Scaling a common assessment of associative ability: Development and validation of a multiple-choice compound remote associates task

The assessment of creativity as an individual difference has historically focused on divergent thinking, which is increasingly viewed as involving the associative processes that are also understood to be a key component of creative potential. Research on associative processes has proliferated in many sub-fields, often using Compound Remote Associates (CRA) tasks with an open response format and relatively small participant samples. In the present work, we introduce a new format that is more amenable to large-scale data collection in survey designs, and present evidence for the reliability and validity of CRA measures in general using multiple large samples. Study 1 uses a large, representative dataset (N = 1,323,480) to demonstrate strong unidimensionality and internal consistency (α = .97; ωt = .87), as well as links to individual differences in temperament, cognitive ability, occupation, and job characteristics. Study 2 uses an undergraduate sample (N = 685) to validate the use of a multiple-choice format relative to the traditional approach. Study 3 uses a crowdsourced sample (N = 357) to demonstrate high test-retest reliability of the items (r =.74). Finally, Study 4 uses a sample that overlaps with Study 1 (N = 1,502,922) to provide item response theory (IRT) parameters for a large set of high-quality CRA items that use a multiple-choice response mode, thus facilitating their use in future research on creativity, insight, and related topics.

Measuring maladaptive personality traits with the Structured Clinical Interview for DSM-IV Axis II Screening Questionnaire using a common metrics approach

The classification of personality disorder (PD) is undergoing a paradigm shift in which categorically defined specific PDs are being replaced by dimensionally defined maladaptive trait domains. To bridge the classificatory approaches, this study attempts to use items from the categorical PD model in DSM-IV to measure the maladaptive trait domains described in DSM-5 Section III/ICD-11. A general population sample comprising 1228 participants completed the Screening Questionnaire of the Structured Clinical Interview for DSM-IV Axis II (SCID-II-SQ), the Personality Inventory for DSM-5 (PID-5), and the anankastia scale of the Personality Inventory for ICD-11 (PiCD). Using item response theory models and a psychometric linking technique, SCID-II-SQ items were evaluated for their contribution to measuring maladaptive trait domains. The best discriminating items were then selected to derive proxy scales. We found that convergent validity of these proxy scales was in a similar range to that of other self-report measures for PD, except for the proxy scale for PiCD anankastia. However, only the proxy scale for negative affectivity showed acceptable reliability that would allow its application in research settings. Future studies should seek to establish a common metric between specific PDs and maladaptive trait domains using self-report measures with higher specificity or semi-structured interviews.

Self- and informant-reported personality traits and vaccination against COVID-19

As COVID-19 vaccines' accessibility has grown, so has the role of personal choice in vaccination, and not everybody is willing to vaccinate. Exploring personality traits' associations with vaccination could highlight some person-level drivers of, and barriers to, vaccination. We used self- and informant-ratings of the Five-Factor Model domains and their subtraits (a) measured approximately at the time of vaccination with the 100 Nuances of Personality (100NP) item pool (N = 56,575) and (b) measured on average ten years before the pandemic with the NEO Personality Inventory-3 (NEO-PI-3; N = 3,168). We tested individual domains' and either items' (in the 100NP sample) or facets' (in the NEO-PI-3 sample) associations with vaccination, as well as their collective ability to predict vaccination using elastic net models trained and tested in independent sample partitions. Although the NEO-PI-3 domains and facets did not predict vaccination ten years later, the domains correlated with vaccination in the 100NP sample, with vaccinated people scoring slightly higher on neuroticism and agreeableness and lower on openness, controlling for age, sex, and education. Collectively, the five domains predicted vaccination with an accuracy of r = .08. Associations were stronger at the item level. Vaccinated people were, on average, more science-minded, politically liberal, respectful of rules and authority, and anxious but less spiritual, religious, and self-assured. The 100NP items collectively predicted vaccination with r = .31 accuracy. We conclude that unvaccinated people may be a psychologically heterogeneous group and highlight some potential areas for action in vaccination campaigns.

A bottom-up approach dramatically increases the predictability of body mass from personality traits

Personality traits consistently relate to and allow predicting body mass index (BMI), but these associations may not be adequately captured with existing inventories' domains or facets. Here, we aimed to test the limits of how accurately BMI can be predicted from and described with personality traits. We used three large datasets (combined N ≈ 100,000) with nearly 700 personality assessment items to (a) empirically identify clusters of personality traits linked to BMI and (b) identify relatively small sets of items that predict BMI as accurately as possible. Factor analysis revealed 14 trait clusters showing well-established personality trait-BMI associations (disorganization, anger) and lesser-known or novel ones (altruism, obedience). Most of items' predictive accuracy (up to r = .24 here but plausibly much higher) was captured by relatively few items. Brief scales that predict BMI have potential clinical applications-for instance, screening for risk of excessive weight gain or related complications.

