• Aucun résultat trouvé

Affective Forecasting About Future Events: My Friend Better Than Me?

N/A
N/A
Protected

Academic year: 2021

Partager "Affective Forecasting About Future Events: My Friend Better Than Me?"

Copied!
1
0
0

Texte intégral

(1)

PARTICIPANTS

N = 138 (80 females)

Age: 18 – 33 years (23.3 mean age)

MATERIAL

STEP 1

Participants were recruited in the same class and were asked to work in pairs of friends. Then, they predicted the emotional reactions of themselves and their friend when they will obtain results for one exam two months later on a 7-point Likert scale.

They should forecast these reactions for three possibilities:

if they will achieve low grade (< 4/10), middle grade (= 5 until 6/10), and high grade (> 7/10).

Additionally, they completed a personality test (Big Five Inventory) for themselves and for their friend anew.

STEP 2

Participants were contacted by SMS eight hours after that the results of this exam were available, and were requested to rate their current emotional feelings on the same 7-point Likert Scale.

Contact:

Virginie CHRISTOPHE Université de Liège

Department of Psychology: Cognition & Behavior Boulevard du Rectorat (B32)

B-4000 Liège, Belgium

Email: v.christophe@student.ulg.ac.be

Affective Forecasting About Future Events:

My Friend Better Than Me?

Previous experiments have demonstrated that we often fail to make accurate affective forecasting for future. One of the best known biases that lead us to make wrong predictions is called Impact Bias (overvaluation of the emotional impact of specific events). Another bias is the Personality Neglect bias that is the neglect of our dispositional traits to predict our emotional reactions. However, few are known about empathic forecasting as well as the influence of events intensity.

Because they have an external point of view, could our friends avoid these biases and be better predictors than us? So, is that all events cause the same degree of prediction error?

This study replicates previous findings demonstrating the Impact Bias. Indeed, we tend to make wrong predictions about our emotional state but only for extreme events whereas moderate ones seem to be not affected by such bias. This stresses the role of events intensity.

While external people like friends correctly assess our personality, they also fail in predicting our future emotions. But in a similar manner, theses errors of prediction depend of events intensity. Despite an external point of view, empathic forecasting appear sensitive to the same bias that personal affective forecasting.

II

nd

International Conference on Time Perspective

Warsaw – 29

th

July to 1

th

August 2014

Grade

Bad Middle Good

Emotio nal R ea cti on 1 2 3 4 5 6 7 Affective Forecasting Empathic Forecasting Experienced Feeling *** *** *** **

PREDICTIONS AND EXPERIENCED FEELING

Agreeability Conscientiousness Extraversion Neuroticism Openness

r=0.31 (p=0.001) r=0.23 (p<0.01) r=0.51 (p<0.001) r=0.33 (p=0.001) r=0.41 (p<0.001)

PERSONNAL AND FRIEND’S CORRELATIONS TO BFI TEST

Références

Documents relatifs

Comme le suggère Laurent Siproudhis d ’ emblée dans le titre de son dernier éditorial pour la revue Colon &amp; Rectum (vous noterez en passant la grande souplesse d ’ esprit de

Keywords: TEL communities of practice, extended meeting, ambient video awareness, videoconferencing, foreground channel, backchannel, synchronous communication.. 1

I shall describe three configurations of uncertainty, each associated with its own field of opportunities and frames of reference, which, at different historical moments,

Figure 12: Evolution of the global mean water vapor column (left) and the precipitation (right), during the year following the outflow channel reference event...

Enfin le chapitre VIII est consacré à la conception d'un schéma réactionnel susceptible de rendre compte de l'ensemble des transformations subies par les charbons durant

Moreover, it performs automatic analysis of financial indexes to identify relevant events related to the stock market.. Then, sequential pattern mining is used to predict

This was followed by problem solving support with 66%, talk aloud prompts with 59%, reflective prompts with 52%, task sequence prompts with 37%, affect boosts with 32%, and the

In the kinetic equation, we expand the gyroaveraged distribution function into a Hermite-Laguerre basis, and express the moments of the collision operator in a series of products of