Trait Dimensions Research Articles

Influence of false beliefs and empathy on white lies among children with mild intellectual disabilities: focusing on trait and state perspectives

Children with mild intellectual disabilities often exhibit poor social skills owing to intellectual impairments. This makes it essential to enhance their communication abilities. This study provides a novel contribution by systematically investigating the effects of false beliefs and empathy on white lie behavior among children with mild intellectual disabilities, considering both state and trait dimensions. Experiment 1 examined the impact of trait-level false beliefs and trait-level empathy on trait-level white lies. The results demonstrated that trait-level false beliefs and trait-level empathy both significantly promoted white lie behavior. Experiment 2 explored the influence of state-level false beliefs and state-level empathy on state-level white lies and found similar positive effects. By integrating both trait and state perspectives, this research fills a gap in the literature on white lie behavior in children with mild intellectual disabilities and uncovers the mechanisms through which false beliefs and empathy operate in different contexts. These findings offer comprehensive educational and intervention strategies to improve social adaptation in children with mild intellectual disabilities.

Everyday State Attachment: Dynamic Features and Role of Trait Attachment.

Attachment research has traditionally focused on traits, enhancing our understanding of attachment-related individual differences. However, to chart the dynamic properties of the attachment system, more research is needed on the within-person fluctuation of attachment states. In this ecological momentary assessment (EMA) study, we examined (a) the associations between the baseline, variability, and inertia of each state attachment dimension (security, avoidance, and anxiety) and (b) how trait attachment (anxiety and avoidance) predicts these dynamic features. In two adult samples (Ns = 122 and 127), trait attachment dimensions were first assessed using Experiences in Close Relationships-Revised. Then, attachment states were assessed seven or ten times a day over 1 week (4629 and 5322 successful EMA observations). For state security, individuals with high baseline exhibited lower variability. In contrast, for state avoidance, those with high baseline showed higher variability. Both trait attachment anxiety and avoidance predicted lower baseline and higher variability of state security. Moreover, both trait dimensions predicted higher baselines of the corresponding states. Our findings provide insights into the real-time regulatory dynamics of the attachment system and their interconnection with trait attachment, underscoring the importance of baseline and variability in understanding how attachment manifests in everyday life.

Child Maltreatment Increases the Risk for Callous-Unemotional Traits: A Three-Level Meta-Analytic Review.

Callous-unemotional (CU) traits are characterized by lack of remorse and guilt, lack of empathy, and shallow and deficient emotions. A growing body of research has examined the association between child maltreatment and CU traits. However, empirical findings on this association are inconclusive. Therefore, this meta-analytic review aimed to examine the overall association between child maltreatment and CU traits and potentially moderating variables. After searching five databases (Web of Science, ScienceDirect, PubMed, MEDLINE, and CNKI), a total of 37 studies (N = 26,010 participants) yielding 165 effect sizes were synthesized in three-level meta-analytic models. Results showed that child maltreatment is significantly and positively related to CU traits (mean r = .183; p < .001). Moderator analyses showed that this association is larger in females than in males, larger in community samples (r = .243) than in clinical samples (r = .073), and in terms of CU trait dimensions stronger for callousness (r = .251) than for uncaring (r = .134) and unemotional dimensions (r = .050). It is concluded that child maltreatment is a risk factor for the development of CU traits and discussed that interventions for CU traits in antisocial groups should be aware of the environmental influence of interpersonal trauma resulting from childhood maltreatment experiences.

Trait-based ecology, trait-free ecology, and in between.

Trait-based ecology has become a popular phrase. But all species have traits, and their contributions to ecological processes are governed by those traits. So then, is not all ecology trait-based? Actually, there do exist areas of ecology that are consciously trait-free, such as neutral theory and species abundance distributions. But much of ecology could be considered actually or potentially trait-based. A spectrum is described, from trait-free through trait-implicit and trait-explicit to trait-centric. Trait-centric ecology includes positioning ecological strategies along trait dimensions, with a view to inferring commonalities and to generalizing from species studied in more detail. Trait-explicit includes physiological and functional ecology, and areas of community ecology and ecosystem function that invoke traits. Trait-implicit topics are those where it is important that species are different, but formulations did not initially characterize the differences via traits. Subsequently, strands within these trait-implicit topics have often moved towards making use of species traits, so the boundary with trait-explicit is permeable. Trait-based ecology is productive because of the dialogue between understanding processes in detail, via traits that relate most closely, and generalizing across many species, via traits that can be compared widely. An enduring key question for trait-based ecology is which traits for which processes.

