2017
DOI: 10.7554/elife.28098
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Neural and computational processes underlying dynamic changes in self-esteem

Abstract: Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-es… Show more

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Cited by 117 publications
(175 citation statements)
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References 83 publications
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“…Those more fearful of negative evaluation selected significantly fewer positive attributes when asked to predict how the computer persona would describe them, but displayed no bias when making predictions about unknown others. The fact that this negative bias specifically manifested when evaluations are related to the self, suggests that individuals integrate social information differently depending on the context and focus of the evaluation, which is consistent with the cognitive models (Beck, 1971;Cooley, 1902) Computational cognitive studies have recently addressed self-evaluation (Koban et al, 2017;Will et al, 2017). So far, studies have mostly relied on associative learning models (Rescorla & Wagner, 1972) to capture phenomena such as healthy people giving more weight to positive, rather than negative, information about themselves.…”
supporting
confidence: 65%
“…Those more fearful of negative evaluation selected significantly fewer positive attributes when asked to predict how the computer persona would describe them, but displayed no bias when making predictions about unknown others. The fact that this negative bias specifically manifested when evaluations are related to the self, suggests that individuals integrate social information differently depending on the context and focus of the evaluation, which is consistent with the cognitive models (Beck, 1971;Cooley, 1902) Computational cognitive studies have recently addressed self-evaluation (Koban et al, 2017;Will et al, 2017). So far, studies have mostly relied on associative learning models (Rescorla & Wagner, 1972) to capture phenomena such as healthy people giving more weight to positive, rather than negative, information about themselves.…”
supporting
confidence: 65%
“…The deviation from the preferred allocation generates an "error" signal that could drive adaptive actions to reduce the size of the deviation. In much the same way as reward prediction errors 8 represent differences between expected and actual rewards and provide a basis for value-based decisions 9,10 , this social error signal could represent deviation from the preferred allocation and serve as a basis for value-based decisions in the social domain.…”
Section: Introductionmentioning
confidence: 99%
“…The evaluation of the self is an important way to be aware of how the self is perceived and to build self-image 17,34,39 . An individual’s self-image is formed and updated as a result of how the individual sees the self, as well as how others see the individual, which are respectively linked to the private and public self-image 40,41 .…”
Section: Discussionmentioning
confidence: 99%