A dynamic stimulus distribution methodology for efficient large-scale pairwise relational data collection.

Area of Science:

• Cognitive Science
• Psychology
• Computational Neuroscience

Background:

• Subjective comparisons yield valuable data for understanding consciousness.
• Traditional pairwise comparisons require N² trials, posing challenges for large-scale data collection.
• Online platforms facilitate participant recruitment but often suffer from inefficient data distribution.

Purpose of the Study:

• To develop a method for minimizing variance in stimulus pair presentation frequencies.
• To enhance the efficiency and uniformity of data collection in online consciousness studies.
• To provide a practical solution for managing pairwise judgments in large experimental datasets.

Main Methods:

• Integration of PsychoPy with GitHub Gist to record stimulus pair presentation frequencies.
• Development of JavaScript code for dynamic trial selection in online experiments.
• Implementation of a system that reads, selects, and updates stimulus pair frequencies for each participant.

Main Results:

• Demonstrated effectiveness of the proposed method in balancing trial counts across stimulus pairs.
• Enabled dynamic balancing of frequencies for more efficient data collection.
• Successfully stored and managed presentation frequencies using GitHub Gist.

Conclusions:

• The novel method significantly improves data collection efficiency for subjective comparisons in online studies.
• This approach offers a scalable solution for characterizing consciousness structures.
• The integration of PsychoPy and GitHub Gist provides a robust framework for future research in experimental psychology.