Personality Traits and Quality of Life Among Older Adults: The Chain Mediating Effects of Perceived Social Support and Positive Coping Styles

The pathways between personality traits and older adults’ quality of life (QOL) have been well studied. However, perceived social support and positive coping styles should not be ignored by older adults’ QOL. Hence, this study examines the chain mediating role of perceived social support and positive coping styles between personality traits and older adults’ QOL. In total, 230 older individuals (Mean age = 69.43, SD age = 7.23, age range from 60 to 93) participated in this study. All older people provided a rating of QOL and perceived social support, the Chinese Big Five personality, and positive coping styles. (1a) the five personality traits of older adults were significantly associated with QOL; (2b) perceived social support mediated the relationship between three personality traits (neuroticism, extraversion, and agreeableness) and QOL; (3c) positive coping styles played a mediator role in the personality traits (neuroticism, extraversion, and agreeableness) and QOL; (4d) perceived social support and positive coping styles had a significant chain mediating effect on the three dimensions of personality traits (neuroticism, extraversion, and agreeableness) and QOL. As mediating influences, social support and positive coping styles can be applied to efforts to promote QOL for older adults who share the personality traits of neuroticism, extraversion, and agreeableness.

The third dimension of alpine plant leaf traits is related to cold-tolerance

Alpine plants possess unique traits to adapt alpine environments. Whether leaf trait relationships of alpine plants can be captured by the two trait dimensions of organ size and resource economics is unknown. We hypothesized that, beyond the trait dimensions of leaf size and resource economics, non-structured carbohydrates (NSC) would reflect a dimension of cold-tolerance in alpine plants. To test this hypothesis, we measured 12 leaf traits critical to leaf construction and growth in 143 species across 7 sites ranging from alpine steppes to alpine meadows along an environmental gradient on the Tibetan Plateau. Furthermore, a cold resistance experiment was conducted at one of these sites to estimate the lethal temperature causing 50% frost damage (LT50) of 11 alpine species. The majority of variations in 12 leaf traits of alpine plants were captured by three trait axes, in which leaf carbon (LCC) and NSC (including starch, LSC and soluble sugars, LSS) were clustered in a new dimension (PC3) beyond leaf size and structure, and resource economics. Although LCC, LSC and LSS all showed negative correlations with MAT, a significant negative correlation was only found between LSS and LT50. It indicated that PC3 was able to reflect the cold-tolerance of alpine plants to some extent, in which LSS was the most critical trait. The storage and transformation of NSC under stressful conditions could reflect a dimension of long-term metabolic adaptation and cold-tolerance, which is an extension of the resource-utilization strategy beyond construction cost and growth.

The Influence of Personality on Music Preference Across Geographic Locations

How does personality affect music preference? The Big Five Theory of Personality is used to answer this question as it is the most widely used scientific method of assessing personality. The Big 5 comprises five broad trait dimensions: extraversion, openness, agreeableness, conscientiousness, and neuroticism. Most studies on music preference involve separating music into different genres and assessing preference. The main correlates of the Big 5 and music preference are discussed. Also, some research has shown that geography matters in the link between personality and music preference, while others do not.

Corporate governance and financial distress in China a multi-dimensional nonlinear study based on machine learning

Promoting governance efficiency is crucial for preventing corporate financial distress. However, previous research has been constrained by limited dimensions of governance predictors and insufficient linear estimations to predict financial distress. To address this issue, this study gathers 37 corporate governance indicators of Chinese publicly listed firms from 2009 to 2022 in the dimensions of ownership structure, board features and executive traits. The LightGBM machine learning approach is then used to compare the predicting power of these individual indicators, as well as the predicting power of the dimensions. The SHAP (SHapley Additive exPlanations) method is further adopted to conduct an in-depth interpretability analysis upon the established prediction. Our approach identifies the nonlinear effects of important corporate governance indicators on financial distress and prioritizes those indicators based on their impact levels. Our results show that the most influential indicator is institutional ownership, followed by managerial ownership and executive compensation disparity. In addition, the dimension of ownership structure has the highest predicting power among the three. Overall, our study provides new insights into how firms can optimize their corporate governance mechanisms to prevent financial distress.

