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Hierarchical drift‐diffusion modelling uncovers differences of valenced self‐evaluation.

  • Published In: Asian Journal of Social Psychology, 2024, v. 27, n. 4. P. 792 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Nan; Shi, Kun; Li, Jiwen; Chen, Haopeng; Tang, Jianchao; Liu, Yadong; Zhao, Xiaolin; Yang, Juan 3 of 3

Abstract

Differences in valenced self‐evaluation refer to positive and negative coexistence in the process of self‐evaluation, while there is a clear difference in cognitive processes. The present study aimed to uncover the differences in the latent cognitive parameters (e.g., processing speed) in valenced self‐evaluation using the hierarchical drift‐diffusion model in two independent experiments. A self‐referential decision‐making task was applied in both experiments, and a self‐descriptiveness task plus the rating of related emotions (e.g., pride and shame) were also used but only in Experiment 2. Results of Experiments 1 & 2 showed a faster processing speed for accepting positive attributes and longer times for encoding and response execution in negative self‐evaluation. Moreover, Experiment 2 found cognitive parameters had predictive effects on subsequent decisional outcomes such as self‐descriptiveness and self‐related emotions via Bayesian inference. The current study provided findings that help to understand the cognitive mechanism behind self‐positivity and self‐accuracy biases. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Asian Journal of Social Psychology. 2024/12, Vol. 27, Issue 4, p792
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2024
  • ISSN:1367-2223
  • DOI:10.1111/ajsp.12638
  • Accession Number:181922006
  • Copyright Statement:Copyright of Asian Journal of Social Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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