Development of a Public-Domain Measure of Two-Dimensional Rotation Ability and Preliminary Evidence for Discriminant Validity among Occupations

Despite their known influence in science, technology, engineering, and mathematics (STEM) fields, spatial abilities remain an underassessed aspect of cognition, particularly in educational settings. One explanation could be a lack of affordable, valid instruments for measuring various aspects of spatial ability. We evaluate the validity of a set of public-domain, algorithmically generated two-dimensional rotation items using a sample from the Synthetic Aperture Personality Assessment (SAPA) Project (N = 1,020,195). We examine the psychometric properties of the items and their relationship with various other cognitive abilities and personality traits. In addition, we identify the highest performing college majors and occupations on the 2D rotation items and on a set of 3D rotation items. Findings suggest strong unidimensionality for the 2D rotation items and the presence of lower-order factors which reflect differences across items in mental rotation demands. The highest scoring majors and occupations were similar-but not identical-across the 2D and 3D rotation measures and point to potentially meaningful differences across areas of expertise.

Assessing Dysfunctional Expectations in Posttraumatic Stress Disorder: Development and Validation of the Posttraumatic Expectations Scale (PTES)

Dysfunctional expectations are a particularly important subset of cognitions that influence the development and maintenance of various mental disorders. This study aimed to develop and validate a scale to assess dysfunctional expectations in posttraumatic stress disorder (PTSD), the "Posttraumatic Expectations Scale" (PTES). In a cross-sectional study, 70 PTSD patients completed the PTES, the Posttraumatic Cognitions Inventory (PTCI), as well as measures of the severity of symptoms of PTSD and depression. The results show that the PTES has excellent internal consistency and correlates significantly with the PTCI and PTSD symptom severity. A regression analysis revealed that the PTES explained variance of PTSD symptom severity above the PTCI, supporting the incremental validity of the PTES. While the original version of the PTES comprises 81 items, short scales were constructed using the BISCUIT (best items scales that are cross-validated, unit-weighted, informative and transparent) method. The current findings provide preliminary psychometric evidence suggesting that the PTES is an internally consistent and valid novel self-report measure in patients with PTSD. However, conclusions about the psychometric properties of the PTES are limited because of the absence of criterion-related validity, factor structure evidence, variability over time/response to intervention, and test-retest reliability. Future research should use the PTES in large-scale longitudinal studies to address these aspects to further validate the scale.

Toward Individualized Prediction of Binge-Eating Episodes Based on Ecological Momentary Assessment Data: Item Development and Pilot Study in Patients With Bulimia Nervosa and Binge-Eating Disorder

BACKGROUND

Prevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail.

OBJECTIVE

We developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data.

METHODS

We first derived a novel EMA-item set that covers a broad set of potential idiographic binge-eating antecedents from literature and an eating disorder focus group (n=11). The final EMA-item set (6 prompts per day for 14 days) was assessed in female patients with bulimia nervosa or binge-eating disorder. We used a correlation-based machine learning approach (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent) to select parsimonious, idiographic item subsets and predict binge-eating occurrence from EMA data (32 items assessing antecedent contextual and affective states and 12 time-derived predictors).

RESULTS

On average 67.3 (SD 13.4; range 43-84) EMA observations were analyzed within participants (n=13). The derived item subsets predicted binge-eating episodes with high accuracy on average (mean area under the curve 0.80, SD 0.15; mean 95% CI 0.63-0.95; mean specificity 0.87, SD 0.08; mean sensitivity 0.79, SD 0.19; mean maximum reliability of r_D 0.40, SD 0.13; and mean r_CV 0.13, SD 0.31). Across patients, highly heterogeneous predictor sets of varying sizes (mean 7.31, SD 1.49; range 5-9 predictors) were chosen for the respective best prediction models.

CONCLUSIONS

Predicting binge-eating episodes from psychological and contextual states seems feasible and accurate, but the predictor sets are highly idiographic. This has practical implications for mobile health and just-in-time adaptive interventions. Furthermore, current theories around binge eating need to account for this high between-person variability and broaden the scope of potential antecedent factors. Ultimately, a radical shift from purely nomothetic models to idiographic prediction models and theories is required.

Personalized Prediction of Behaviors and Experiences: An Idiographic Person-Situation Test

A longstanding goal of psychology is to predict the things that people do and feel, but tools to accurately predict future behaviors and experiences remain elusive. In the present study, we used intensive longitudinal data (N = 104 college-age adults at a midwestern university; total assessments = 5,971) and three machine-learning approaches to investigate the degree to which three future behaviors and experiences-loneliness, procrastination, and studying-could be predicted from past psychological (i.e., personality and affective states), situational (i.e., objective situations and psychological situation cues), and time (i.e., trends, diurnal cycles, time of day, and day of the week) phenomena from an idiographic, person-specific perspective. Rather than pitting persons against situations, such an approach allows psychological phenomena, situations, and time to jointly predict future behaviors and experiences. We found (a) a striking degree of prediction accuracy across participants, (b) that a majority of participants' future behaviors are predicted by both person and situation features, and (c) that the most important features vary greatly across people.

