Self-esteem depends on beliefs about the rate of change of social approval

A.A.Y. Low; W.J.T. Hopper; I. Angelescu; L. Mason; G.-J. Will; M. Moutoussis

doi:10.1038/s41598-022-10260-6

Self-esteem depends on beliefs about the rate of change of social approval

A.A.Y. Low^*
, W.J.T. Hopper
, I. Angelescu
, L. Mason
, G.-J. Will
, M. Moutoussis

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

A major challenge in understanding the neurobiological basis of psychiatric disorders is rigorously quantifying subjective metrics that lie at the core of mental illness, such as low self-esteem. Self-esteem can be conceptualized as a ‘gauge of social approval’ that increases in response to approval and decreases in response to disapproval. Computational studies have shown that learning signals that represent the difference between received and expected social approval drive changes in self-esteem. However, it is unclear whether self-esteem based on social approval should be understood as a value updated through associative learning, or as a belief about approval, updated by new evidence depending on how strongly it is held. Our results show that belief-based models explain self-esteem dynamics in response to social evaluation better than associative learning models. Importantly, they suggest that in the short term, self-esteem signals the direction and rate of change of one’s beliefs about approval within a group, rather than one’s social position.

Original language	English
Article number	6643
Pages (from-to)	1-15
Journal	Scientific Reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-10260-6
Publication status	Published - 22 Apr 2022

Bibliographical note

Funding Information:
AAY Low is supported by the Agency for Science, Technology and Research, Singapore (A*STAR) National Science Scholarship (MBBS-PhD). W Hopper is supported by the Ecole Doctorale Frontières de l’Innovation en Recherche et Education—Programme Bettencourt. LM is supported by a Medical Research Council Clinician Scientist Fellowship (MR/S006613/1). G-J.W. was funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement (No 707404) and the Sara van Dam z.l. Foundation, Royal Netherlands Academy of Arts & Sciences. The Wellcome Trust Centre for Human Neuroimaging is funded by the Wellcome Trust. The Max Planck—University College London Centre for Computational Psychiatry and Ageing Research is a joint initiative of the Max Planck Society and UCL. M. Moutoussis receives support from the NIHR UCLH Biomedical Research Centre.

Funding Information:
AAY Low is supported by the Agency for Science, Technology and Research, Singapore (A*STAR) National Science Scholarship (MBBS-PhD). W Hopper is supported by the Ecole Doctorale Frontières de l’Innovation en Recherche et Education—Programme Bettencourt. LM is supported by a Medical Research Council Clinician Scientist Fellowship (MR/S006613/1). G-J.W. was funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement (No 707404) and the Sara van Dam z.l. Foundation, Royal Netherlands Academy of Arts & Sciences. The Wellcome Trust Centre for Human Neuroimaging is funded by the Wellcome Trust. The Max Planck—University College London Centre for Computational Psychiatry and Ageing Research is a joint initiative of the Max Planck Society and UCL. M. Moutoussis receives support from the NIHR UCLH Biomedical Research Centre.

Publisher Copyright:
© 2022, The Author(s).

Funding

AAY Low is supported by the Agency for Science, Technology and Research, Singapore (A*STAR) National Science Scholarship (MBBS-PhD). W Hopper is supported by the Ecole Doctorale Frontières de l’Innovation en Recherche et Education—Programme Bettencourt. LM is supported by a Medical Research Council Clinician Scientist Fellowship (MR/S006613/1). G-J.W. was funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement (No 707404) and the Sara van Dam z.l. Foundation, Royal Netherlands Academy of Arts & Sciences. The Wellcome Trust Centre for Human Neuroimaging is funded by the Wellcome Trust. The Max Planck—University College London Centre for Computational Psychiatry and Ageing Research is a joint initiative of the Max Planck Society and UCL. M. Moutoussis receives support from the NIHR UCLH Biomedical Research Centre. AAY Low is supported by the Agency for Science, Technology and Research, Singapore (A*STAR) National Science Scholarship (MBBS-PhD). W Hopper is supported by the Ecole Doctorale Frontières de l’Innovation en Recherche et Education—Programme Bettencourt. LM is supported by a Medical Research Council Clinician Scientist Fellowship (MR/S006613/1). G-J.W. was funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement (No 707404) and the Sara van Dam z.l. Foundation, Royal Netherlands Academy of Arts & Sciences. The Wellcome Trust Centre for Human Neuroimaging is funded by the Wellcome Trust. The Max Planck—University College London Centre for Computational Psychiatry and Ageing Research is a joint initiative of the Max Planck Society and UCL. M. Moutoussis receives support from the NIHR UCLH Biomedical Research Centre.

