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<p>We present a dataset capturing multiple manifestations of self-bias, the systematic prioritization of self-related information, across cognitive, social, and economic decision-making domains. While individual self-bias effects have been extensively documented, their underlying relationships remain poorly characterized, limiting the development of integrative theoretical frameworks. This dataset addresses this limitation by providing comprehensive trial-by-trial data from 134 participants who completed 10 classic self-bias paradigms: self-reference effect, mere ownership effect, self-face visual search, self-name visual search, cocktail party effect, self-name attentional blink, shape-label matching, self-enhancement, implicit association test of self-esteem, and endowment effect. We also collected key individual difference variables, including personality traits, self-esteem, and cultural-related self-construals. The dataset enables researchers to elucidate underlying mechanisms of self-biases, apply computational models to elucidate underlying mechanisms, and investigate how individual differences may modulate self-bias across domains. This resource provides an empirical foundation for determining whether self-biases reflect a unitary construct, like a g-factor of self-processing, or domain-specific phenomena, advancing our understanding of how self-relevance shapes human cognition and behavior.</p>

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A Comprehensive Dataset for Investigating the Structure of Self-Bias

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月6日

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  • Yuxuan Qi,
  • Fengjie Zou,
  • Xi Ying Chau,
  • Michelle Zhou,
  • Fei Wang,
  • Jie Sui

摘要

We present a dataset capturing multiple manifestations of self-bias, the systematic prioritization of self-related information, across cognitive, social, and economic decision-making domains. While individual self-bias effects have been extensively documented, their underlying relationships remain poorly characterized, limiting the development of integrative theoretical frameworks. This dataset addresses this limitation by providing comprehensive trial-by-trial data from 134 participants who completed 10 classic self-bias paradigms: self-reference effect, mere ownership effect, self-face visual search, self-name visual search, cocktail party effect, self-name attentional blink, shape-label matching, self-enhancement, implicit association test of self-esteem, and endowment effect. We also collected key individual difference variables, including personality traits, self-esteem, and cultural-related self-construals. The dataset enables researchers to elucidate underlying mechanisms of self-biases, apply computational models to elucidate underlying mechanisms, and investigate how individual differences may modulate self-bias across domains. This resource provides an empirical foundation for determining whether self-biases reflect a unitary construct, like a g-factor of self-processing, or domain-specific phenomena, advancing our understanding of how self-relevance shapes human cognition and behavior.