Overcorrection for Social Categorization Information Moderates Impact Bias in Affective Forecasting

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Description: Plural societies require individuals to forecast how others—both in-group and out-group members—will respond to gains and setbacks. Typically, greater information results in correction from inaccurate initial forecasts. By contrast to the typical palliative effects of more information, we find that that correcting for targets’ social categories result in more extreme, less accurate forecasts. Forecasters in three experiments exhibited more impact bias in their affective forecasts for in-group and out-group members’ responses to positive and negative outcomes when provided with social categorization information about their target (e.g., a “Democrat” or “Republican”) than when provided with no category information (e.g., a “person”). Inducing time pressure reduced the extremity of forecasts for group-labeled targets but did not affect forecasts for unidentified targets, suggesting that the increased impact bias for identified group members was due to differences in correction rather than in intuitive predictions.

License: CC-By Attribution 4.0 International

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Complete materials used, as well as data and R code, can be found under the corresponding directory for each experiment.

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