Intuitive judgments of social statistics: How exhaustive does sampling need to be?

Real-world estimation taps into basic numeric abilities

Accurately estimating and assessing real-world quantities (e.g., how long it will take to get to the train station; the calorie content of a meal) is a central skill for adaptive cognition. To date, theoretical and empirical work on the mental resources recruited by real-world estimation has focused primarily on the role of domain knowledge (e.g., knowledge of the metric and distributional properties of objects in a domain). Here we examined the role of basic numeric abilities - specifically, symbolic-number mapping - in real-world estimation. In Experiment 1 ( N = 286 ) and Experiment 2 ( N = 592 ), participants first completed a country-population estimation task (a task domain commonly used to study real-world estimation) and then completed a number-line task (an approach commonly used to measure symbolic-number mapping). In both experiments, participants with better performance in the number-line task made more accurate estimates in the estimation task. Moreover, Experiment 2 showed that performance in the number-line task predicts estimation accuracy independently of domain knowledge. Further, in Experiment 2 the association between estimation accuracy and symbolic-number mapping did not depend on whether the number-line task involved small numbers (up to 1000) or large numbers that matched the range of the numbers in the estimation task (up to 100,000,000). Our results show for the first time that basic numeric abilities contribute to the estimation of real-world quantities. We discuss implications for theories of real-world estimation and for interventions aiming to improve people's ability to estimate real-world quantities.

The limits of personal experience

This article examines how three types of experience-personal, related others, and unrelated others-influence decision-making. We present the complexities and nuances in using these experiential sources to suggest that personal experience is preferred to the other two sources. We discuss the implications of this preference for decision-making processes, especially in contexts involving transformative outcomes. To conclude, we discuss how people rely on other experiential sources when their preferred source is limited.

The perception of dramatic risks: Biased media, but unbiased minds

In their famous study on risk judgments, Lichtenstein, Slovic, Fischhoff, Layman, and Combs (1978) concluded that people tend to overestimate the frequencies of dramatic causes of death (e.g., homicide, tornado) and underestimate the frequencies of nondramatic ones (e.g., diabetes, heart disease). Further, their analyses of newspapers indicated that dramatic risks are overrepresented in the media-suggesting that people's distorted risk perceptions might be driven by distortions in media coverage. Although these patterns were not evaluated statistically in the original analyses, the conclusions have become a staple in the social sciences. How reliable are they? And are they replicable? In a systematic literature search, I identified existing replications of Lichtenstein et al.'s investigation and submitted both the original data and the data from the replications to a Bayesian statistical analysis. All datasets indicated very strong evidence for an overrepresentation of dramatic risks and an underrepresentation of nondramatic risks in media coverage. However, a reliable overestimation (underestimation) of dramatic (nondramatic) risks in people's frequency judgments emerged only in Lichtenstein et al.'s dataset; it did not replicate in the other datasets. In fact, aggregated across all datasets, there was evidence for the absence of a differential distortion of dramatic and nondramatic causes of death in people's risk frequency judgments. Additional analyses suggest that when judging risk frequency, people rely on samples from their personal social networks rather than from the media. The results reveal a limited empirical basis for the common notion that distortions in people's risk judgments echo distortions in media coverage. They also suggest that processes of risk frequency judgments include a metacognitive mechanism that is sensitive to the source of mentally available samples.

Subjective Confidence as a Monitor of the Replicability of the Response

Confidence is commonly assumed to monitor the accuracy of responses. However, intriguing results, examined in the light of philosophical discussions of epistemic justification, suggest that confidence actually monitors the reliability of choices rather than (directly) their accuracy. The focus on reliability is consistent with the view that the construction of truth has much in common with the construction of reality: extracting reliable properties that afford prediction. People are assumed to make a binary choice by sampling cues from a "collective wisdomware," and their confidence is based on the consistency of these cues, in line with the self-consistency model. Here, however, I propose that internal consistency is taken to index the reliability of choices themselves-the likelihood that they will be repeated. The results of 10 studies using binary decisions from different domains indicated that confidence in a choice predicts its replicability both within individuals and across individuals. This was so for domains for which choices have a truth value and for those for which they do not. For the former domains, differences in replicability mediated the prediction of accuracy whether confidence was diagnostic or counterdiagnostic of accuracy. Metatheoretical, methodological, and practical implications are discussed.

