Retribution and emotional regulation: The effects of time delay in angry economic interactions

Individuals driven by negative emotions often punish non-cooperators at a cost to themselves. The current research demonstrates that, although time delays can attenuate this effect, they can also produce unintended consequences. Five experiments investigated the effects of time delays and thought patterns on punishments in direct and third party interactions. The results show that time delays decreased punishment (Experiment 1) by reducing negative emotions (Experiments 2A and 2B). However, thought patterns during a delay were crucially important (Experiments 3A and 3B): People who engaged in a distraction task punished less; people who engaged in affective rumination punished more; and people who engaged in cognitive reappraisal were unaffected by a delay. These differences meant that, after a time delay, affective ruminators administered greater punishments than cognitive reappraisers or distracted individuals. Implications of these findings for managing punitive impulses via time delays are discussed.     

Time course of visual attention with emotional faces

When a briefly presented and then masked visual object is identified, it impairs the identification of the second target for several hundred milliseconds. This phenomenon is known as attentional blink or attentional dwell time. The present study is an attempt to investigate the role of salient emotional information in shifts of covert visual attention over time. Two experiments were conducted using the dwell time paradigm, in which two successive targets are presented at different locations with a variable stimulus onset asynchrony (SOA). In the first experiment, real emotional faces (happy/sad) were presented as the first target, and letters (L/T) were presented as the second target. The order of stimulus presentation was reversed in the second experiment. In the first experiment, identification of the letters preceded by happy faces showed better performance than did those preceded by sad faces at SOAs less than 200 msec. Similarly, happy faces were identified better than sad faces were at short SOAs in Experiment 2. The results show that the time course of visual attention is dependent on emotional content of the stimuli. The findings indicate that happy faces are associated with distributed attention or broad scope of attention and require fewer attentional resources than do sad faces.     

Time course of visual attention across perceptual levels and objects

Theories of shifts of visual attention based on attentional blink or dwell time do not directly address shifts of attention across different levels (global or local) involving multiple objects. Two experiments were conducted employing the attentional dwell time paradigm to investigate the shifts of visual attention between objects selected at same or different levels. Participants were instructed to identify two successive compound stimuli at a pre-specified level (global or local) presented at two different locations with variable SOA. The initial pair of locations in which the stimulus was presented was fixed in Experiment 1 but not in Experiment 2. Experiment 1 results showed very little impairment for second target identification when both the targets were at the global level. Attentional shift was better with both targets at the same level compared to different levels. Experiment 2 results showed that local followed by global target identification is difficult at short SOAs compared to other conditions. The results indicate that scope of attention affects the time course of visual attention. Global processing could be performed with very little capacity limitation simultaneously with distributed attention. The default mode of attention might be distributed and attention becomes focused for target identification. Different mechanisms may underlie shifts in focused attention between different locations and changes in attentional set required by changes in perceptual levels.     

It’s not what you said,it’s how you said it: An analysis of therapist vocal features during psychotherapy

Psychotherapy is a conversation, whereby, at its foundation, many interventions are derived from the therapist talking. Research suggests that the voice can convey a variety of emotional and social information, and individuals may change their voice based on the context and content of the conversation (e.g. talking to a baby or delivering difficult news to patients with cancer). As such, therapists may adjust aspects of their voice throughout a therapy session depending on if they are beginning a therapy session and checking in with a client, conducting more therapeutic ‘work’ or ending the session. In this study, we modelled three vocal features—pitch, energy and rate—with linear and quadratic multilevel models to understand how therapists’ vocal features change throughout a therapy session. We hypothesised that all three vocal features would be best fit with a quadratic function—starting high and more congruent with a conversational voice, decreasing during the middle portions of therapy where more therapeutic interventions were being administered, and increasing again at the end of the session. Results indicated a quadratic model for all three vocal features was superior in fitting the data, as compared to a linear model, suggesting that therapists begin and end therapy using a different style of voice than in the middle of a session.     

The Silent Treatment? Changes in patient emotional expression after silence

Psychotherapy can be an emotionally laden conversation, where both verbal and nonverbal interventions may impact the therapeutic process. Prior research has postulated mixed results regarding how clients emotionally react following a silence after the therapist is finished talking, potentially due to studying a limited range of silences with primarily qualitative and self-report methodologies. A quantitative exploration may illuminate new findings. Utilising research and automatic data processing from the field of linguistics, we analysed the full range of silence lengths (0.2–24.01 s), and measures of emotional expression—vocally encoded arousal and emotional valence from the works spoken—of 84 audio recordings of Motivational Interviewing sessions. We hypothesised that both the level and the variance of client emotional expression would change as a function of silence length; however, due to the mixed results in the literature, the direction of emotional change was unclear. We conducted a multilevel linear regression to examine how the level of client emotional expression changed across silence length, and an ANOVA to examine the variability of client emotional expression across silence lengths. Results of both analyses indicated that as silence length increased, emotional expression largely remained the same. Broadly, we demonstrated a weak connection between silence length and emotional expression, indicating no persuasive evidence that silence leads to client emotional processing and expression.     

A Cross-Cultural Comparison of Job Stressors and Reactions Among Employees Holding Comparable Jobs in Two Countries

The present study examined occupational stress in a cross-cultural context with a sample of female clerical employees from an Eastern and a Western culture, namely, India and the United States. An open-ended methodology was used to study occupation-specific stress. Two other variables that researchers have studied in the job stress process, locus of control and social support, were also examined. Large differences were obtained in the perception of the source of stress, the coping mechanisms, and reactions to the stressor. The implications of these findings are discussed.     

Estimating sample properties of principal components using SAS

An SAS (statistical analysis system) program for computing the standard errors of principal component coefficients (obtained using the covariance matrix) is presented. The standard errors are useful for checking the stability of the coefficients, which prevents the user from overinterpreting the results. An application of the usefulness of the program is illustrated through an example.     

The genetic basis of entrepreneurship: Effects of gender and personality

Extending previous research on the genetic underpinnings of entrepreneurship, we investigate gender differences in the genetic influences on the tendency of people to become entrepreneurs. We also examined two mediating variables through which genetic factors may impact this tendency: extraversion and neuroticism. Based on 1285 pairs of identical twins (449 male and 836 female pairs) and 849 pairs of same-sex fraternal twins (283 male and 566 female pairs), we found that females have a strong genetic influence and zero shared-environmental influences on their tendency to become entrepreneurs. In contrast, males show zero genetic influence, but a large shared-environmental influence. Extraversion and neuroticism mediate the genetic influences on women’s tendency to become entrepreneurs, whereas extraversion mediates shared-environmental influences on men’s tendency to become entrepreneurs. We discuss this sharp difference in genetic influences on entrepreneurship across gender groups and highlight the different challenges that men and women face in their entrepreneurial endeavors.     

The genetics of economic risk preferences

We examine the influence of genetics on economic risk preferences by administering a measure of these preferences to monozygotic (MZ) (i.e., identical) and dizygotic (DZ) (i.e., non‐identical) twin pairs. Our analysis supports a dominant genetic effect and virtually no additive genetic effect on economic risk preferences, with the heritability of preferences estimated at 0.63. These findings suggest that over half of the variation in such preferences can be explained by genetic factors, with the remainder of the variance explained by environmental influences not shared among sibling twins. We discuss the implications of our findings for the study of individual differences in economic risk preferences. Copyright © 2009 John Wiley & Sons, Ltd.