Perceptions of Income Inequality and Women's Intrasexual Competition.

Income inequality has been empirically linked to interpersonal competition and risk-taking behaviors, but a separate line of findings consistently shows that individuals have inaccurate perceptions of the actual levels of income inequality in society. How can inequality be both consistently misperceived and yet a reliable predictor of behavior? The present study extends both these lines of research by evaluating if the scope of input used to assess income inequality (i.e., at the national, state, county, or postal code level) can account for perception discrepancies and if actual/perceived inequality is associated with female intrasexual competition. Female participants recruited online from the general US population (n = 691) provided demographic information, measures of perceived income inequality, and measures of intrasexual competition attitudes and behavior. Actual and perceived income inequality (at any level) did not predict negative attitudes toward other women or female weighting of physical appearance as a desirable trait. Perceived income inequality and actual county-level inequality was, however, predictive of female competition in the form of self-sexualization clothing choice. Further analyses found that age and importance placed on physical attractiveness also predicted women's clothing choices. Perceptions of income inequality were predicted not by actual Gini indices, but by beliefs about the levels of poverty and income gaps. These results highlight the importance of better understanding the proximate cues by which people perceive environmental features such as inequality, and how those cues are used to adjust interpersonal behaviors.

Improving patient understanding of prenatal screening tests: Using naturally sampled frequencies, pictures, and accounting for individual differences.

Objective: Health professionals must communicate prenatal screening test results effectively to patients, but these tests involve very low prevalence and high false positive risks; a situation known to be difficult for people to understand. Methods: The present experiments evaluated the effectiveness of presenting prenatal screening test results for Trisomy 21, Trisomy 13, or DiGeorge Syndrome, using either naturally sampled frequencies or standard percentages. Participants were given a task of interpreting the posterior probability of an embryo having the condition. Results: People were significantly better with naturally sampled frequencies. Numerical literacy and visuospatial ability significantly accounted for individual differences in performance within conditions. Participants nevertheless did not differ in ratings of how useful the different presentation formats were, suggesting a lack of awareness of how format influenced understanding. These results held regardless of whether the respondents were undergraduates (Experiment 1) or members of the general population recruited online (Experiment 2). Conclusion: Using naturally sampled frequencies improves patient understanding of prenatal screening tests results, with low cost of implementation. Innovation: Using realistic prenatal screening test results, these results show how to improve patient counseling via the use of naturally sampled frequencies.

Sex, and other important things: Tracking ratios of ecologically significant categories.

The ratio of males and females in a population (the sex ratio) has been documented as an important factor in calibrating mating behaviors. This implies mental processes of attention, perception, categorization, and memory to obtain these environmental sex ratios. Although recent work has indicated that sex ratio information can be processed quickly, accurately, and with little effort, there are still open questions about whether sex ratio information is cognitively privileged or prioritized, relative to other environmental information. The present experiments used an ensemble coding paradigm with larger, more complex matrices of stimuli and with a more feasible range of ratios (between 7:13 to 13:7) than many prior studies on sex ratio perception. Experiment 1 found that sex ratio estimates are sensitive to actual seen ratios (of a 4 × 5 matrix of faces, shown for about 500 ms), and that those judgments are more accurate than similarly presented ensemble coding judgments for vehicles (ratios of cars and truck) or for animals (ratios of cats and dogs). Experiment 2 found that sex ratio estimates and hair color ratio estimates are about equal in accuracy. These results together suggest that faces are a privileged content for frequency tracking, relative to other aspects of the environment. Further research can extend this work by disambiguating factors such as complexity and discriminability of various facial cues and the stage of processing at which those cues are being used. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Simulating human behavioral changes in livestock production systems during an epidemic: The case of the US beef cattle industry.

Human behavioral change around biosecurity in response to increased awareness of disease risks is a critical factor in modeling animal disease dynamics. Here, biosecurity is referred to as implementing control measures to decrease the chance of animal disease spreading. However, social dynamics are largely ignored in traditional livestock disease models. Not accounting for these dynamics may lead to substantial bias in the predicted epidemic trajectory. In this research, an agent-based model is developed by integrating the human decision-making process into epidemiological processes. We simulate human behavioral change on biosecurity practices following an increase in the regional disease incidence. We apply the model to beef cattle production systems in southwest Kansas, United States, to examine the impact of human behavior factors on a hypothetical foot-and-mouth disease outbreak. The simulation results indicate that heterogeneity of individuals regarding risk attitudes significantly affects the epidemic dynamics, and human-behavior factors need to be considered for improved epidemic forecasting. With the same initial biosecurity status, increasing the percentage of risk-averse producers in the total population using a targeted strategy can more effectively reduce the number of infected producer locations and cattle losses compared to a random strategy. In addition, the reduction in epidemic size caused by the shifting of producers' risk attitudes towards risk-aversion is heavily dependent on the initial biosecurity level. A comprehensive investigation of the initial biosecurity status is recommended to inform risk communication strategy design.

