Self‐specific Optimism Bias in Belief Updating Is Associated with High Trait Optimism

Abstract

People learn more from new information when it leads to favorable future outlooks and thus can maintain optimism despite conflicting evidence. In two studies (N = 20 and 26), we investigated whether this optimism bias in belief updating is self‐specific by modifying a recently introduced learning paradigm. In each trial, participants had to estimate the probability of experiencing a negative future event, were then presented with the population base rate of that event, and were subsequently asked for a second, updated estimation. In half of the 88 trials with varying events, estimations were made for oneself, in the other half for a similar other. We tested whether the updates (differences between the first and second estimates) following undesirable base rate were lower than those following desirable base rates, and whether this difference was greater for self relative to other. In both studies, the overall results support the presence of a self‐specific optimism bias in belief updating. However, taking into account trait optimism (TO) as a moderator variable revealed that this was the case only in participants with high TO, whereas those with low TO showed optimistic belief updating for both self and other. In Study 2, we additionally controlled for possible misclassifications of base rate desirability. Taken together, the optimism bias in belief updating was demonstrated by a selective neglect of unfavorable information. A self‐specific influence of this bias in individuals with high TO may ultimately cause the impression of a more positive future outlook relative to others. Copyright © 2014 John Wiley & Sons, Ltd.

Optimistic update bias holds firm: Three tests of robustness following Shah et al.

“…Specifically, in this report we detail that: (i) previous studies have shown that after taking into account subjects’ base rates, exactly as Shah et al lobby for, an optimism update bias is still observed (Garrett and Sharot, 2014, Kuzmanovic et al, 2015); (ii) comparing human data to Bayesian agents shows that subjects are less Bayesian when updating their beliefs in response to bad news than good news, thus the optimistic update bias cannot be explained away as Bayesian. Shah et al’s simulations of Bayesian agents rest on specific parameters that are incompatible with human data; (iii) Shah et al’s finding of a pessimistic update bias for positive events is due to a confound in the set of stimuli they used.…”

“…Numerous studies spanning behavioural economics (Eil and Rao, 2011, Krieger et al, 2016, Krieger et al, 2014, Möbius et al, 2012), psychology (Garrett and Sharot, 2014, Kuzmanovic et al, 2015, Moutsiana et al, 2013) and neuroscience (Garrett et al, 2014, Korn et al, 2012, Kuzmanovic et al, 2016, Lefebvre et al, 2016, Ma et al, 2016, Moutsiana et al, 2015, Sharot, Guitart-Masip et al, 2012, Sharot et al, 2011, Sharot, Kanai et al, 2012) have shown that people alter their beliefs to a greater extent in response to good news than bad news. This asymmetry can lead to a positive bias in beliefs regarding oneself, referred to as the superiority illusion (Hoorens, 1993, Kruger and Dunning, 1999, Svenson, 1981), and in beliefs regarding one’s future, referred to as unrealistic optimism (Calderon, 1993, Radcliffe and Klein, 2002, Shepperd et al, 2005, Weinstein, 1980, for review see Sharot & Garrett, 2016).…”

Updating Beliefs under Perceived Threat

“…In fact, asymmetry in information integration is observed even when the second estimate is elicited immediately after information is on screen (Kuzmanovic et al, 2015, 2016; Kuzmanovic and Rigoux, 2017). Here, we submitted memory scores to a group (threat manipulation/control) by valence (good news/bad news) ANOVA (see Materials and Methods for details).…”

What is unrealistic optimism?

“…Thus, the asymmetric reliance on new information dependent on its valence results in larger updates after good news than after bad news, therefore representing a biased learning and supporting the most favorable future outlooks. In a modification of this paradigm, Kuzmanovic et al, 2015, Kuzmanovic et al, 2016 have also shown that individuals show this asymmetric update pattern to a larger extent for themselves than for similar others. Moreover, given that the extent of the asymmetric updating (i.e., the difference between mean updates after good and bad news) can be computed for each single individual, this approach allows to assess optimism bias at the individual level.…”