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. 2013 Oct 8;110(41):16396-401.
doi: 10.1073/pnas.1305631110. Epub 2013 Sep 9.

Human Development of the Ability to Learn From Bad News

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Free PMC article

Human Development of the Ability to Learn From Bad News

Christina Moutsiana et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

Humans show a natural tendency to discount bad news while incorporating good news into beliefs (the "good news-bad news effect"), an effect that may help explain seemingly irrational risk taking. Understanding how this bias develops with age is important because adolescents are prone to engage in risky behavior; thus, educating them about danger is crucial. We reveal a striking valence-dependent asymmetry in how belief updating develops with age. In the ages tested (9-26 y), younger age was associated with inaccurate updating of beliefs in response to undesirable information regarding vulnerability. In contrast, the ability to update beliefs accurately in response to desirable information remained relatively stable with age. This asymmetry was mediated by adequate computational use of positive but not negative estimation errors to alter beliefs. The results are important for understanding how belief formation develops and might help explain why adolescents do not respond adequately to warnings.

Keywords: decision making; learning; optimism.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Paradigm. (A) In each trial, participants were presented with a short description of 1 of 40 adverse events and asked to estimate how likely this event was to occur to them. They were then presented with the average probability of that event occurring. The second session was the same as the first except that the average probability of the event to occur was not presented. Examples of trials for which the participant’s estimate was (B) higher or (C) lower than the average probability. Here, for illustration purposes only, the blue and red frames denote the participant’s response (either an overestimation or underestimation, respectively), and the blue and red filled boxes denote information that calls for an adjustment in (B) a desirable or (C) an undesirable direction.
Fig. 2.
Fig. 2.
Relationship between age and learning from good and bad news. (A) Correlation across subjects between age and learning from good and bad news (i.e., trials for which the information presented was better or worse than expected). (B and C) Learning is defined as the correlation between estimation errors and update across trials for each subject. Data from two subjects demonstrate this association for trials in which the subject received good news (thus, estimation errors are positive) and trials in which the subject received bad news (thus, estimation errors are negative). The slope of each line is the learning score of that subject. In this example, learning from bad news is worse than learning from good news in the younger participant but does not differ as much for the older participant.
Fig. 3.
Fig. 3.
Controlling for other variables. (A) Each bar represents the magnitude of the Pearson correlation coefficient (r) between that variable and age. Bars are plotted separately for good news trials and bad news trials. For learning, bars represent partial correlation coefficients that control for the magnitude and standard division across trials of the two factors that compose the learning score (i.e., corrected learning score); these differed significantly between good and bad news trials. Lower asterisks represent factors that showed a significant correlation with age for either good or bad news. (B) These factors were entered in a hierarchical regression model explaining learning from bad news. The four blocks correspond to the order of the variables that were entered. First, we controlled for the magnitude and SD of the two factors that compose the learning score (block 1); then, we controlled for any additional variable that correlated with age (block 2); next, we controlled for learning from good news (block 3); and finally, we introduced age (block 4). Betas are plotted after the final block is entered. Age significantly accounted for additional variance in learning from bad news over and above all other predictors. *P < 0.05; n.s., no significant difference between bars.

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