Are there gender differences in cognitive reflection? Invariance and differences related to mathematics

ABSTRACT

Cognitive reflection is recognized as an important skill, which is necessary for making advantageous decisions. Even though gender differences in the Cognitive Reflection test (CRT) appear to be robust across multiple studies, little research has examined the source of the gender gap in performance. In Study 1, we tested the invariance of the scale across genders. In Study 2, we investigated the role of math anxiety, mathematical reasoning, and gender in CRT performance. The results attested the measurement equivalence of the Cognitive Reflection Test – Long (CRT- L), when administered to male and female students. Additionally, the results of the mediation analysis showed an indirect effect of gender on CRT-L performance through mathematical reasoning and math anxiety. The direct effect of gender was no longer statistically significant after accounting for the other variables. The current findings suggest that cognitive reflection is affected by numerical skills and related feelings.     

Fundamental failure to think logically about scientific questions: An illustration of tunnel vision with the application of Wason's Card Selection Test to criminal evidence

Logic and science are in some respects fundamentally different disciplines, in that for example, application of rules of logic can yield conclusions that are at odds with physical reality. Not surprisingly, people have ample difficulty with logical thinking. Nonetheless, in some instances, logical thinking can fuel empirical decision making. In the current research, it was established that applying rules of logic to a particular area of empirical decision making, that is, criminal fact finding, is indeed quite difficult (Study 1). Furthermore, the ability to think logically was found to be associated with superior evaluation of criminal evidence (Study 2). Implications of these findings are discussed.     

Salience Versus Proportional Reasoning: Rethinking the Mechanism Behind Graphical Display Effects

Two experiments examined predictions from two separate explanations for previously observed display effects for communicating low‐probability risks: foreground:background salience and proportional reasoning. According to foreground:background salience, people's risk perceptions are based on the relative salience of the foreground (number of people harmed) versus the background (number of people at risk), such that calling attention to the background makes the risk seem smaller. Conversely, the proportional reasoning explanation states that what matters is whether the respondent attends to the proportion, which conveys how small the risk is. In Experiment 1, we made the background more salient via color and bolding; in contrast to the foreground:background salience prediction, this manipulation did not influence participants' risk aversion. In Experiment 2, we separately manipulated whether the foreground and the background were displayed graphically or numerically. In keeping with the proportional reasoning hypothesis, there was an interaction whereby participants given formats that displayed the foreground and background in the same modality (graphs or numbers, thereby making the proportion easier to form) saw the probability as smaller and were less risk averse than participants given the information in different modalities. There was also a main effect of displaying the background graphically, providing some support for foreground:background salience. In total, this work suggests that the proportional reasoning account provides a good explanation of many display effects related to communicating low‐probability risks, although there is some role for foreground:background salience as well. Copyright © 2018 John Wiley & Sons, Ltd.     

Real Objects Can Impede Conditional Reasoning but Augmented Objects Do Not

In this study, Knauff and Johnson‐Laird's (2002) visual impedance hypothesis (i.e., mental representations with irrelevant visual detail can impede reasoning) is applied to the domain of external representations and diagrammatic reasoning. We show that the use of real objects and augmented real (AR) objects can control human interpretation and reasoning about conditionals. As participants made inferences (e.g., an invalid one from "if P then Q" to "P"), they also moved objects corresponding to premises. Participants who moved real objects made more invalid inferences than those who moved AR objects and those who did not manipulate objects (there was no significant difference between the last two groups). Our results showed that real objects impeded conditional reasoning, but AR objects did not. These findings are explained by the fact that real objects may over‐specify a single state that exists, while AR objects suggest multiple possibilities.     

