Abstract
Multistable perceptual phenomena provide insights into the mind’s dynamic states within a stable external environment and the neural underpinnings of these consciousness changes are often studied with binocular rivalry. Conventional methods to study binocular rivalry suffer from biases and assumptions that limit their ability to describe the continuous nature of this perceptual transitions and to discover what kind of percept was perceived across time. In this study, we propose a novel way to avoid those shortcomings by combining a continuous psychophysical method that estimates introspection during binocular rivalry with machine learning clustering and transition probability analysis. This combination of techniques reveals individual variability and complexity of perceptual experience in 28 normally sighted participants. Also, the analysis of transition probabilities between perceptual categories, i.e., exclusive and different kinds of mixed percepts, suggest that interocular perceptual competition, triggered by low-level stimuli, involves conflict between monocular and binocular neural processing sites rather than mutual inhibition of monocular sites. Layman abstract When our brain receives ambiguous information about the world, it changes its interpretation between different alternatives and thereby provides insight into how the mind works. Scientists often use a technique called binocular rivalry, where each eye sees a different image, to provoke an ambiguous visual world that is perceived as ongoing competition among interpretations of the two eyes inputs. Traditional methods for studying binocular rivalry struggle to describe the continuous nature of this fluctuation and to estimate the range of different perceived experiences. We have created a new approach in which participants reproduce their ongoing perceptual experiences combined machine learning analyses of these states. We found that individuals visual experience is more varied and complex than previously thought. Our results suggest that when our eyes see conflicting images, the brain’s effort to make sense of what is seen involves syntheses among both monocular and binocular brain areas, not just competition between monocular areas.
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Abstract
Multistable perceptual phenomena provide insights into the mind’s dynamic states within a stable external environment and the neural underpinnings of these consciousness changes are often studied with binocular rivalry. Conventional methods to study binocular rivalry suffer from biases and assumptions that limit their ability to describe the continuous nature of this perceptual transitions and to discover what kind of percept was perceived across time. In this study, we propose a novel way to avoid those shortcomings by combining a continuous psychophysical method that estimates introspection during binocular rivalry with machine learning clustering and transition probability analysis. This combination of techniques reveals individual variability and complexity of perceptual experience in 28 normally sighted participants. Also, the analysis of transition probabilities between perceptual categories, i.e., exclusive and different kinds of mixed percepts, suggest that interocular perceptual competition, triggered by low-level stimuli, involves conflict between monocular and binocular neural processing sites rather than mutual inhibition of monocular sites.
Layman abstract When our brain receives ambiguous information about the world, it changes its interpretation between different alternatives and thereby provides insight into how the mind works. Scientists often use a technique called binocular rivalry, where each eye sees a different image, to provoke an ambiguous visual world that is perceived as ongoing competition among interpretations of the two eyes inputs. Traditional methods for studying binocular rivalry struggle to describe the continuous nature of this fluctuation and to estimate the range of different perceived experiences. We have created a new approach in which participants reproduce their ongoing perceptual experiences combined machine learning analyses of these states. We found that individuals visual experience is more varied and complex than previously thought. Our results suggest that when our eyes see conflicting images, the brain’s effort to make sense of what is seen involves syntheses among both monocular and binocular brain areas, not just competition between monocular areas.
Competing Interest Statement
Both authors are founders and shareholders of the company PerZeption Inc. (USA) which has licensed the patent for InFoRM.
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