STUDY ALERT: The Small World of Psychopathology

Denny Borsboom, Angelique O. J. Cramer, Verena D. Schmittmann, Sacha Epskamp, Lourens J. Waldorp

Abstract

“It’s ok — Not everyone can be good at math”: Instructors with an entity theory comfort (and demotivate) students

Aneeta Rattan, Catherine Good, Carol S. Dweck

Can comforting struggling students demotivate them and potentially decrease the pool of students pursuing math- related subjects? In Studies 1–3, instructors holding an entity (fixed) theory of math intelligence more readily judged students to have low ability than those holding an incremental (malleable) theory. Studies 2–3 further revealed that those holding an entity (versus incremental) theory were more likely to both comfort students for low math ability and use “kind” strategies unlikely to promote engagement with the field (e.g., assigning less homework). Next, we explored what this comfort-oriented feedback communicated to students, compared with strategy-oriented and control feedback (Study 4). Students responding to comfort-oriented feedback not only perceived the instructor’s entity theory and low expectations, but also reported lowered motivation and lower expectations for their own performance. This research has implications for understanding how pedagogical practices can lock students into low achievement and deplete the math pipeline.

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The Road Not Taken: Creative Solutions Require Avoidance of High-Frequency Responses

Nitin Gupta, Yoonhee Jang, Sara C. Mednick, and David E. Huber

To investigate individual differences in creativity as measured with a complex problem-solving task, we developed a computational model of the remote associates test (RAT). For 50 years, the RAT has been used to measure creativity. Each RAT question presents three cue words that are linked by a fourth word, which is the correct answer. We hypothesized that individuals perform poorly on the RAT when they are biased to consider high-frequency candidate answers.To assess this hypothesis, we tested individuals with 48 RAT questions and required speeded responding to encourage guessing. Results supported our hypothesis. We generated a norm-based model of the RAT using a high-dimensional semantic space, and this model accurately identified correct answers. A frequency-biased model that included different levels of bias for high- frequency candidate answers explained variance for both correct and incorrect responses. Providing new insight into the nature of creativity, the model explains why some RAT questions are more difficult than others, and why some people perform better than others on the RAT.

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Intelligence and the brain: A model-based approach

Rogier A. Kievit, Hilko van Rooijen, Jelte M. Wicherts, Lourens J. Waldorp, Kees-Jan Kan, H. Steven Scholte, and Denny Borsboom

Various biological correlates of general intelligence (g) have been reported. Despite this, however, the relationship between neurological measurements and g is not fully clear. We use structural equation modeling to model the relationship between behavioral Wechsler Adult Intelligence Scale (WAIS) estimates of g and neurological measurements (voxel-based morphometry and diffusion tensor imaging of eight regions of interest). We discuss psychometric models that explicate the relationship between g and the brain in a manner in line with the scientific study of g. Fitting the proposed models to the data, we find that a MIMIC model (for multiple indicators, multiple causes), where the contributions of different brain regions to a unidimensional g are estimated separately, provides the best fit against the data.

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Sex Differences in the Brain: The Not So Inconvenient Truth

Margaret M. McCarthy, Arthur P. Arnold, Gregory F. Ball, Jeffrey D. Blaustein, and Geert. J. De Vries

The arguments for studying sex differences in the brain are as follows. (1) There is compelling evidence of pervasive and robust differences between males and females in both normal and pathological conditions. (2) The number of published studies limited to males remains stunningly and stubbornly high (Zucker and Beery, 2010). Scientific conclusions based on the study of one sex could have limited value in understanding some phenomena in the other sex. Thus, females deserve more study. (3) In addition, direct comparison of the two sexes is beneficial because of the unique perspective it offers.

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Predicting Preferences for Sex Acts: Which Traits Matter Most, and Why?

Ashley Peterson, Glenn Geher, and Scott Barry Kaufman

Several dispositional traits have been examined in mating contexts by evolutionary psychologists. Such traits include life history strategy, sociosexuality, and the Big Five. Recently, scholars have examined the validity and predictive utility of mating intelligence, a new construct designed to capture the cognitive processes that underlie mating psychology. The current research employed a battery of dispositional traits that include all these constructs in an effort to predict preferences for different kinds of sex acts. Sexual acts vary wildly, and the ability to predict this variability may well hold an important key to underlying sexual strategies. A sample of 607 young adults (144 males and 463 females) completed measures of each of these traits as well as a measure of preference for specific sex acts (along with providing information on their sexual orientation). The traits predicted variability in preference for sex acts – with mating intelligence being the most predictive (for instance, mating intelligence was positively related to preference for vaginal intercourse across the sexes). Sex differences emerged (e.g., males show a stronger preference for anal sex than do females). Discussion focuses on (a) sex differences in preference for sex acts along with (b) why the trait variables predicted preferences in sex acts.

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Openness, Fluid Intelligence, and Crystallized Intelligence: Toward an Integrative Model

Matthias Ziegler, Erik Danay, Moritz Heene, Jens Asendorpf, Markus Bühner

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Assessing Individual Differences in the Degree to Which People are Committed to Following Their Beliefs

Matthew A. Maxwell-Smith, Victoria M. Esses

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Publish (your data) or (let the data) perish! Why not publish your data too?

Jelte M. Wicherts, Marjan Bakker

The authors argue that upon publication of a paper, the data should be made available through online archives or repositories. Reasons for not sharing data are discussed and contrasted with advantages of sharing, which include abiding by the scientific principle of openness, keeping the data for posterity, increasing one’s impact, facilitation of secondary analyses and collaborations, prevention and correction of errors, and meeting funding agencies’ increasingly stringent stipulations concerning the dissemination of data. Practicing what they preach, the authors include data as an online appendix to this editorial. These data are from a cohort of psychology freshmen who completed Raven’s Advanced Progressive Matrices, tests of Numerical Ability, Number Series, Hidden Figures, Vocabulary, Verbal Analogies, and Logical Reasoning, two Big Five personality in- ventories, and scales for social desirability and impression management. Student’s sex and grade point average (GPA) are also included. Data could be used to study predictive validity of cognitive ability tests, Extraversion, Neuroticism, Conscientiousness, Openness to Experience, Agreeableness, and the general factor of personality, as well as sex differences, differential prediction, and relations between personality and intelligence.

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The variable nature of cognitive control: a dual mechanisms framework

Todd S. Braver

A core component of cognitive control – the ability to regulate thoughts and actions in accordance with internally represented behavioral goals – might be its intrinsic variability. In this article, I describe the dual mechanisms of control (DMC) framework, which postulates that this variability might arise from qualitative distinctions in temporal dynamics between proactive and reactive modes of control. Proactive control reflects the sustained and anticipatory maintenance of goal-relevant information within lateral prefrontal cortex (PFC) to enable optimal cognitive performance, whereas reactive control reflects transient stimulus-driven goal reactivation that recruits lateral PFC (plus a wider brain network) based on interference demands or episodic associations. I summarize recent research that demonstrates how the DMC framework provides a coherent explanation of three sources of cognitive control variation – intra-individual, inter-individual and between-groups – in terms of proactive versus reactive control biases.

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