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Fabian Soto, Ali Pournaghdali; Sensitivity vs. awareness curve: a novel model-based analysis to uncover the processes underlying nonconscious perception. Journal of Vision 2020;20(11):959. https://doi.org/10.1167/jov.20.11.959.
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We present a novel model-based analysis of the association between awareness and perceptual processing based on a multidimensional version of signal detection theory (general recognition theory, or GRT). The analysis fits a GRT model to behavioral data and uses the estimated model to construct a sensitivity vs. awareness (SvA) curve, representing sensitivity in the discrimination task at each value of relative likelihood of awareness. This approach treats awareness as a continuum rather than a dichotomy, but also provides an objective benchmark for low likelihood of awareness, at the point in which an ideal observer would categorize a perceptual effect as resulting from no stimulus. Confidence intervals are built for the SvA curve using parametric bootstrapping, so that conclusions about the relation of perceptual processing and awareness can be reached by simple visual inspection. In two experiments, we assessed nonconscious facial expression recognition using SvA curves in a condition in which emotional faces (fearful vs. neutral) were rendered invisible using continuous flash suppression (CFS) for 500 (Experiment 1) and 700 (Experiment 2) milliseconds. Participants had to provide subjective awareness reports, expression discrimination responses, and meta-cognitive judgements of confidence on those discrimination responses. We predicted and found sub-conscious processing of face emotion, in the form of higher than chance-level sensitivity in the area of low likelihood of awareness. We also found evidence for meta-cognitive sensitivity in the absence of awareness. The similarity between the pattern of results from perceptual discrimination and metacognitive judgements is in line with the detection-theoretic assumption that both processes are based on the same perceptual evidence variable. More generally, the SvA curve analysis can be applied to a variety of designs to answer questions about the dependence of perceptual processing on awareness, and is easily available as part of an R package.
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