Keywords

Humans
Learning
Self Concept
Social Behavior

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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s41598-022-10260-6Final published version, 2.19 MBLicence: CC BY

Cite this

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title = "Self-esteem depends on beliefs about the rate of change of social approval",

abstract = "A major challenge in understanding the neurobiological basis of psychiatric disorders is rigorously quantifying subjective metrics that lie at the core of mental illness, such as low self-esteem. Self-esteem can be conceptualized as a {\textquoteleft}gauge of social approval{\textquoteright} that increases in response to approval and decreases in response to disapproval. Computational studies have shown that learning signals that represent the difference between received and expected social approval drive changes in self-esteem. However, it is unclear whether self-esteem based on social approval should be understood as a value updated through associative learning, or as a belief about approval, updated by new evidence depending on how strongly it is held. Our results show that belief-based models explain self-esteem dynamics in response to social evaluation better than associative learning models. Importantly, they suggest that in the short term, self-esteem signals the direction and rate of change of one{\textquoteright}s beliefs about approval within a group, rather than one{\textquoteright}s social position.",

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note = "Funding Information: AAY Low is supported by the Agency for Science, Technology and Research, Singapore (A*STAR) National Science Scholarship (MBBS-PhD). W Hopper is supported by the Ecole Doctorale Fronti{\`e}res de l{\textquoteright}Innovation en Recherche et Education—Programme Bettencourt. LM is supported by a Medical Research Council Clinician Scientist Fellowship (MR/S006613/1). G-J.W. was funded by the European Union's Horizon 2020 research and innovation program under the Marie Sk{\l}odowska-Curie grant agreement (No 707404) and the Sara van Dam z.l. Foundation, Royal Netherlands Academy of Arts \& Sciences. The Wellcome Trust Centre for Human Neuroimaging is funded by the Wellcome Trust. The Max Planck—University College London Centre for Computational Psychiatry and Ageing Research is a joint initiative of the Max Planck Society and UCL. M. Moutoussis receives support from the NIHR UCLH Biomedical Research Centre. Funding Information: AAY Low is supported by the Agency for Science, Technology and Research, Singapore (A*STAR) National Science Scholarship (MBBS-PhD). W Hopper is supported by the Ecole Doctorale Fronti{\`e}res de l{\textquoteright}Innovation en Recherche et Education—Programme Bettencourt. LM is supported by a Medical Research Council Clinician Scientist Fellowship (MR/S006613/1). G-J.W. was funded by the European Union's Horizon 2020 research and innovation program under the Marie Sk{\l}odowska-Curie grant agreement (No 707404) and the Sara van Dam z.l. Foundation, Royal Netherlands Academy of Arts \& Sciences. The Wellcome Trust Centre for Human Neuroimaging is funded by the Wellcome Trust. The Max Planck—University College London Centre for Computational Psychiatry and Ageing Research is a joint initiative of the Max Planck Society and UCL. M. Moutoussis receives support from the NIHR UCLH Biomedical Research Centre. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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N2 - A major challenge in understanding the neurobiological basis of psychiatric disorders is rigorously quantifying subjective metrics that lie at the core of mental illness, such as low self-esteem. Self-esteem can be conceptualized as a ‘gauge of social approval’ that increases in response to approval and decreases in response to disapproval. Computational studies have shown that learning signals that represent the difference between received and expected social approval drive changes in self-esteem. However, it is unclear whether self-esteem based on social approval should be understood as a value updated through associative learning, or as a belief about approval, updated by new evidence depending on how strongly it is held. Our results show that belief-based models explain self-esteem dynamics in response to social evaluation better than associative learning models. Importantly, they suggest that in the short term, self-esteem signals the direction and rate of change of one’s beliefs about approval within a group, rather than one’s social position.

AB - A major challenge in understanding the neurobiological basis of psychiatric disorders is rigorously quantifying subjective metrics that lie at the core of mental illness, such as low self-esteem. Self-esteem can be conceptualized as a ‘gauge of social approval’ that increases in response to approval and decreases in response to disapproval. Computational studies have shown that learning signals that represent the difference between received and expected social approval drive changes in self-esteem. However, it is unclear whether self-esteem based on social approval should be understood as a value updated through associative learning, or as a belief about approval, updated by new evidence depending on how strongly it is held. Our results show that belief-based models explain self-esteem dynamics in response to social evaluation better than associative learning models. Importantly, they suggest that in the short term, self-esteem signals the direction and rate of change of one’s beliefs about approval within a group, rather than one’s social position.

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Self-esteem depends on beliefs about the rate of change of social approval

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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