Dispositional mindfulness and the nonjudgmental regard towards others: the role on sexual prejudice and transphobia among cisgender heterosexual people

Although several studies examined the relationship between high levels of dispositional mindfulness and low scores of prejudices referred to various social groups, the findings have sometimes been inconsistent. Such inconsistency may be explained by the fact that much research has relied exclusively on self-oriented dispositional mindfulness as predictor of intergroup biases, thus underestimating the role of the nonjudgmental regard towards others. In the current study, we explored the associations between dispositional mindfulness related to the self, Nonjudgmental Regard towards Others (NRO), sexual prejudice, genderism/transphobia and gender bashing. We expected that higher scores on both dimensions of NRO traits (i.e. Ideological Acceptance and Emotion Acceptance) would be negatively associated with sexual prejudice, genderism/transphobia and gender bashing over mindful traits related to the self. 911 cis-heterosexual participants recruited through Prolific completed the online questionnaire. Dominance analyses were conducted to test our hypotheses. The results partially confirmed the predictions. NRO Emotion Acceptance was the best predictor of sexual prejudice, genderism/transphobia and gender bashing among the mindful traits, above socio-anagraphics, religiosity, political, and ideological variables. However, NRO Ideological Acceptance was not associated with the measures of prejudice. These results suggest that having a nonjudgmental attitude toward others’ emotions bay be a better factor in reducing prejudice. Limitations, future research directions, and social implications are discussed.

Association of Production and Selected Dimensional Conformation Traits in Holstein Friesian Cows.

The objective of this study was to estimate the heritability of dairy production traits and that for dimensional traits and to calculate the correlation between the two heritability values in a Holstein Friesian cow herd bred in Hungary. Data of 15,032 Holstein Friesian cows born in the period 2008-2018 from 666 sires were collected for the study in 6 large dairy herds. Among the conformation traits, stature (ST), chest width (CW), body depth (BD), and rump width (RW), and for production traits, in the first lactation of cows, the 305-day milk yield (MY), milk butterfat yield (FY), and milk protein yield (MY) were evaluated. Heritability estimates of ST, CW, BD, and RW were 0.49, 0.25, 0.31, and 0.30, and those of MY, FY, and PY were 0.40, 0.35, and 0.30, respectively. BD and RW had no phenotypic (b = -0.01) or genetic (b = 0.00-0.01) change. The production traits (MY, FY, PY) increased to a greater extent (b = 2.2-43.3) than the examined conformation traits over time. Consequently, it is indicated that the selection for dairy production did not result in an increase in the studied dimensional traits.

Leaf trait networks shift toward high modularity during the succession of a subtropical forest, in southwest China

During the process of forest succession, coexisting plant species fulfill diverse functions by modifying the interrelationships among functional traits. Examining the interrelationships of functional traits across multiple dimensions would enable a comprehensive understanding of the plants’ adaptive strategies during forest succession. Plant trait networks (PTNs) comprehensively capture and visualize these intricate relationships among functional traits. Nonetheless, the changes in PTNs during forest secondary succession remain unclear. In this study, we measured 24 functional traits related to leaf resource acquisition, photosynthesis, hydraulics, and physical defense across tree species at three successional stages: young forest (YF), intermediate forest (IF), and old-growth forest (OF) in the Ailao Mountains, Southwest China. We established leaf trait networks (LTNs) to investigate the interrelationships among functional traits during forest succession. Our results revealed a decrease in the Edge Density decreased and an increase in the Modularity as succession advanced. In the YF stage, leaf nitrogen, photosynthetic rate, and force to punch were the hub traits; whereas, canopy exposure and leaf dry mass were highly connected traits in the IF and OF stages, respectively. The connectivity of highly connected traits was related to the plant’s life-history strategies and environmental adaptations at different successional stages. In terms of the importance of the four trait dimensions, resource acquisition and photosynthetic traits consistently demonstrated strong connectivity within LTNs throughout the succession. We concluded that co-occurring tree species adapted to the changing environmental conditions by adjusting trait modules through alternative strategies during secondary succession. Specifically, resource acquisition and trait coordination were more advantageous in the YF stage, while higher modularity offered greater opportunities for niche differentiation in the OF stage. This study provides new insights into exploring the mechanisms of forest succession through the analysis of trait correlation networks.