Impact of truck contamination and information sharing on foot-and-mouth disease spreading in beef cattle production systems.

As cattle movement data in the United States are scarce due to the absence of mandatory traceability programs, previous epidemic models for U.S. cattle production systems heavily rely on contact rates estimated based on expert opinions and survey data. These models are often based on static networks and ignore the sequence of movement, possibly overestimating the epidemic sizes. In this research, we adapt and employ an agent-based model that simulates beef cattle production and transportation in southwest Kansas to analyze the between-premises transmission of a highly contagious disease, foot-and-mouth disease. First, we assess the impact of truck contamination on the disease transmission with the truck agent following an independent clean-infected-clean cycle. Second, we add an information-sharing functionality such that producers/packers can trace back and forward their trade records to inform their trade partners during outbreaks. Scenario analysis results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. Mitigation strategies informed by information sharing can effectively mitigate epidemics, highlighting the benefit of promoting information sharing in the cattle industry. In addition, we identify salient characteristics that must be considered when designing an information-sharing strategy, including the number of days to trace back and forward in the trade records and the role of different cattle supply chain stakeholders. Sensitivity analysis results show that epidemic sizes are sensitive to variations in parameters of the contamination period for a truck or a loading/unloading area of premises, and indirect contact transmission probability and future studies can focus on a more accurate estimation of these parameters.

"Human status criteria: Sex differences and similarities across 14 nations": Correction to Buss et al. (2020).

Reports an error in "Human status criteria: Sex differences and similarities across 14 nations" by David M. Buss, Patrick K. Durkee, Todd K. Shackelford, Brian F. Bowdle, David P. Schmitt, Gary L. Brase, Jae C. Choe and Irina Trofimova (Journal of Personality and Social Psychology, Advanced Online Publication, May 28, 2020, np). In the article, the third sentence in the Content level subsection in the Status Criteria More Central to Women section of the Results should appear instead as Fidelity, chastity/purity, and long-term mating success increase women's status more than men's. A coding error in Figure 7 for Dishonoring Family appeared. The corrected Figure 7 now appears. (The following abstract of the original article appeared in record 2020-35662-001). Social status is a central and universal feature of our highly social species. Reproductively relevant resources, including food, territory, mating opportunities, powerful coalitional alliances, and group-provided health care, flow to those high in status and trickle only slowly to those low in status. Despite its importance and centrality to human social group living, the scientific understanding of status contains a large gap in knowledge-the precise criteria by which individuals are accorded high or low status in the eyes of their group members. It is not known whether there exist universal status criteria, nor the degree to which status criteria vary across cultures. Also unknown is whether status criteria are sex differentiated, and the degree of cross-cultural variability and consistency of sex-differentiated status criteria. The current article investigates status criteria across 14 countries (N = 2,751). Results provide the first systematic documentation of potentially universal and sex-differentiated status criteria. Discussion outlines important next steps in understanding the psychology of status. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Convergent evidence for a theory of rapid, automatic, and accurate sex ratio tracking.

It is presumed that people track the sex ratios in their environment (the number of males relative to number of females) in order to adaptively adjust their decisions and behaviors, but this actual tracking ability has not been established. The relevance of sex ratio information, drawn from evolutionary biology and studies of human relationship decision making, is integrated here with memory research (on frequency encoding), perception research (on ensemble coding), and neuroscience research. A series of four experiments provide empirical results to help fill research gaps and facilitate this theoretical integration. In particular, these studies connect details from memory research on relatively automatic frequency encoding of both items and categories, perception research on summary statistics from ensemble coding, and theoretical ideas about the function of these abilities (specifically applied to human sex ratios based on faces) from social and evolutionary approaches. Collectively this research demonstrates an evolved psychological mechanism for functional, fast, and relatively automatic human abilities to track experienced sex ratios in the social world. This sex ratio information is theorized to underpin documented facultative adjustments in relationship dynamics as well as perceptions of social group characteristics. This integrative approach highlights how the coding, memory, and judgments about population sex ratios can both account for a number of existing findings and point towards key further research.

Human status criteria: Sex differences and similarities across 14 nations.

