In the present study we introduce a sensitive video-based test for the evaluation of subtle mindreading difficulties: the Movie for the Assessment of Social Cognition (MASC). This new mindreading tool involves watching a short film and answering questions referring to the actors' mental states. A group of adults with Asperger syndrome (n = 19) and well-matched control subjects (n = 20) were administered the MASC and three other mindreading tools as part of a broader neuropsychological testing session. Compared to control subjects, Asperger individuals exhibited marked and selective difficulties in social cognition. A Receiver Operating Characteristic (ROC) analysis for the mindreading tests identified the MASC as discriminating the diagnostic groups most accurately. Issues pertaining to the multidimensionality of the social cognition construct are discussed.

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A few years ago, the world experienced the most severe economic crisis since the Great Depression. According to the depression baby hypothesis, people who live through such macroeconomic shocks take less financial risk in their future lives (e.g., lower stock market participation). This hypothesis has previously been tested against survey data. Here, we tested it in a simulated experimental stock market (based on the Spanish stock index, IBEX-35), varying both the length of historical data available to participants (including or excluding a macroeconomic shock) and the mode of learning about macroeconomic events (through sequential experience or symbolic descriptions). Investors who learned about the market from personal experience took less financial risk than did those who learned from graphs, thus echoing the description-experience gap observed in risky choice. In a second experiment, we reversed the market, turning the crisis into a boom. The description-experience gap persisted, with investors who experienced the boom taking more risk than those who did not. The results of a third experiment suggest that the observed gap is not driven by a wealth effect, and modeling suggests that the description-experience gap is explained by the fact that participants who learn from experience are more risk averse after a negative shock. Our findings highlight the crucial role of the mode of learning for financial risk taking and, by extension, in the legally required provision of financial advice.

This article relates natural frequency representations of cue-criterion relationships to fast-and-frugal heuristics for inferences based on multiple cues. In the conceptual part of this work, three approaches to classification are compared to one another: The first uses a natural Bayesian classification scheme, based on profile memorization and natural frequencies. The second is based on Naïve Bayes, a heuristic that assumes conditional independence between cues (given the criterion). The third approach is to construct fast-and-frugal classification trees, which can be conceived as pruned versions of diagnostic natural frequency trees. Fastand-frugal trees can be described as lexicographic classifiers but can also be related to another fundamental class of models, namely linear models. Linear classifiers with fixed thresholds and noncompensatory weights coincide with fast-and-frugal trees-not as processes but in their output. Various heuristic principles for tree construction are proposed. In the second, empirical part of this article, the classification performance of the three approaches when making inferences under uncertainty (i.e., out of sample) is evaluated in 11 medical data sets in terms of Receiver Operating Characteristics (ROC) diagrams and predictive accuracy. Results show that the two heuristic approaches, Naïve Bayes and fast-and-frugal trees, generally outperformed the model that is normative when fitting known data, namely classification based on natural frequencies (or, equivalently, profile memorization). The success of fast-and-frugal trees is grounded in their ecological rationality: their construction principles can exploit the structure of information in the data sets. Finally, implications, applications, limitations, and possible extensions of this work are discussed.

Some issues that have been settled by the scientific community, such as evolution, the effectiveness of vaccinations, and the role of CO 2 emissions in climate change, continue to be rejected by segments of the public. This rejection is typically driven by people's worldviews, and to date most research has found that conservatives are uniformly more likely to reject scientific findings than liberals across a number of domains. We report a large (N > 1,000) preregistered study that addresses two questions: First, can we find science denial on the left? Endorsement of pseudoscientific complementary and alternative medicines (CAM) has been anecdotally cited as being more consonant with liberals than conservatives. Against this claim, we found more support for CAM among conservatives than liberals. Second, we asked how liberals and conservatives resolve dilemmas in which an issue triggers two opposing facets of their worldviews. We probed attitudes on gender equality and the evolution of sex differencestwo constructs that may create conflicts for liberals (who endorse evolution but also equality) and conservatives (who endorse gender differences but are sceptical of evolution). We find that many conservatives reject both gender equality and evolution of sex differences, and instead embrace "naturally occurring" gender differences. Many liberals, by contrast, reject evolved gender differences, as well as naturally occurring gender differences, while nonetheless strongly endorsing evolution.

Fast-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.

People frequently engage in dishonest behavior at a cost to others, and it is therefore beneficial to study interventions promoting honest behavior. We implemented a novel intervention that gave participants a choice to promise to be truthful or not to promise. To measure cheating behavior, we developed a novel variant of the mind game—the dice‐box game—as well as a child‐friendly sender–receiver game. Across three studies with adolescents aged 10 to 14 years (N = 640) from schools in India, we found that promises systematically lowered cheating rates compared with no‐promise control conditions. Adolescents who sent truthful messages in the sender–receiver game cheated less in the dice‐box game and promises reduced cheating in both tasks (Study 1). Promises in the dice‐box game remained effective when negative externalities (Study 2) or incentives for competition (Study 3) were added. A joint analysis of data from all three studies revealed demographic variables that influenced cheating. Our findings confirm that promises have a strong, binding effect on behavior and can be an effective intervention to reduce cheating.