Three correctives can get researchers out of the trap of constructing unitary theories of “thinking”: (1) Strong inference methods largely avoid problems associated with universal prescriptive normativism; (2) theories must recognize that significant modularity of cognitive processes is antithetical to general accounts of thinking; and (3) consideration of the domain-specificity of rationality render many of the present article's issues moot.

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. (shrink)

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. (shrink)

Reasoning about social groups and their associated markers was investigated as a particular case of human reasoning about cue-category relationships. Assertions that reasoning involving cues and associated categories elicits specific probabilistic assumptions are supported by the results of three experiments. This phenomenon remains intact across the use of categorical syllogisms, conditional syllogisms, and the use of social groups that vary in their perceived cohesiveness, or entitativity. Implications are discussed for various theories of reasoning, and additional aspects of social group/coalitional reasoning (...) are also discussed. (shrink)

A more complete and balanced theoretical framework for social psychology, as recommended in the target article, must include functional explanations of processes – moving beyond enumerations of processes and their properties. These functional explanations are at a different, but complementary, level from process descriptions. The further advancement of social psychology relies on the incorporation of such multilevel explanations.

Female waist-to-hip ratio (WHR) has generally been an important general predictor of ratings of physical attractiveness and related characteristics. Individual differences in ratings do exist, however, and may be related to differences in the reproductive tactics of the male raters such as pursuit of short-term or long-term relationships and adjustments based on perceptions of one’s own quality as a mate. Forty males, categorized according to sociosexual orientation and physical qualities (WHR, Body Mass Index, and self-rated desirability), rated female models on (...) both attractiveness and likelihood they would approach them. Sociosexually restricted males were less likely to approach females rated as most attractive (with 0.68–0.72 WHR), as compared with unrestricted males. Males with lower scores in terms of physical qualities gave ratings indicating more favorable evaluations of female models with lower WHR. The results indicate that attractiveness and willingness to approach are overlapping but distinguishable constructs, both of which are influenced by variations in characteristics of the raters. (shrink)

Both the theoretical frameworks that organize the first part of Barbey & Sloman's (B&S's) target article and the empirical evidence marshaled in the second part are marked by distinctions that should not exist (i.e., false dichotomies), conflations where distinctions should be made, and selective omissions of empirical results that create illusions of theoretical and empirical favor.

Special design criteria are largely unable to discriminate between claims that specific competencies in judgements under uncertainty are a result of an adaptation for representing naturally sampled frequencies, or due only to inherent properties of such a format. Because divisions between these perspectives are thin, evidence via additional criteria are persuasive only in combination, using inference to the best available explanation.

The tension between focusing on species similarities versus species differences (phylogenetic versus adaptationist approaches) recurs in discussions about the nature of neural connectivity and organization following brain expansion. Whereas Striedter suggests a primary role for response inhibition, other possibilities include dense recurrent connectivity loops. Computer simulations and brain imaging technologies are crucial in better understanding actual neuronal connectivity patterns.

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. (shrink)