Locating triarchic model constructs in the hierarchical structure of a comprehensive trait-based psychopathy measure: Implications for research and clinical assessment.

The triarchic model posits that distinct trait constructs of boldness, meanness, and disinhibition underlie psychopathy. The triarchic model traits are conceptualized as biobehavioral dimensions that can be assessed using different sets of indicators from alternative measurement modalities; as such, the triarchic model would hypothesize that these traits are not confined to any one item set. The present study tested whether the triarchic model dimensions would emerge from a hierarchical-structural analysis of the facet scales of the Elemental Psychopathy Assessment (EPA), an inventory designed to comprehensively index psychopathy according to the five-factor personality model. Study participants (Ns = 811, 170) completed the EPA and three different scale sets assessing the triarchic traits along with criterion measures of antisocial/externalizing behaviors. Bass-ackwards modeling of the EPA facet scales revealed a four-level structure, with factors at the third level appearing similar to the triarchic trait dimensions. An analysis in which scores for the Level-3 EPA factors were regressed onto corresponding latent-trait dimensions defined using the different triarchic scale sets revealed extremely high convergence (βs = .84-.91). The Level-3 EPA factors also evidenced validity in relation to relevant criteria, approximating and sometimes exceeding that evident for the Level-4 EPA factors. Together, these results indicate that the triarchic trait constructs are embedded in a psychopathy inventory designed to align with a general personality model and effectively predict pertinent external criteria. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Internal consistency of measures for ICD-11 personality disorder severity and traits: A systematic review and meta-analysis.

A number of measures and scales have been developed for the ICD-11 personality disorder (PD) diagnosis, including severity and trait dimensions. The present systematic review and meta-analysis sought to evaluate the internal consistency of these measures across different populations and cultures. A systematic search was conducted across four databases where relevant studies were subjected to explicit eligibility criteria resulting in 49 included studies and 370 effect sizes. Study characteristics were tabulated, their methodological quality was evaluated, and findings were synthesized using random effects meta-analysis. Findings overall indicated that measures of ICD-11 PD severity and trait domains have adequate levels of internal consistency (α/ω = 0.82, 95% CI [0.81; 0.83], I2 = 97.3%). Aspects such as sample, country, language, format, and measured construct were significant sources of variation. Additional meta-analyses revealed that some measures performed better than others for certain dimensions. Internal consistency was overall supported across ICD-11 measures of severity and trait domains. Future research should further investigate the interrater reliability, test-retest reliability and stability, and alignment with interview-based PD diagnoses.

Diagnostic and Statistical Manual of Mental Disorders, fifth edition, personality disorders and the alternative model: Prediction of naturalistically observed behavior, interpersonal functioning, and psychiatric symptoms, 1 year later.

Traditional personality disorders (PDs; e.g., Diagnostic and Statistical Manual of Mental Disorders, fifth edition [DSM-5] Section II PDs), as well as dimensional traits (e.g., alternative model for PD [AMPD]), offer unique advantages in personality pathology assessment. However, very little is known about how these systems compare in predicting observable behavior. This study compares self-report ratings of PD symptoms (i.e., Structured Clinical Interview for DSM-IV PD) with self-reports of AMPD traits (i.e., Personality Inventory for DSM-5) in predicting clinical outcomes, 1 year later, via three different methods: (a) naturalistically observed psychosocial functioning (i.e., electronically activated recorder [EAR]), (b) informant-reported interpersonal functioning (i.e., Inventory of Interpersonal Problems-32), and (c) self-reported suicidality (SI), depression, anxiety, and substance use symptoms (i.e., Psychiatric Diagnostic Screening Questionnaire). Data were analyzed from 72 individuals in current or recent psychiatric treatment meeting diagnosis for at least one PD. Results showed that DSM Section II PD and AMPD ratings yielded meaningful and comparable predictions of naturalistically observed EAR variables and informant-rated interpersonal functioning. The AMPD appeared to offer slight advantages in the prediction of EAR-observed negative affect, hostile words, and informant-rated interpersonal functioning, with clearer advantages at the facet level. Overall, these results provide tentative evidence that both DSM Section II PD and AMPD systems show meaningful links with clinical outcomes measured via multiple methods 1 year later, but with clearer advantages for the AMPD at the facet level. Moreover, results show that the EAR is a viable method for capturing naturalistically observed clinically meaningful, in vivo behavior of individuals exhibiting maladaptive personality patterns. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Relationships Between Autistic Trait Dimensions and Speech Understanding, Affective Sound Intolerance, and Self-Reported Hearing Difficulties

