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Increasing power for detecting awareness: a new approach to test group level objective performance
Shaked Lublinsky1, Itay Yaron1, William Marshall2
1Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Neuroscience of Consciousness
|July 29, 2026
Summary
Detecting unconscious effects is difficult. New frequentist and Bayesian awareness tests offer higher statistical power, improving the reliability of unconscious processing research and reducing bias from conscious awareness.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Psychological Research Methods
Background:
- Demonstrating unconscious effects is challenging due to difficulties in reliably assessing awareness.
- Low statistical power of existing awareness tests can lead to false positives, suggesting unconscious effects when conscious processing occurred.
Purpose of the Study:
- To introduce and evaluate novel frequentist and Bayesian awareness tests designed to enhance the detection of consciousness.
- To compare the power, sensitivity, and specificity of these new tests against commonly used methods.
Main Methods:
- Simulations were used to compare the performance of the proposed Group Binomial (frequentist and Bayesian) tests against t-tests and logistic regression.
- Reanalysis of 79 previously reported effects from 15 papers on unconscious processing was conducted.
Main Results:
- The proposed frequentist and Bayesian Group Binomial tests demonstrated a clear power advantage over existing methods across various scenarios.
- Reanalysis revealed instances where the proposed tests detected effects missed by conventional methods, highlighting their superior sensitivity.
Conclusions:
- The developed awareness tests significantly improve the ability to reliably detect consciousness, thereby strengthening the evidence base for unconscious processing.
- Adopting these more powerful statistical approaches will reduce the risk of misattributing conscious effects to unconscious processing in future research.
