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Area of Science:

  • Affective science
  • Decision science
  • Psychological science

Background:

  • Self-reports are the primary method for measuring subjective affective experiences in affective science.
  • The psychological processes transforming subjective experience into self-reports are not well understood.

Purpose of the Study:

  • To propose a novel framework for understanding self-reports in affective science.
  • To leverage computational tools from decision-making research, specifically sequential sampling models.
  • To disentangle affective experience from decision processes in self-reporting.

Main Methods:

  • Framing self-reports as dynamic affective decisions.
  • Applying sequential sampling models to analyze the transformation of subjective experience into self-reports.
  • Outlining how this framework can probe moderators of affective experience.

Main Results:

  • The proposed decision-making framework offers a method to distinguish affective experience from self-report noise.
  • This approach can illuminate mechanisms underlying moderators like context, individual differences, and emotion regulation.
  • Adopting this framework can yield broader insights into affective processes.

Conclusions:

  • Viewing affective self-reports as decisions enhances measurement accuracy in affective science.
  • This integrative approach fosters collaboration between affective and decision scientists.
  • It leads to a more comprehensive understanding of psychological science.