Relationships Between Autistic Trait Dimensions and Speech Understanding, Affective Sound Intolerance, and Self-Reported Hearing Difficulties

Deep learning aided computer vision system for automated linear type trait evaluation in dairy cows

The assessment of traits is important in determining production potential, reproductive performance, and overall health of dairy cows. The assessment of these traits typically involves visual inspection and manual measurement, which can be time-consuming, subject to bias, and potentially distressing for the animals. To address these challenges, convolutional neural networks (CNNs)-aided non-invasive computer vision system was developed in the present study. This system consists of a depth camera to acquire the RGB images and depth information of cows. The DeepLabV3+ model, having the ResNet50 model as a backbone, was utilized to segment the body parts of cows from RGB images. Image processing-based algorithms were developed to extract key pixel locations for each trait from these segmented images. The system estimated trait dimensions utilizing 3D data of respective key points. The mean-IoU (intersection-over-union) values for the developed segmentation models were 93.46%, 91.25%, and 99.27% for side-view, back-view traits, and stature, respectively. Additionally, the vision system was able to estimate the trait dimensions with mean absolute percentage error (MAPE) below 6.0%. For a few traits, MAPE, however, exceeded 10.0%, indicating higher error. Inaccurate segmentation, imprecise key point extraction, visual overlaps of specific body parts, and variations in cow postures contribute to such errors. The developed system attained a Ratio of Performance to Deviation (RPD) above 1.2 for all traits, indicating its ability to estimate the dimensions of traits efficaciously. Thus, the present study demonstrated the potential of a CNN-based computer vision-based system for automating the trait measurement process in cows.

Personality traits and co-occurrence of depressive symptoms and high BMI: a prospective cohort study.

We assess the associations between personality traits and co-occurrence of depressive symptoms and high BMI from adolescence to early adulthood. We employed a nationally representative cohort in China from 2010 to 2020 year. We included adolescents aged 10-19 years without depressive symptoms and unhealthy weight status (obesity, overweight, or thinness) at baseline and excluded those without any measurement of depressive symptoms or BMI at follow-ups. We assessed baseline personality traits in 7 dimensions of conscientiousness, openness, neuroticism, agreeableness, extraversion, self-esteem, and responsibility. We also assessed the combined effects of these 7 dimensions of personality traits by generating individual-level personality trait risk scores based on the weighted sum of all these 7 dimensions of personality traits. We measured the co-occurrence of depressive symptoms and high BMI using both a single measurement of depressive symptoms and BMI at the last follow-up and repeated measurements of them over 10 years. We used the multinomial logistic regression models to examine the exposure-outcome associations. At baseline, we included 1778 individuals (mean age: 14.4 year; female: 853 (48.0%)). At follow-ups, we observed increased risk of co-occurrence of depressive symptoms and high BMI per 1-SD increase in neuroticism score (1.95-2.38 odds ratio) or 1-SD decrease in self-esteem and conscientiousness (0.63-0.80 odds ratio; all P values < 0.05); we observed no evidence of associations between openness, agreeableness, extraversion, or responsibility and the risk of co-occurrence of depressive symptoms and high BMI (all P values > 0.05). For the combined effects of the 7 dimensions of personality traits, we found an elevated risk of co-occurrence of depressive symptoms and high BMI per 1-SD increase in the personality trait risk scores (OR (95% CI), single measurement at the last follow-up: 2.01, 1.66 to 2.43; trajectory classification using the repeated measurements 2.30, 1.55 to 3.42; average level using the repeated measurements: 2.27, 1.93 to 2.67). In this national cohort in China, personality traits were found to be associated with the co-occurrence of depressive symptoms and high BMI from adolescence to early adulthood. These findings highlight the importance of stratifying individuals based on their personality traits and providing targeted interventions for those at risk of comorbid depression and obesity.

Exploring Phylogenetic Signal in Multivariate Phenotypes by Maximizing Blomberg's K.