Socializing social sampling models: The limits of explaining norm perceptions and biases with sampling from social circles

People often overestimate the prevalence of unfavorable behavior. To explain these misperceptions, social sampling models propose that individuals infer the social norm from the behavior of their own social circle. We investigated this idea by asking a friendship network of college freshmen to report their own behavior and norm perceptions across eight domains at two timepoints (N = 104). Assessing this complete social network allows to directly test if sampling from the social circle shapes norm perception. Replicating previous findings, freshmen systematically misperceived the average social norm within their cohort. Yet, these misperceptions persisted even when individuals judged their own social circle, indicating that sampling from social circles does not fully explain normative biases. Moreover, cognitive modelling of norm perceptions suggested that individuals unlikely limited their search to their own social circle.

Biased perception of distributions: Anchoring, interpolation and smoothing as potential causes

Perceiving the degree of variation in the social and non-social environment is a cognitive task that is important for many judgments and decisions. In the present research, we investigated cognitive underpinnings of how people estimate the average value of segments of a statistical distribution (e.g., what is the average income of the richest 25% of a population?). In three experiments (total N = 222), participants learned about the values of experimentally created distributions of income values and city sizes and later estimated the mean value of the four quarters of values. We expected participants to draw on heuristic shortcuts to generate such judgments. More specifically, we hypothesized that participants use the endpoints of the distributions as anchors and determine the mean values by linear interpolation. In addition, we tested the contribution of three further processes (Range-Frequency adjustments, Normal Smoothing, Linear Smoothing). Quantitative model tests suggest that anchoring and Linear Smoothing both affected mean interquartile judgments. This conclusion is corroborated by tests of qualitative predictions of the models under consideration.

Studies in Ecological Rationality

Ecological rationality represents an alternative to classic frameworks of rationality. Extending on Herbert Simon's concept of bounded rationality, it holds that cognitive processes, including simple heuristics, are not per se rational or irrational, but that their success rests on their degree of fit to relevant environmental structures. The key is therefore to understand how cognitive and environmental structures slot together. In recent years, a growing set of analyses of heuristic-environment systems has deepened the understanding of the human mind and how boundedly rational heuristics can result in successful decision making. This article is concerned with three conceptual challenges in the study of ecological rationality. First, do heuristics also succeed in dynamic contexts involving competitive agents? Second, can the mind adapt to environmental structures via an unsupervised learning process? Third, how can research go beyond mere descriptions of environmental structures to develop theories of the mechanisms that give rise to those structures? In addressing these questions, we illustrate that a successful theory of rationality will focus on the adaptive aspects of the mind and will need to account for three components: the mind's information processing, the environment to which the mind adapts, and the intersection between the environment and the mind.

Human social sensing is an untapped resource for computational social science

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

The Role of Social Circle Perceptions in "False Consensus" about Population Statistics: Evidence from a National Flu Survey

Purpose. "False consensus" refers to individuals with (v. without) an experience judging that experience as more (v. less) prevalent in the population. We examined the role of people's perceptions of their social circles (family, friends, and acquaintances) in shaping their population estimates, false consensus patterns, and vaccination intentions. Methods. In a national online flu survey, 351 participants indicated their personal vaccination and flu experiences, assessed the percentage of individuals with those experiences in their social circles and the population, and reported their vaccination intentions. Results. Participants' population estimates of vaccination coverage and flu prevalence were associated with their perceptions of their social circles' experiences, independent of their own experiences. Participants reporting less social circle "homophily" (or fewer social contacts sharing their experience) showed less false consensus and even "false uniqueness." Vaccination intentions were greater among nonvaccinators reporting greater social circle vaccine coverage. Discussion. Social circle perceptions play a role in population estimates and, among individuals who do not vaccinate, vaccination intentions. We discuss implications for the literature on false consensus, false uniqueness, and social norms interventions.

Familiarity-Matching: An Ecologically Rational Heuristic for the Relationships-Comparison Task

Previous studies have shown that people often use heuristics in making inferences and that subjective memory experiences, such as recognition or familiarity of objects, can be valid cues for inferences. So far, many researchers have used the binary choice task in which two objects are presented as alternatives (e.g., "Which city has the larger population, city A or city B?"). However, objects can be presented not only as alternatives but also in a question (e.g., "Which country is city X in, country A or country B?"). In such a situation, people can make inferences based on the relationship between the object in the question and each object given as an alternative. In the present study, we call this type of task a "relationships-comparison task." We modeled the three inference strategies that people could apply to solve it (familiarity-matching [FM; a new heuristic we propose in this study], familiarity heuristic [FH], and knowledge-based inference [KI]) to examine people's inference processes. Through Studies 1, 2, and 3, we found that (a) people tended to rely on heuristics, and that FM (inferences based on similarity in familiarity between objects) well explained participants' inference patterns; (b) FM could work as an ecologically rational strategy for the relationships-comparison task since it could effectively reflect environmental structures, and that the use of FM could be highly replicable and robust; and (c) people could sometimes use a decision strategy like FM, even in their daily lives (consumer behaviors). The nature of the relationships-comparison task and human cognitive processes is discussed.

