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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

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The Cognition/Metacognition Trade-Off.

David Rosenbaum¹, Moshe Glickman^2,3, Stephen M Fleming^2,3,4

¹School of Psychological Sciences, Tel Aviv University.

Psychological Science

|March 25, 2022

Summary

Decision-making algorithms that integrate evidence to a boundary offer speed but reduce confidence accuracy. Computational modeling and experiments reveal a trade-off between decision speed and metacognitive accuracy.

Keywords:

computational models decision confidence decision-making diffusion model integration to boundary judgment metacognition optimality preregistered reaction time

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Area of Science:

Cognitive Science
Computational Neuroscience
Decision Science

Background:

Integration to boundary is an optimal decision algorithm for rapid choices.
This strategy may impact metacognitive accuracy, the ability to assess one's own confidence.

Purpose of the Study:

To investigate the trade-off between decision speed and metacognitive accuracy.
To compare the effects of different evidence integration strategies on metacognitive accuracy.

Main Methods:

Computational modeling of evidence integration strategies (fixed boundary, collapsing boundary, random timer).
Three cross-domain experiments with 102 university students in free-response and interrogation sessions.
Analysis of choice and confidence reports following evidence observation.

Main Results:

Integration to a fixed boundary reduces metacognitive accuracy due to less evidence integration variability.
An interrogation protocol, preventing integration to boundary, enhanced metacognitive accuracy.
Participants in free-response sessions used a collapsing boundary strategy, balancing choice and metacognitive accuracy.

Conclusions:

The integration to boundary decision strategy creates a trade-off between speed and metacognitive accuracy.
Collapsing boundary strategies offer a compromise, optimizing both decision accuracy and confidence.
Understanding these strategies is crucial for designing effective decision-making systems.