[Correction Notice: An Erratum for this article was reported online in Journal of Personality and Social Psychology on Sep 7 2020 (see record 2020-68801-001). In the original article, the third sentence in the Content level subsection in the Status Criteria More Central to Women section of the Results should appear instead as Fidelity, chastity/purity, and long-term mating success increase women's status more than men's. A coding error in Figure 7 for Dishonoring Family appeared. The corrected Figure 7 now appears.] Social status is a central and universal feature of our highly social species. Reproductively relevant resources, including food, territory, mating opportunities, powerful coalitional alliances, and group-provided health care, flow to those high in status and trickle only slowly to those low in status. Despite its importance and centrality to human social group living, the scientific understanding of status contains a large gap in knowledge-the precise criteria by which individuals are accorded high or low status in the eyes of their group members. It is not known whether there exist universal status criteria, nor the degree to which status criteria vary across cultures. Also unknown is whether status criteria are sex differentiated, and the degree of cross-cultural variability and consistency of sex-differentiated status criteria. The current article investigates status criteria across 14 countries (N = 2,751). Results provide the first systematic documentation of potentially universal and sex-differentiated status criteria. Discussion outlines important next steps in understanding the psychology of status. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Developing a Better and More User-Friendly Numeracy Scale for Patients.

BACKGROUND: A person's ability to work with and understand numerical information (i.e., numeracy) is increasingly important in everyday health and other decision-making contexts. Several survey measures of numeracy have been developed to address this trend, including the widely used General Numeracy Scale (GNS), which is thematically focused on health decision-making and is assumed to measure a unidimensional construct of numeracy. OBJECTIVE: The present research was designed to evaluate this proposed unidimensional structure of general numeracy, for which prior data have given mixed empirical support. METHODS: Three samples completed the GNS, in different forms, and responses were analyzed in terms of underlying factor structure. KEY RESULTS: We show that both one-factor and four-factor models of numeracy are plausible based on the GNS (Study 1), and then develop a multiple-choice version of the GNS (i.e., the MC-GNS) that demonstrates some increased clarity in factor structure due to the consistent response format (Study 2). A further study evaluated the convergent and discriminant validity of the MC-GNS (Study 3), finding it to be as good as or better than the prior scale. CONCLUSIONS: Additionally, the MC-GNS is easier for people to take, likely to be less stressful, and easier for practitioners to score. Collectively, this research identifies a problem with the GNS measure, develops improvements to help address this problem, and in the process creates a way to more easily measure numeracy in practical settings. [HLRP: Health Literacy Research and Practice. 2019;3(3):e174-e180.]. PLAIN LANGUAGE SUMMARY: Numeracy is important across health contexts. Prevalent numeracy scales assumedly measure a single construct but empirical support for this is lacking. We find both one- and four-factor models are consistent with one scale and develop a revision that clarifies this structure without sacrificing validity. This revised numeracy scale is easier to administer and score, and therefore preferable in practical settings.

Comparing the Efficacy of Static and Dynamic Graph Types in Communicating Complex Statistical Relationships.

Graphs are useful tools to communicate meaningful patterns in data, but their efficacy varies considerably based on the figure's construction and presentation medium. Specifically, a digital format figure can be dynamic, allowing the reader to manipulate it and little is known about the efficacy of dynamic figures. This present study compared how effectively static and dynamic graphical formats convey relationship information, and in particular variable interactions. Undergraduates (N = 128, 56% female, M age = 18.9) were given a brief tutorial on main effects and interactions in data and then answered 48 multiple-choice questions about specific graphs. Each question involved one of four figure types and one of four relationship types (main effect only, interaction only, main effect and interaction, or no relationship), with relationship types and graphical formats fully crossed. Multilevel logistic regression analysis revealed that participants were fairly accurate at detecting main effects and null relationships but struggled with interaction effects. Additionally, the static 3D graph lowered performance for detecting main effects, although this negative effect disappeared when participants were allowed to rotate the 3D graph. These results suggest that dynamic figures in digital publications are a potential tool to effectively communicate data, but they are not a panacea. Undergraduates continued to struggle with more complicated relationships (e.g., interactions) regardless of graph type. Future studies will need to examine more experienced populations and additional dynamic graph formats, especially ones tailored for demonstrating interactions (e.g., profiler plots).

What Would It Take to Get You into an Electric Car? Consumer Perceptions and Decision Making about Electric Vehicles.

