Accurately Measuring Nonconscious Processing Using a Generative Bayesian Framework

Ariel Goldstein*, Asael Y. Sklar, Noam Siegelman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Despite considerable interest in subliminal effects, fundamental questions about the proper way of examining them remain unanswered, sowing doubts regarding the veracity of published results. A central question is whether observed effects result from nonconscious processing rather than from some stimuli being consciously perceived by participants which are missed due to error in the awareness measurement. Here, we suggest a solution that implements a Bayesian modeling approach to measure the behavioral effects due to nonconscious stimuli accurately. Our solution relies on a Bayesian estimate of the correlation between variables that accounts for measurement error by modeling their uncertainty. We use simulations to show that this method accurately estimates nonconscious effects. The method we suggest is easy to use, and we describe its implementation on a relevant data set.

Original languageAmerican English
Pages (from-to)336-355
Number of pages20
JournalPsychology of Consciousness: Theory Research, and Practice
Volume9
Issue number4
DOIs
StatePublished - 24 Feb 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 American Psychological Association

Keywords

  • Awareness test
  • Hierarchical bayesian framework
  • Subliminal perception

Fingerprint

Dive into the research topics of 'Accurately Measuring Nonconscious Processing Using a Generative Bayesian Framework'. Together they form a unique fingerprint.

Cite this