Machine Learning and Prediction in Psychological Assessment

Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.

Praxisorientierte Forschung in der Psychotherapie

Zusammenfassung. In den letzten Jahrzehnten hat sich durch randomisiert-kontrollierte Studien (RCTs) eine breite Evidenzbasis von Psychotherapie mit mittleren bis großen Effekten für verschiedene psychische Störungen gebildet. Neben der Bestimmung dieser Wirksamkeit („Efficacy“) ebneten Studien zur Wirksamkeit unter alltäglichen Routinebedingungen („Effectiveness“) historisch den Weg zur Entwicklung eines praxisorientierten Forschungsparadigmas. Im Beitrag wird argumentiert, dass im Rahmen dieses Paradigmas praxisbasierte Studien eine wertvolle Ergänzung zu RCTs darstellen, da sie existierende Probleme in der Psychotherapieforschung adressieren können. In der gegenwärtigen praxisorientierten Forschung liefern dabei neue Ansätze aus der personalisierten Medizin und Methoden aus der ‚Computational Psychiatry‘ wichtige Anhaltspunkte zur Optimierung von Effekten in der Psychotherapie. Im Kontext der Personalisierung werden bspw. klinische multivariable Prädiktionsmodelle entwickelt, welche durch Rückmeldeschleifen an Praktiker_innen kurzfristig ein evidenzbasiertes Outcome-Monitoring ermöglicht und langfristig das Praxis-Forschungsnetzwerk in Deutschland stärkt. Am Ende des Beitrags werden zukünftige Richtungen für die praxisorientierte Forschung im Sinne des ‘Precision Mental Health Care’ -Paradigmas abgeleitet und diskutiert.

Alterations in Emotional Diversity Correspond With Increased Severity of Attenuated Positive and Negative Symptoms in the Clinical High-Risk Syndrome

Background: Alterations in emotional functioning are a key feature of psychosis and are present in individuals with a clinical high-risk (CHR) syndrome. However, little is known about alterations in emotional diversity (i.e., the variety and relative abundance of emotions that humans experience) and clinical correlates in this population. Methods: Individuals meeting criteria for a CHR syndrome (N = 47) and matched healthy controls (HC) (N = 58) completed the modified Differential Emotions Scale (used to derive scores of total, positive, and negative emotional diversity) and clinical interviews (i.e., Structured Interview for Psychosis-Risk Syndromes). Results: Findings showed that the CHR group experienced lower levels of positive emotional diversity compared to HCs. Among the CHR individuals, lower levels of positive and higher levels of negative emotional diversity were associated with more severe attenuated positive and negative symptoms. Analyses controlled for mean levels of emotion and current antipsychotic medication use. Discussion: Results demonstrate that altered emotional diversity (in particular lower levels of positive and higher levels of negative emotional diversity) is a clinically relevant marker in CHR individuals, above and beyond alterations in mean levels of emotional experiences. Future studies may probe sources, downstream consequences, and potential modifiability of decreased emotional diversity in individuals at CHR.

Personality predictors of emergency department post-discharge outcomes

Personality traits are important predictors of health behaviors, healthcare utilization, and health outcomes. However, we know little about the role of personality traits for emergency department outcomes. The present study used data from 200 patients (effective Ns range from 84 to 191), who were being discharged from the emergency department at an urban hospital, to investigate whether the Big Five personality traits were associated with post-discharge outcomes (i.e., filling prescriptions, following up with primary care physician, making an unscheduled return to the emergency department). Using logistic regression, we found few associations among the broad Big Five domains and post-discharge outcomes. However, results showed statistically significant associations between specific Big Five items (e.g., “responsible”) and the three post-discharge outcomes. This study demonstrates the feasibility of assessing personality traits in an emergency medicine setting and highlights the utility of having information about patients’ personality tendencies for predicting post-discharge compliance.

Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT

Food craving (FC) peaks are highly context-dependent and variable. Accurate prediction of FC might help preventing disadvantageous eating behavior. Here, we examine whether data from 2 weeks of ecological momentary assessment (EMA) questionnaires on stress and emotions (active EMA, aEMA) alongside temporal features and smartphone sensor data (passive EMA, pEMA) are able to predict FCs ~2.5 h into the future in N = 46 individuals. A logistic prediction approach with feature dimension reduction via Best Item Scale that is Cross-Validated, Weighted, Informative and Transparent (BISCWIT) was performed. While overall prediction accuracy was acceptable, passive sensing data alone was equally predictive to psychometric data. The frequency of which single predictors were considered for a model was rather balanced, indicating that aEMA and pEMA models were fully idiosyncratic.