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. 2018 Mar;29(3):379-389.
doi: 10.1177/0956797617737129. Epub 2018 Jan 30.

Concern for Others Leads to Vicarious Optimism

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Free PMC article

Concern for Others Leads to Vicarious Optimism

Andreas Kappes et al. Psychol Sci. .
Free PMC article

Abstract

An optimistic learning bias leads people to update their beliefs in response to better-than-expected good news but neglect worse-than-expected bad news. Because evidence suggests that this bias arises from self-concern, we hypothesized that a similar bias may affect beliefs about other people's futures, to the extent that people care about others. Here, we demonstrated the phenomenon of vicarious optimism and showed that it arises from concern for others. Participants predicted the likelihood of unpleasant future events that could happen to either themselves or others. In addition to showing an optimistic learning bias for events affecting themselves, people showed vicarious optimism when learning about events affecting friends and strangers. Vicarious optimism for strangers correlated with generosity toward strangers, and experimentally increasing concern for strangers amplified vicarious optimism for them. These findings suggest that concern for others can bias beliefs about their future welfare and that optimism in learning is not restricted to oneself.

Keywords: altruism; learning bias; open data; open materials; optimism; other-regarding preferences; preregistered.

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article.

Figures

Fig. 1.
Fig. 1.
Vicarious optimism task. On each trial (a), participants imagined a negative event happening to a target individual (friend or stranger), estimated the likelihood of the event happening to the target, learned about the average likelihood for that event, and finally reestimated the likelihood. A good-news event (b) was defined by a first estimate that was higher than the average likelihood. The estimation error was then calculated by subtracting the first estimate from the average likelihood, and the update was calculated by subtracting the first estimate from the second estimate. The learning rate, which indicated how well the estimation error predicted the subsequent update, was the unstandardized regression coefficient indicating the strength of the relationship between the estimation error and the subsequent update. A bad-news event (c) was defined by a first estimate that was lower than the average likelihood. The estimation error was then calculated by subtracting the average likelihood from the first estimate, and the update was calculated by subtracting the second from the first estimate. Again, the learning rate indicated how well the estimation error predicted the subsequent update.
Fig. 2.
Fig. 2.
Results from Study 1 (N = 68): mean learning rate as a function of whether participants received good news versus bad news, separately for the self and friend conditions. The learning rate is the unstandardized regression coefficient indicating the strength of the relationship between the estimation error and the subsequent update. Error bars represent standard errors of the mean. Asterisks indicate significant differences between conditions (p < .05).
Fig. 3.
Fig. 3.
Results from (a) Study 2a (N = 170), (b) Study 2b (N = 470), and Study 3 (N = 285): mean learning rate as a function of whether participants received good versus bad news. Results are shown separately for the friend, identifiable-stranger, and unidentifiable-stranger conditions (Study 2a); the identifiable- and unidentifiable-stranger conditions (Study 2b); and the liked- and disliked-stranger conditions (Study 3). The learning rate is the unstandardized regression coefficient indicating the strength of the relationship between the estimation error and the subsequent update. Error bars represent standard errors of the mean. Asterisks indicate significant differences between conditions (p < .05).
Fig. 4.
Fig. 4.
Results from Study 4 (N = 76): mean donation amount as a function of participants’ learning rate bias. Error bars represent standard errors of the mean. The asterisk indicates that the difference between conditions was significant (p < .05).

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