Between-Subject Variability in the Breaking Continuous Flash Suppression Paradigm: Potential Causes, Consequences, and Solutions

Abstract

A recent focus in the field of consciousness research involves investigating the propensity of initially non-conscious visual information to gain access to consciousness. A critical tool for measuring conscious access is the so-called breaking continuous flash suppression paradigm (b-CFS). In this paradigm, a high contrast dynamic pattern is presented to one eye, thereby temporarily suppressing a target stimulus that is presented to the other eye. The time it takes for observers to report (e.g., the location of) the initially suppressed stimulus provides a measure of conscious access. Typical observations in b-CFS studies include the finding that upright faces are released from suppression faster than inverted faces, and the finding that stimuli that match the current content of visual working memory are released from suppression faster than mismatching stimuli. Interestingly, the extent to which observers exhibit these effects varies extensively (in the range of hundreds of milliseconds). By re-analyzing existing datasets and a new dataset we establish that the difference in RTs between conditions in b-CFS tasks (i.e., the effect of interest) is highly correlated with participants' overall suppression durations, and with their trial-to-trial variability in RTs. We advocate the usage of a simple latency- normalization method, which (1) removes the between-subject variability in suppression duration from the effect of interest, while (2) providing distributions of RT differences that are better suited for parametric testing. We next compare this latency-normalization method to two other transformations that are widely applied on within-subject RT data (z-transformations and log-transformations). Finally, we tentatively discuss how trial-to-trial variability and overall suppression duration might relate to prolonged phases of shallow suppression that are more prone to modulations of conscious access.

Typical visual-field locations facilitate access to awareness for everyday objects

“…Interestingly, these findings suggest that the presence of regularities may not only facilitate conscious and explicit interactions with the world (e.g., Wolfe, Võ, Evans, & Greene, 2011), but may also determine whether we perceive an object in the first place. However, whether differences in breaking-CFS reflect differences in unconscious processing or more general differences in stimulus detectability is a matter of ongoing debate (Blake, Brascamp, & Heeger, 2014;Gayet & Stein, 2017;Gayet, Van der Stigchel, & Paffen, 2014;Yang, Brascamp, Kang, & Blake, 2014). Under a more cautious interpretation, our findings therefore reveal that typical positioning influences stimulus detectability, potentially reflecting differences in unconscious processing.…”

Unconscious processing of facial dominance: The role of low-level factors in access to awareness.

“…changing the size, contrast, or luminance). To address the concern that effects might have increased with longer overall suppression times (e.g., Gayet & Stein, 2017), we also calculated latency--normalized dominance effects for all b--CFS experiments (Table S4 in the Supplementary Material). Latency-normalization reduced both between--subject variability and between--condition variability in overall RTs.…”

“…In addition to analyzing log--transformed mean suppression times we also analyzed the effect of dominance on b--CFS using the latency normalization procedure proposed by Gayet and Stein (2017). For Experiment 1 we calculated, separately for upright and inverted faces, the latency--normalized "dominance effect I" as the difference between mean RTs for most--dominant faces and mean RTs for neutral-dominant faces divided by the overall mean RT for the two conditions.…”

Continuous flash suppression operates in local spatial zones: Effects of mask size and contrast

“…In 1). This normalisation procedure was performed to reduce inter-subject variability and Type II error (but see normalisation methods discussed in Gayet & Stein, 2017). Means for experiment conditions were calculated from these normalised suppression durations across all participants.…”