Blind spots in the search for happiness: Implicit attitudes and nonverbal leakage predict affective forecasting errors

A Systematic Review Of Implicit Attitudes And Their Neural Correlates In Eating Behaviour

An increasing number of studies suggests that implicit attitudes toward food and body shape predict eating behaviour and characterize patients with eating disorders (EDs). However, literature has not been previously analysed, thus differences between patients with EDs and healthy controls and the level of automaticity of the processes involved in implicit attitudes are still matters of debate. The present systematic review aimed to synthetize current evidence from papers investigating implicit attitudes towards food and body in healthy and EDs populations. PubMed, EMBASE (Ovid), PsycINFO, Web of Science and Scopus were systematically screened and 183 studies using different indirect paradigms were included in the qualitative analysis. The majority of studies reported negative attitudes towards overweight/obese body images in healthy and EDs samples and weight bias as a diffuse stereotypical evaluation. Implicit food attitudes are consistently reported as valid predictors of eating behaviour. Few studies on the neurobiological correlates showed neurostimulation effects on implicit attitudes, but the automaticity at brain level of implicit evaluations remains an open area of research. In conclusion, implicit attitudes are relevant measures of eating behaviour in healthy and clinical settings, although evidence about their neural correlates is limited.

Trait-Unconsciousness, State-Unconsciousness, Preconsciousness, and Social Miscalibration in the Context of Implicit Evaluation

Implicit evaluations are often assumed to reflect “unconscious attitudes.” We review data from our lab to conclude that the truth of this statement depends on how one defines “unconscious.” A trait definition of unconscious according to which implicit evaluations reflect cognitions that are introspectively inaccessible at all times appears to be inaccurate. However, when unconscious is defined as a state which cognitions can be in at specific times, some data suggest that the cognitions reflected on implicit evaluations may sometimes unfold without direct awareness, in that people seem to rarely pay attention to them. Additionally, people appear to be miscalibrated in their reports in that they construe even conscious biases in self-serving ways. This analysis suggests that implicit evaluations do not reflect unconscious cognitions per se, but rather awareness-independent cognitions that are often preconscious and miscalibrated. Discussion centers on the meaning of this analysis for theory and application.

You won't regret it (or love it) as much as you think: impact biases for everyday health behavior outcomes

When predicting the future, people tend to overestimate the intensity of their emotions, a phenomenon known as the impact bias. Design: In two studies, we examined the impact bias for health outcomes. In Study 1, participants were randomized to think about a negative health outcome in the future or one in the past. In Study 2, participants came to the laboratory and were asked to predict and report their emotions surrounding an actual health outcome (consuming an unhealthy food). Results: In both studies, an impact bias emerged. In Study 1, participants thinking about an outcome in the future estimated more negative emotion than those thinking about an outcome in the past. In Study 2, when facing an actual health outcome, participants anticipated more negative and positive emotion than they experienced. Impact biases were also associated with behavioral motivation - desire to change the outcome (Study 1) and increased preventive intentions (Study 2). Additional analyses revealed that regret was a particularly important emotion. Conclusion: Although research has highlighted an impact bias for severe health outcomes like disease, these studies provide evidence of an impact bias for health outcomes generally. They also suggest that the bias may have implications for behavior intentions.

Interventions to Engage Affective Forecasting in Health-Related Decision Making: A Meta-Analysis

Background

People often use affective forecasts, or predictions about how a decision will make them feel, to guide medical and health decision making. However, these forecasts are susceptible to biases and inaccuracies that can have consequential effects on decision making and health.

Purpose

A meta-analysis was performed to determine the effectiveness of intervening to address affective forecasting as a means of helping patients make better health-related choices.

Methods

We included between-subjects experimental and intervention studies that targeted variables related to affective forecasting (e.g., anticipated regret, anticipated affect) as a means of changing health behaviors or decisions. We determined the overall effect of these interventions on targeted affective constructs and behavioral outcomes, and whether conceptual and methodological factors moderated these effects.

Results

A total of 133 independent effect sizes were identified from 37 publications (N = 72,020). Overall, affective forecasting interventions changed anticipated regret, d = 0.24, 95% confidence interval (CI) (0.15, 0.32), p < .001, behavior, d = 0.29, 95% CI (0.13, 0.45), p < .001, and behavioral intentions, d = 0.19, 95% CI (0.11, 0.28), p < .001, all measured immediately postintervention. Interventions did not change anticipated positive and negative affect, and effects on intentions and regret did not extend to follow-up time points, ps > .05. Generally, effects were not moderated by conceptual model, intervention intensity, or behavioral context.

Conclusions

Affective forecasting interventions had a small consistent effect on behavioral outcomes regardless of intervention intensity and conceptual framework, suggesting such constructs are promising intervention targets across several health domains.

Realistic affective forecasting: The role of personality

Affective forecasting often drives decision-making. Although affective forecasting research has often focused on identifying sources of error at the event level, the present investigation draws upon the "realistic paradigm" in seeking to identify factors that similarly influence predicted and actual emotions, explaining their concordance across individuals. We hypothesised that the personality traits neuroticism and extraversion would account for variation in both predicted and actual emotional reactions to a wide array of stimuli and events (football games, an election, Valentine's Day, birthdays, happy/sad film clips, and an intrusive interview). As hypothesised, individuals who were more introverted and neurotic anticipated, correctly, that they would experience relatively more unpleasant emotional reactions, and those who were more extraverted and less neurotic anticipated, correctly, that they would experience relatively more pleasant emotional reactions. Personality explained 30% of the concordance between predicted and actual emotional reactions. Findings suggest three purported personality processes implicated in affective forecasting, highlight the importance of individual-differences research in this domain, and call for more research on realistic affective forecasts.

Evaluation of forecasting models for air cargo

Purpose

– Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes.

Design/methodology/approach

– A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized.

Findings

– Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors.

Originality/value

– The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.

Individual Well-Being and Relationship Maintenance at Odds

Partner aggression negatively affects well-being in ways that the people experiencing aggression may not expect. Individuals ( n = 171) who reported aggression by their current partner completed a longitudinal study. At the start of the study, participants rated their current happiness and how happy they expected to feel if their relationship were to end. The data revealed a partner aggression–unhappiness link and evidence of misforecasting future happiness: Committed individuals overestimated their unhappiness after a breakup because they expected worse things from a breakup than actually materialized, and people who experienced high partner aggression overestimated their unhappiness because they became more happy without the partner than they had expected. Forecasting unhappiness after a breakup predicted staying in an aggressive relationship. In aggressive relationships, bias occurs not only in forecasting future happiness but also in misreading how badly one feels now.