Predicting Political Biases Against the Occupy Wall Street and Tea Party Movements

Accusations of hypocrisy have flown against both supporters and opponents of the Occupy Wall Street (OWS) and Tea Party movements. Integrating the ideologically objectionable premise model (IOPM), a newly devised model of political judgment, with political tolerance research, we find that how the political activities of OWS and Tea Party demonstrators are described determines whether or not biases against these groups emerge among people low and high in right‐wing authoritarianism (RWA). Specifically, people low in RWA were more biased against the Tea Party than OWS regardless of whether the groups engaged in normatively threatening or reassuring political behavior, whereas people high in RWA were more biased against OWS than the Tea Party when the groups engaged in normatively threatening (and therefore objectionable), but not normatively reassuring (and therefore acceptable) behavior. These findings further support the IOPM's contention that premise objectionableness, not right‐wing orientation, determines biases in political judgment.     

The Wished‐For Always Wins Until the Winner Was Inevitable All Along: Motivated Reasoning and Belief Bias Regulate Emotion During Elections

How do biases affect political information processing? A variant of the Wason selection task, which tests for confirmation bias, was used to characterize how the dynamics of the recent U.S. presidential election affected how people reasoned about political information. Participants were asked to evaluate pundit‐style conditional claims like “The incumbent always wins in a year when unemployment drops” either immediately before or immediately after the 2012 presidential election. A three‐way interaction between ideology, predicted winner (whether the proposition predicted that Obama or Romney would win), and the time of test indicated complex effects of bias on reasoning. Before the election, there was partial evidence of motivated reasoning—liberals performed especially well at looking for falsifying information when the pundit's claim predicted Romney would win. After the election, once the outcome was known, there was evidence of a belief bias—people sought to falsify claims that were inconsistent with the real‐world outcome rather than their ideology. These results suggest that people seek to implicitly regulate emotion when reasoning about political predictions. Before elections, people like to think their preferred candidate will win. After elections, people like to think the winner was inevitable all along.     

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).     

Beliefs About the True Self Explain Asymmetries Based on Moral Judgment

Past research has identified a number of asymmetries based on moral judgments. Beliefs about (a) what a person values, (b) whether a person is happy, (c) whether a person has shown weakness of will, and (d) whether a person deserves praise or blame seem to depend critically on whether participants themselves find the agent's behavior to be morally good or bad. To date, however, the origins of these asymmetries remain unknown. The present studies examine whether beliefs about an agent's “true self” explain these observed asymmetries based on moral judgment. Using the identical materials from previous studies in this area, a series of five experiments indicate that people show a general tendency to conclude that deep inside every individual there is a “true self” calling him or her to behave in ways that are morally virtuous. In turn, this belief causes people to hold different intuitions about what the agent values, whether the agent is happy, whether he or she has shown weakness of will, and whether he or she deserves praise or blame. These results not only help to answer important questions about how people attribute various mental states to others; they also contribute to important theoretical debates regarding how moral values may shape our beliefs about phenomena that, on the surface, appear to be decidedly non‐moral in nature.     

A threatening exchange: Gender and life history strategy predict perceptions and reasoning about sexual harassment

Sexual harassment is a serious societal issue, with extensive economic and psychological consequences, yet it is also an ill-defined construct fundamentally defined in terms of subjective perception. The current work was designed to examine the ways in which individual differences between people are systematically related to different perceptions of sexual harassment scenarios, as well as reasoning about those harassment situations. Participants (N = 460) read several possible harassment scenarios and rated how uncomfortable they would find them. They then also evaluated a quid pro quo sexual harassment situation in terms of their interpretation of it as a threat or a social exchange and completed a deductive reasoning task about the same situation. Females and individuals with slow life history strategies were more uncomfortable with potential harassment situations and were more likely to interpret the quid pro quo scenario as a threat. Further, interpreting the scenario as a threat was associated with poorer performance on the deductive logic task, compared to those who interpreted the scenario as a social exchange.     

Identifying expectations about the strength of causal relationships

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people’s a priori beliefs about causal systems, with recent research focusing on people’s expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning—a method in which participants make inferences about data generated based on their own responses in previous trials—to estimate participants’ prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants’ prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.