Due to the hierarchical structure of the tree of life, closely related species often resemble each other more than distantly related species; a pattern termed phylogenetic signal. Numerous univariate statistics have been proposed as measures of phylogenetic signal for single phenotypic traits, but the study of phylogenetic signal for multivariate data, as is common in modern biology, remains challenging. Here we introduce a new method to explore phylogenetic signal in multivariate phenotypes. Our approach decomposes the data into linear combinations with maximal (or minimal) phylogenetic signal, as measured by Blomberg's K. The loading vectors of these phylogenetic components or K-components can be biologically interpreted, and scatterplots of the scores can be used as a low-dimensional ordination of the data that maximally (or minimally) preserves phylogenetic signal. We present algebraic and statistical properties, along with two new summary statistics, KA and KG, of phylogenetic signal in multivariate data. Simulation studies showed that KA and KG have higher statistical power than the previously suggested statistic Kmult, especially if phylogenetic signal is low or concentrated in a few trait dimensions. In two empirical applications to vertebrate cranial shape (crocodyliforms and papionins), we found statistically significant phylogenetic signal concentrated in a few trait dimensions. The finding that phylogenetic signal can be highly variable across the dimensions of multivariate phenotypes has important implications for current maximum likelihood approaches to phylogenetic signal in multivariate data.

The Complexity of Borderline Personality Disorder: Network Analysis of Personality Factors and Defense Styles in the Context of Borderline Personality Organization.

Borderline personality disorder (BPD) is known to share characteristics with a variety of personality disorders (PDs) and exhibits diverse patterns of defense mechanisms. To enhance our understanding of BPD, it's crucial to shift our focus from traditional categorical diagnostics to the dimensional traits shared with other PDs, as the borderline personality organization (BPO) model suggests. This approach illuminates the nuanced spectrum of BPD characteristics, offering deeper insights into its complexity. While studies have investigated the comorbidity of BPD with other PDs, research exploring the relationship between various personality factors and defense mechanisms within BPD itself has been scarce. The present study was undertaken to investigate the complex interrelationships between various personality factors and defense styles in individuals diagnosed with BPD. Using a network analysis approach, data from 227 patients diagnosed with BPD were examined using the Defense Style Questionnaire and Personality Disorder Questionnaire-4+ for assessment. Intricate connections were observed between personality factors and defense styles. Significant associations were identified between various personality factors and defense styles, with immature defense styles, such as maladaptive and image-distorting being particularly prominent in BPD in the centrality analysis. The maladaptive defense style had the highest expected influence centrality. Furthermore, the schizotypal, dependent, and narcissistic personality factors demonstrated relatively high centrality within the network. Network analysis can effectively delineate the complexity of various PDs and defense styles. These findings are expected to facilitate a deeper understanding of why BPD exhibits various levels of organization and presents with heterogeneous characteristics, consistent with the perspectives proposed by the BPO.

Personality Inventory for the DSM-5 trait correlates with self-reported acts of rape

BackgroundSexual aggression researchers have identified “dark triad” personality traits as potential etiologic contributors to rape perpe-tration. The Personality Inventory for the DSM-5 (PID-5) canvasses trait dimensions that underlie a proposed hybrid diag-nostic model for the personality disorders.Participants and procedureAssociations between PID-5 item/facet/domain scores and self-reported acts of rape were examined in a national survey of men (N = 517) administered on a crowdsourcing platform.ResultsPervasive associations were found between various PID-5 risk indicators and acts of oral, anal, or vaginal physical contact with nonconsenting partner(s). While dimensional correlation coefficients were modest in size, odds ratios for elevated PID-5 facet dimensions were substantial. The odds of self-reported rape increased with the trait elevation count in a graded fash-ion. Selected PID-5 subscale dimensions (callousness, irresponsibility, grandiosity, intimacy avoidance, distractibility, and attention seeking) were significant correlates of self-reported rape. A subset of PID-5 items accounted for unshared variance in the criterion measures. Scores from the PID-5 appeared to provide useful risk indicators of sexual aggressiveness.ConclusionsScores from the PID-5 appeared to provide useful risk indicators of sexual aggressiveness. Men with personality disorder trait elevations appear far more likely to acknowledge past behavior that constitutes sexual assault. These interpretations were limited by the cross-sectional, retrospective, and self-report nature of these data.