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Bits of confidence: Metacognition as uncertainty reduction
1Department of Psychology, Ariel University, Ariel, Israel. danielfi@ariel.ac.il.
Psychonomic Bulletin & Review
|September 2, 2025
Summary
This study reframes metacognition as information exchange, introducing new metrics (meta-U, meta-KL, meta-J) to measure confidence judgment efficiency. These novel measures offer advantages over existing methods for understanding self-assessment accuracy.
Area of Science:
- Cognitive Science
- Neuroscience
- Information Theory
Background:
- Confidence judgments are crucial for metacognition, impacting learning, memory, and decision-making.
- Current methods for assessing metacognitive accuracy have limitations.
- Metacognition involves self-evaluation of one's own decisions.
Purpose of the Study:
- To reframe metacognition as a structured information exchange between stimulus, actor, and rater.
- To develop novel information-theoretic measures of metacognitive efficiency.
- To offer a unified framework for studying confidence and metacognitive inefficiency.
Main Methods:
- Conceptualizing metacognition as a multi-agent communication system.
- Applying information theory principles (entropy and divergence).
- Developing three novel metacognitive efficiency measures: meta-U, meta-KL, and meta-J.
Main Results:
- Novel metrics quantify information transmission in confidence judgments.
- Simulations demonstrate advantages over traditional metrics (meta-d', M-ratio).
- New measures show improved interpretability, robustness, and sensitivity.
Conclusions:
- Formalizing metacognitive sensitivity as an information-processing problem provides a unified approach.
- The proposed framework enhances understanding of confidence judgments.
- This research illuminates sources of metacognitive inefficiency.
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