presenting a test result. The participants indicated their feelings and their perceived risk, evoked by a 17% risk level. In the intuitive decision condition, a significant correlation (r = .30) between numeracy and the order of information processing was found: the higher the numeracy, the earlier the processing of the percentage, and the lower the numeracy, the earlier the processing of the pictograph. This intuitive, initial focus on a format prevailed over the first half of the intuitive decision-making process. In the deliberative decision condition, the correlation between numeracy and order of information processing was not significant. In both decision conditions, high and low numerates processed pictograph and percentage formats with similar depths and derived similar meanings from them in terms of feelings and perceived risk. In both conditions numeracy had no effects on the degree of attention on the percentage or the pictograph (number of fixations on formats and transitions between them). The results suggest that pictographs attract low numerates’ attention, and percentages attract high numerates’ attention in the first, intuitive, phase of numeric information processing. Pictographs thus ensure low numerates’ further elaboration on numeric risk information, which is an important precondition of risk understanding and decision making.
Prosociality in the economic Dictator Game is associated with less parochialism and greater willingness to vote for intergroup compromise
Authors: Mosleh Mohsen , Stewart Alexander J. , Plotkin Joshua B. , Rand David G. 
Published: January 2023

AbstractIs prosociality parochial or universalist? To shed light on this issue, we examine the relationship between the amount of money given to a stranger (giving in an incentivized Dictator Game) and intergroup attitudes and behavior in the context of randomly assigned teams (a minimal group paradigm) amongN= 4,846 Amazon Mechanical Turk workers. Using a set of Dynamic Identity Diffusion Index measures, we find that participants who give more in the Dictator Game show less preferential identification with their team relative to the other team, and more identification with all participants regardless of team. Furthermore, in an incentivized Voter Game, participants who give more in the Dictator Game are more likely to support compromise by voting for the opposing team in order to avoid deadlock. Together, these results suggest that – at least in this subject pool and using these measures – prosociality is better characterized by universalism than parochialism.