The study of consciousness faces unique challenges because it investigates subjective experience, which is accessible only from a first-person perspective, unlike objective third-person phenomena in other sciences. This article reviews historical and contemporary efforts to measure consciousness and its absence, focusing on two main approaches: objective performance-based measures and subjective report-based measures of awareness. It compares their advantages and disadvantages, evaluates them against methodological criteria, and discusses transforming both into a common sensitivity measure (d') for comparison. New approaches are explored, including Bayesian models to support claims of absent awareness and machine-learning decoding models, alongside future challenges such as measuring qualia—the qualitative contents of awareness.
Working memory may operate on unconscious perceptual contents, though it remains linked to conscious perception. A large, multisite replication (19 labs, 531 participants, 720 trials) of Soto et al. (2011) found above-chance accuracy (.55) on a visual discrimination task when participants reported not seeing the subliminal Gabor grating. Performance correlated positively with cue detection sensitivity (r = .228), and the regression intercept was significantly above chance (β₀ = .521). The study provides an open-access dataset and confirms that measures were reliable and valid, supporting the existence of unconscious working memory.
Research on unconscious perception has faced persistent methodological challenges since the late 19th century. This review discusses key difficulties in demonstrating perception without awareness, examining how objective versus subjective measures of awareness produce different awareness thresholds and lead to alternative experimental approaches. New methodologies include regression-based Bayesian modeling, sensitivity vs. awareness curves from General Recognition Theory, the liminal-prime paradigm, and two-interval forced choice designs. The authors emphasize the need for brain-based approaches and highlight promising studies in this area, along with the role of individual differences and frameworks such as predictive coding and active inference. These advances aim to address challenges in demonstrating cognition without awareness.