Americans buy millions of vehicles every year, but research on how those decisions are made, including personality and individual difference factors, has rarely taken full advantage of psychological insights. This is an increasingly important topic because, for instance, decisions to buy electric vehicles (EVs) rather than gasoline-powered vehicles have environmental and geopolitical consequences. A series of studies reveal the very different perceptions of EVs and gas vehicles. Although vehicle choices at the aggregate level were strongly correlated with economic considerations, individual consumer choices were correlated with delay discounting rates in Study 1 but not Study 2. These studies also did not find significant correlations between EV purchase decisions and a number of other individual difference traits often thought to be factors in such decisions (i.e., social value orientation, political attitudes, environmental attitudes, preference for novel products, or an array of core social values). Regression models indicate that factors which did predict vehicle choices concerns were about the performance and range of EVs, EV prevalence in general, and beliefs about what statements different vehicle types made about their owners and the owners' values. More attention to the vehicle-associated values and immediate performance/use issues can help to promote EV purchase decisions and subsequent improvements in environmental and political stability.

Do Different Mental Models Influence Cybersecurity Behavior? Evaluations via Statistical Reasoning Performance.

Cybersecurity research often describes people as understanding internet security in terms of metaphorical mental models (e.g., disease risk, physical security risk, or criminal behavior risk). However, little research has directly evaluated if this is an accurate or productive framework. To assess this question, two experiments asked participants to respond to a statistical reasoning task framed in one of four different contexts (cybersecurity, plus the above alternative models). Each context was also presented using either percentages or natural frequencies, and these tasks were followed by a behavioral likelihood rating. As in previous research, consistent use of natural frequencies promoted correct Bayesian reasoning. There was little indication, however, that any of the alternative mental models generated consistently better understanding or reasoning over the actual cybersecurity context. There was some evidence that different models had some effects on patterns of responses, including the behavioral likelihood ratings, but these effects were small, as compared to the effect of the numerical format manipulation. This points to a need to improve the content of actual internet security warnings, rather than working to change the models users have of warnings.

Adding up to good Bayesian reasoning: Problem format manipulations and individual skill differences.

Bayesian reasoning is ubiquitous in everyday life. However, how people perform in formal Bayesian reasoning tasks depends on both how the task is presented and individual differences in general ability. Different theoretical views predict that these individual difference factors should either interact with, or be independent of, presentation formats. This research first established changes in reasoning performance across different presentation formats (information presented either in natural frequencies or in percentages and either with pictorial aids or with no pictures). Concurrently, participants were assessed for their general level of numerical literacy and level of spatial ability. Multiple analyses indicate that the contributions of numerical literacy and spatial ability to Bayesian reasoning success were largely independent of the presentation of the tasks (numerical format and picture presence). These findings are shown in a subsequent experiment to hold, broadly, across 4 different assessments of numerical literacy and 4 different assessments of spatial ability. This result is most consistent with an ecological rationality view of statistical reasoning. (PsycINFO Database Record

Communicating HIV Results to Low-Risk Individuals: Still Hazy After All These Years.

Revised Centers for Disease Control and Prevention recommendations on HIV testing now promote testing of most risk groups. However, positive results for low-risk individuals are more likely to be false positives than for high-risk individuals, making clear communication of test results even more imperative. In a study, we evaluated current counseling of low-risk test recipients via a sample of 29 HIV hotline counselors from U.S. state and national hotlines. 100% of counselors interviewed failed to provide an accurate conditional HIV risk for low-risk women, but were more likely than a 1998 German sample to report that false positives could occur. In a second study, undergraduates read idealized transcripts of interviews with HIV counselors and computed conditional risk for a low-risk individual. The natural frequency format offered a small but significant improvement in conditional reasoning, comparable to the effect of numerical literacy. Applications for ecologically valid numerical presentations of risk and implications for numeracy are discussed.

Good fences make for good neighbors but bad science: a review of what improves Bayesian reasoning and why.

Bayesian reasoning, defined here as the updating of a posterior probability following new information, has historically been problematic for humans. Classic psychology experiments have tested human Bayesian reasoning through the use of word problems and have evaluated each participant's performance against the normatively correct answer provided by Bayes' theorem. The standard finding is of generally poor performance. Over the past two decades, though, progress has been made on how to improve Bayesian reasoning. Most notably, research has demonstrated that the use of frequencies in a natural sampling framework-as opposed to single-event probabilities-can improve participants' Bayesian estimates. Furthermore, pictorial aids and certain individual difference factors also can play significant roles in Bayesian reasoning success. The mechanics of how to build tasks which show these improvements is not under much debate. The explanations for why naturally sampled frequencies and pictures help Bayesian reasoning remain hotly contested, however, with many researchers falling into ingrained "camps" organized around two dominant theoretical perspectives. The present paper evaluates the merits of these theoretical perspectives, including the weight of empirical evidence, theoretical coherence, and predictive power. By these criteria, the ecological rationality approach is clearly better than the heuristics and biases view. Progress in the study of Bayesian reasoning will depend on continued research that honestly, vigorously, and consistently engages across these different theoretical accounts rather than staying "siloed" within one particular perspective. The process of science requires an understanding of competing points of view, with the ultimate goal being integration.

