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广东快乐彩开奖结果:Believability of evidence matters for correcting social impressions
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The digital age affords exposure to a staggering amount of information—not all of it true. The extent to which mere exposure to information of uncertain veracity or outright disinformation campaigns shapes our impressions of others, independent of our subjective assessments of its truth value, is thus a key question with important implications, especially because implicit evaluations have been shown to uniquely predict behaviors such as which politician a voter ultimately votes for in an election. This study sheds light on the nature of implicit cognition and the extent to which dissociations between implicit and explicit evaluations can be successfully explained by differential reliance on propositional learning processes.
To what extent are we beholden to the information we encounter about others? Are there aspects of cognition that are unduly influenced by gossip or outright disinformation, even when we deem it unlikely to be true? Research has shown that implicit impressions of others are often insensitive to the truth value of the evidence. We examined whether the believability of new, contradictory information about others influenced whether people corrected their implicit and explicit impressions. Contrary to previous work, we found that across seven studies, the perceived believability of new evidence predicted whether people corrected their implicit impressions. Subjective assessments of truth value also uniquely predicted correction beyond other properties of information such as diagnosticity/extremity. This evidence shows that the degree to which someone thinks new information is true influences whether it impacts implicit impressions.
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Author contributions: J.C., K.F., and M.J.F. designed research; J.C. and K.F. performed research; J.C. analyzed data; and J.C., K.F., and M.J.F. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: Unless otherwise indicated, procedures, sample size, exclusion criteria, hypotheses, and data analysis plans were preregistered before data collection on the Open Science Framework (https://osf.io/3h2ww/).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1903222116/-/DCSupplemental.
Published under the PNAS license.