Predictions from masked motion with and without obstacles

Ariel Goldstein*, Ido Rivlin, Alon Goldstein, Yoni Pertzov, Ran R. Hassin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Predicting the future is essential for organisms like Homo sapiens, who live in a dynamic and ever-changing world. Previous research has established that conscious stimuli can lead to non-conscious predictions. Here we examine whether masked stimuli can also induce such predictions. We use masked movement-with and without obstacles-to examine predictions from masked stimuli. In six experiments a moving object was masked using continuous flash suppression (CFS). A few hundred milliseconds after the object had disappeared, a conscious probe appeared in a location that was either consistent with the masked stimulus or not. In Experiments 1-3 the movement was linear, and reaction times (RTs) indicated predictions that were based on direction and speed of movement. In Experiment 4, the masked moving object collided with an obstacle and then disappeared. Predictions in this case should reflect deflection, and indeed reaction times revealed predictions on the deflection route. In Experiments 5 and 6 we introduce an innovative way of using eye-tracking during continuous flash suppression (CFS) and report physiological evidence-in the forms of eye-movements-for masked stimuli induced predictions. We thus conclude that humans can use dynamic masked stimuli to generate active predictions about the future, and use these predictions to guide behavior. We also discuss the possible interpretations of these findings in light of the current scientific discussion regarding the relation between masked presentation, subliminal perception and awareness measurement methods.

Original languageAmerican English
Article numbere0239839
JournalPLoS ONE
Volume15
Issue number11 November
DOIs
StatePublished - Nov 2020

Bibliographical note

Publisher Copyright:
© 2020 Goldstein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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