Do Process-1 simulations generate the epistemic feelings that drive Process-2 decision making?

Area of Science:

• Cognitive Science
• Neuroscience
• Mathematical Psychology

Background:

• Dual process models describe fast (Process-1) and slow (Process-2) thinking.
• Epistemic feelings, like confidence, influence decision-making and problem-solving.
• Bayesian brain hypotheses propose probabilistic inference underlies cognition.

Purpose of the Study:

• To formally represent dual process models of problem-solving using Cone-Cocone Diagrams (CCCDs).
• To investigate the role of confidence in epistemic feelings within these models.
• To model the generation of subjective probabilities from experienced confidence.

Main Methods:

• Application of Chu space and Channel Theory for CCCD construction.
• Formal representation of Process-1 problem-solving via CCCD completion.
• Modeling Process-2 as iterated Process-1 problem-solving.
• Simulation-based reverse engineering for subjective probability generation.

Main Results:

• A formal CCCD-based representation of Process-1 problem-solving was developed.
• Process-2 problem-solving was shown to be equivalent to iterated Process-1 problem-solving under CCCD conditions.
• The generation of subjective probabilities from confidence was modeled as a CCCD construction.

Conclusions:

• CCCDs provide a unified framework for understanding dual process problem-solving.
• Confidence plays a key role in both intuitive and deliberative reasoning processes.
• The formalization offers new insights into the relationship between implicit confidence and explicit probability judgments.