Using statistical reasoning performance to reveal information parsing preferences in the mind.

Many cognitive tasks require the parsing of information into smaller, discrete units in order to enable effective information processing. This parsing can, broadly speaking, be done along either situationally ad hoc dimensions or done preferentially along ecologically and evolutionarily relevant dimensions. The present research systematically evaluates these two possibilities within a statistical reasoning context. While replicating results that appear to support the partition-edit-count hypothesis (that item parsing is equipotential, based on subtle linguistic cues), this result was found to be in large part due to confounds in the nature of the tasks rather than the partitioning manipulations (Experiment 1). Additionally, a frequency presentation of the same task not only eliminated the earlier confounds but also improved performance directly and as predicted by the alternative hypothesis (Experiment 2). Attempts to reintroduce a biasing partition frame (Experiment 3) and a process study of participants' task representation (Experiment 4) also both failed to support the partition-edit-count hypothesis. These results favour an ecological rationality perspective and the associated frequency and individuation hypotheses regarding statistical reasoning (i.e., a privileged status for frequency representations to guide parsing of objects, events, and locations into easily countable units).

The nature of thinking, shallow and deep.

Because the criteria for success differ across various domains of life, no single normative standard will ever work for all types of thinking. One method for dealing with this apparent dilemma is to propose that the mind is made up of a large number of specialized modules. This review describes how this multi-modular framework for the mind overcomes several critical conceptual and theoretical challenges to our understanding of human thinking, and hopefully clarifies what are (and are not) some of the implications based on this framework. In particular, an evolutionarily informed "deep rationality" conception of human thinking can guide psychological research out of clusters of ad hoc models which currently occupy some fields. First, the idea of deep rationality helps theoretical frameworks in terms of orienting themselves with regard to time scale references, which can alter the nature of rationality assessments. Second, the functional domains of deep rationality can be hypothesized (non-exhaustively) to include the areas of self-protection, status, affiliation, mate acquisition, mate retention, kin care, and disease avoidance. Thus, although there is no single normative standard of rationality across all of human cognition, there are sensible and objective standards by which we can evaluate multiple, fundamental, domain-specific motives underlying human cognition and behavior. This review concludes with two examples to illustrate the implications of this framework. The first example, decisions about having a child, illustrates how competing models can be understood by realizing that different fundamental motives guiding people's thinking can sometimes be in conflict. The second example is that of personifications within modern financial markets (e.g., in the form of corporations), which are entities specifically constructed to have just one fundamental motive. This single focus is the source of both the strengths and flaws in how such entities behave.

Explaining sex differences in reactions to relationship infidelities: comparisons of the roles of sex, gender, beliefs, attachment, and sociosexual orientation.

To the extent that sex differences are mediated by mechanisms such as sex-roles and beliefs, individual differences in these more proximate traits should account for significant portions of relevant sex differences. Differences between women and men in reactions to sexual and emotional infidelity were assessed in a large sample of participants (n = 477), and these target reactions were evaluated as a function of many potential proximate mediators (infidelity implications beliefs, gender-role beliefs, interpersonal trust, attachment style, sociosexuality, and culture of honor beliefs) and as a function of participant sex. Results found a consistent sex difference that was not mediated by any other variables, although a handful of other variables were related to male, but not female, individual differences. These findings suggest particularly promising directions for future research on integrating evolutionarily based sex differences and proximate individual differences.

When and for whom do frequencies facilitate performance? On the role of numerical literacy.

The thesis that the mind is better prepared to process frequencies-as compared to other numerical formats-continues to be debated. A recent aspect of this issue is the role of numeracy (numerical literacy; one's ability to understand and work with numerical information) and specifically the argument that individual differences in numeracy interact with numerical formats. This interaction, either that frequencies improve performance only for those of low numeracy or that frequencies work only for those of high numeracy, would suggest that better performance using frequencies could be due to (nonevolutionary) numeracy effects. The three present studies revisited prior work with cumulative probability, Bayesian reasoning, and scenario risk assessments to study the effects of numeracy on frequency facilitation. Results from these experiments consistently failed to replicate previous findings of interactions; however, a more consistent finding emerged of a straightforward frequency effect. The lack of interactions and observations of frequency main effects lend support to the evolutionary explanation of the frequency effect. In addition, some possible statistical processes are proposed to explain the observation of interactions in past studies.