An Extension of Combinatorial Contextuality for Cognitive Protocols.

Area of Science:

• Quantum Cognition
• Cognitive Science
• Causal Inference

Background:

• Contextuality is crucial in understanding mental phenomena like human memory.
• Distinguishing between noise and causal influences is a key challenge in cognitive studies.
• Existing methods struggle to adequately determine contextuality when causal influences are present.

Purpose of the Study:

• To extend the combinatorial approach for determining contextuality amidst causal influences.
• To formalize the combinatorial approach within canonical causal models.
• To develop techniques for measuring and treating disturbances, separating noise from causal influences.

Main Methods:

• Formalization of the combinatorial approach using canonical causal models.
• Development of techniques to measure and differentiate between noise and causal influences.
• Creation of a protocol for representing these elements in cognitive experiments.

Main Results:

• Successfully extended the combinatorial approach to handle causal influences.
• Provided methods to distinguish between noise and actual causal influences.
• Established a framework for empirical testing in cognitive experiments.

Conclusions:

• The extended combinatorial approach offers a practical solution for determining contextuality in cognitive phenomena.
• This method enables cognitive modelers to accurately assess contextuality despite inherent causal influences.
• Facilitates a more rigorous understanding of mental phenomena, such as concepts in human memory.