Individually irrational pruning is essential for ecological rationality in a social context

Heuristics, commonly thought to violate the full rationality assumptions, are paradoxically indispensable parts of our decision-making and learning processes. To resolve this seemingly paradox, there have been several studies in the literature that aim at finding some broad daily life conditions and situations where employing heuristics are rational. However, these researches mainly focus on non-social conditions, whereas, for human beings, social and individual processes are interwoven and it would be better to study them jointly. Here, we study the role of pruning heuristic in individual reinforcement learning in a social context, where our simulated learning agents make many of their decisions relying on others' knowledge. Our simulation results suggest that the seemingly irrational pruning heuristic leads to less cost in the social settings. That is, we have a meaningfully more social outcome in the presence of this heuristic in social contexts, and social learning helps the agents to learn better where the pruning heuristic is an obstacle in the way of finding the optimal solution in the individual setting. In sum, the synergy between the pruning behavior and social learning leads to ecological rationality.

Homophily and minority-group size explain perception biases in social networks

People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.

Dynamic cognitive models of intertemporal choice

Traditionally, descriptive accounts of intertemporal choice have relied on static and deterministic models that assume alternative-wise processing of the options. Recent research, by contrast, has highlighted the dynamic and probabilistic nature of intertemporal choice and provided support for attribute-wise processing. Currently, dynamic models of intertemporal choice-which account for both the resulting choice and the time course over which the construction of a choice develops-rely exclusively on the framework of evidence accumulation. In this article, we develop and rigorously compare several candidate schemes for dynamic models of intertemporal choice. Specifically, we consider an existing dynamic modeling scheme based on decision field theory and develop two novel modeling schemes-one assuming lexicographic, noncompensatory processing, and the other built on the classical concepts of random utility in economics and discrimination thresholds in psychophysics. We show that all three modeling schemes can accommodate key behavioral regularities in intertemporal choice. When empirical choice and response time data were fit simultaneously, the models built on random utility and discrimination thresholds performed best. The results also indicated substantial individual differences in the dynamics underlying intertemporal choice. Finally, model recovery analyses demonstrated the benefits of including both choice and response time data for more accurate model selection on the individual level. The present work shows how the classical concept of random utility can be extended to incorporate response dynamics in intertemporal choice. Moreover, the results suggest that this approach offers a successful alternative to the dominating evidence accumulation approach when modeling the dynamics of decision making.

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed.

Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making

Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulation of associative fan (i.e., the decision options were associated with one, two, or three attributes). The results show that both the automatic activation of all attributes associated with a decision option and the controlled sequential retrieval of specific attributes can be traced in material-specific brain areas. Moreover, the two facets of memory retrieval were associated with distinct activation patterns within the frontoparietal network: The dorsolateral prefrontal cortex was found to reflect increasing retrieval effort during both automatic and controlled activation of attributes. In contrast, the superior parietal cortex only responded to controlled retrieval, arguably reflecting the sequential updating of attribute information in working memory. This dissociation in activation pattern is consistent with ACT-R and constitutes an important step toward a neural model of the retrieval dynamics involved in memory-based decision-making.

We'll meet again: revealing distributional and temporal patterns of social contact

What are the dynamics and regularities underlying social contact, and how can contact with the people in one's social network be predicted? In order to characterize distributional and temporal patterns underlying contact probability, we asked 40 participants to keep a diary of their social contacts for 100 consecutive days. Using a memory framework previously used to study environmental regularities, we predicted that the probability of future contact would follow in systematic ways from the frequency, recency, and spacing of previous contact. The distribution of contact probability across the members of a person's social network was highly skewed, following an exponential function. As predicted, it emerged that future contact scaled linearly with frequency of past contact, proportionally to a power function with recency of past contact, and differentially according to the spacing of past contact. These relations emerged across different contact media and irrespective of whether the participant initiated or received contact. We discuss how the identification of these regularities might inspire more realistic analyses of behavior in social networks (e.g., attitude formation, cooperation).