Optimistic Update Bias in Response to False Information

Abstract We investigate the degree to which labelling information as true or false after exposure to it can influence the degree to which it alters beliefs and test whether valence (whether the information is better or worse than expected) plays a key role in modulating this change. To investigate this, we adapt a classic belief updating paradigm by having participants estimate the likelihood of different life events occurring in the future and then varying information presented to participants along two dimensions: valence (whether the information is better or worse than thought) and accuracy (whether information is labelled true or false). By examining how much participants’ beliefs change post exposure to the information, we find a significant effect along both dimensions. Specifically, participants change beliefs more following information labelled true compared to false and following information that is better compared to worse than expected. This latter effect is consistent with past studies that have revealed a valence effect in belief updating (in response to unlabelled information). The novel finding here is that this valence effect exists in response to false information, and we show evidence–using a Bayes Factor analysis–that it is just as strong in response to false as it is in response to true information. Computational modelling analysis suggests that these patterns of updating arise from the differential use of error signals during learning. Together, the results suggest that labelling information post exposure to it as false has the potential to curb the degree to which it can change beliefs. But this is likely to be less effective in cases where that information presents a rosier view of one’s future. Belief Updating, False Information, Optimistic Update Bias