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<p>Self-referential processing captures how individuals perceive themselves. When negatively biased, self-referential processing is closely linked to depression. Increasingly, research has employed drift-diffusion modeling (DDM) to analyze the Self-Referential Encoding Task (SRET), but variations in DDM estimation methods and model complexity may influence findings. This meta-analysis examined (1) the overall difference between DDM parameters for negative versus positive self-trait endorsement, and (2) how DDM parameters for negative and positive self-trait endorsement relate to symptoms of depression. We then qualitatively reviewed variations in DDM methods that may impact findings from the overall-meta-analyses, including sample composition, DDM estimation method, model complexity, and criteria for handling reaction time outliers. We found a reliable positivity advantage for drift rate in self-trait endorsement at the latent, dynamic decision-making level. Moreover, we found that greater drift rate for negative and lower drift rate for positive self-trait endorsement were associated with greater symptoms of depression. Effects were present for bias and non-decision time, but were sparse and underpowered. As an emerging research area, the present study underscores the importance of carefully considering sample composition, model estimation methods, model complexity, and handling of reaction time outliers when applying drift-diffusion modeling to self-referential tasks.</p>

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A Meta-Analysis of Drift-Diffusion Modeling of Self-Referential Processing and Relations with Depressive Symptoms

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Elizabeth V. Edgar,
  • Karim Ibrahim,
  • Michael H. Bloch,
  • Michael J. Crowley

摘要

Self-referential processing captures how individuals perceive themselves. When negatively biased, self-referential processing is closely linked to depression. Increasingly, research has employed drift-diffusion modeling (DDM) to analyze the Self-Referential Encoding Task (SRET), but variations in DDM estimation methods and model complexity may influence findings. This meta-analysis examined (1) the overall difference between DDM parameters for negative versus positive self-trait endorsement, and (2) how DDM parameters for negative and positive self-trait endorsement relate to symptoms of depression. We then qualitatively reviewed variations in DDM methods that may impact findings from the overall-meta-analyses, including sample composition, DDM estimation method, model complexity, and criteria for handling reaction time outliers. We found a reliable positivity advantage for drift rate in self-trait endorsement at the latent, dynamic decision-making level. Moreover, we found that greater drift rate for negative and lower drift rate for positive self-trait endorsement were associated with greater symptoms of depression. Effects were present for bias and non-decision time, but were sparse and underpowered. As an emerging research area, the present study underscores the importance of carefully considering sample composition, model estimation methods, model complexity, and handling of reaction time outliers when applying drift-diffusion modeling to self-referential tasks.