Although scholars agree that moral emotions are critical for deterring unethical and antisocial behavior, there is disagreement about how two prototypical moral emotions-guilt and shameshould be defined, differentiated, and measured. We addressed these issues by developing a new assessment-the Guilt And Shame Proneness scale (GASP)-that measures individual differences in the propensity to experience guilt and shame across a range of personal transgressions. The GASP contains two guilt subscales that assess negative behavior-evaluations (NBEs) and repair action tendencies following private transgressions and two shame subscales that assess negative self-evaluations (NSEs) and withdrawal action tendencies following publically-exposed transgressions. Both guilt subscales were highly correlated with one another and negatively correlated with unethical decision making. Although both shame subscales were associated with relatively poor psychological functioning (e.g., neuroticism, personal distress, low self-esteem), they were only weakly correlated with one another and their relationships with unethical decision making diverged. Whereas shame-NSE constrained unethical decision making, shame-withdraw did not. Our findings suggest that differentiating the tendency to make negative self-evaluations following publically-exposed transgressions from the tendency to hide or withdraw from public is critically important for understanding and measuring dispositional shame proneness. The GASP's ability to distinguish these two classes of responses represents an important advantage of the scale over existing assessments. Although further validation research is required, the present studies are promising in that they suggest the GASP has the potential to be an important measurement tool for detecting individuals susceptible to corruption and unethical behavior.

This chapter uses Cox and Klinger’s motivational model of alcohol use as a framework for reviewing research on motives for using alcohol, marijuana, and tobacco. Results of this review provide strong support for key premises underpinning this model in the alcohol literature, including that people drink alcohol to manage internal feeling states and to obtain valued social outcomes. Importantly, these motives may provide a final common pathway to alcohol use through which the influences of more distal variables are mediated. The research literature on motives for marijuana use revealed important similarities in the nature of motives underlying use and in the unique patterns of use and use-related consequences associated with specific motives. Research on tobacco use motives showed few similarities, with tobacco use being more habitual, automatic, and largely motivated by withdrawal cues, at least among more experienced and dependent users.

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

In Study 1, participants completed five extant shame and guilt proneness inventories based on different theoretical conceptions of the difference between shame and guilt. Factor analyses revealed that despite very different theoretical distinctions, the shame proneness subscales loaded on one factor, and the guilt proneness subscales loaded on one factor. In Study 2, we altered scale items so that hypothetical transgressions were committed in either public or private, and likelihood response options were either typical of a "shame-prone response" (negative selfevaluation; avoidance behavior) or a "guilt-prone response" (negative behavior-evaluation; approach behavior). Our findings indicate that shame and guilt proneness can be measured both by responses to transgressions (e.g., negative self-evaluation and avoidance responses vs.negative behavior-evaluation and approach responses) and the situational context in which the transgression occurs (e.g., public vs. private). We provide recommendations regarding optimal measurement of shame and guilt proneness. What are shame and guilt, and how do they differ? Within psychology, as well as everyday conversation, the terms shame and guilt are often used interchangeably. There is a general confusion about the distinctiveness of these emotions, possibly because of their many similarities. Both are negatively valenced, morality-based, self-conscious, and self-referential emotions (Tangney & Dearing, 2002), and both are associated with the desire to undo one's actions (Frijda, Kuipers, & ter Schure, 1989). In addition, both shame and guilt are experienced when one makes internal attributions about a personal transgression, and both are characterized by feelings of distress. Moreover, shame and guilt frequently co-occur: After committing a moral transgression, individuals often experience a heightened level of both emotions. KeywordsAdding to the confusion, people who are dispositionally prone to feeling shame are often prone to feeling guilt, and vice versa. Shame proneness and guilt proneness are traits that reflect individual differences in cognitive, affective, and behavioral responses to personal transgressions (Tangney & Dearing, 2002). In a widely-used scale of dispositional proneness to shame and guilt, the Test of Self-Conscious Affect (TOSCA; Tangney, Wagner, & Gramzow, 1989), shame and guilt proneness are correlated between .40-.50 (e.g., Tangney, Wagner, Hill-Barlow, Marschall, & Gramzow, 1996).Despite the similarities between shame and guilt, there are also a number of phenomenological differences. Individuals report that shame is associated with not living up to personal standards while guilt is associated with actions that harmed others or the violation of personal duties (Keltner & Buswell, 1997). In recalled experiences of shame, individuals report blushing, higher distress levels, and feeling self-conscious and small. In recalled experiences of Shame Proneness and Guilt Proneness 4 guilt, individuals report having done something wrong, wishing the ...

Advances in digital technology have put music libraries at people’s fingertips, giving them immediate access to more music than ever before. Here we overcome limitations of prior research by leveraging ecologically valid streaming data: 17.6 million songs and over 662,000 hr of music listened to by 5,808 Spotify users spanning a 3-month period. Building on interactionist theories, we investigated the link between personality traits and music listening behavior, described by an extensive set of 211 mood, genre, demographic, and behavioral metrics. Findings from machine learning showed that the Big Five personality traits are predicted by musical preferences and habitual listening behaviors with moderate to high accuracy. Importantly, our work contrasts a recent self-report-based meta-analysis, which suggested that personality traits play only a small role in musical preferences; rather, we show with big data and advanced machine learning methods that personality is indeed important and warrants continued rigorous investigation.