Abstract
The purpose of this study was to investigate whether artificial neural networks (ANN) are able to decode participants’ conscious experience perception from brain activity alone, using complex and ecological stimuli. To reach the aim we conducted pattern recognition data analysis on fMRI data acquired during the execution of a binocular visual rivalry paradigm (BR). Twelve healthy participants were submitted to fMRI during the execution of a binocular non-rivalry (BNR) and a BR paradigm in which two classes of stimuli (faces and houses) were presented. During the binocular rivalry paradigm, behavioral responses related to the switching between consciously perceived stimuli were also collected. First, we used the BNR paradigm as a functional localizer to identify the brain areas involved the processing of the stimuli. Second, we trained the ANN on the BNR fMRI data restricted to these regions of interest. Third, we applied the trained ANN to the BR data as a ‘brain reading’ tool to discriminate the pattern of neural activity between the two stimuli. Fourth, we verified the consistency of the ANN outputs with the collected behavioral indicators of which stimulus was consciously perceived by the participants. Our main results showed that the trained ANN was able to generalize across the two different tasks (i.e. BNR and BR) and to identify with high accuracy the cognitive state of the participants (i.e. which stimulus was consciously perceived) during the BR condition. The behavioral response, employed as control parameter, was compared with the network output and a statistically significant percentage of correspondences (p-value <0.05) were obtained for all subjects. In conclusion the present study provides a method based on multivariate pattern analysis to investigate the neural basis of visual consciousness during the BR phenomenon when behavioral indicators lack or are inconsistent, like in disorders of consciousness or sedated patients.
Highlights
Multivariate pattern analysis (MVPA) is able to process information coming from differently located clusters of voxels and makes it possible to detect particular patterns of neural activity that may remain hidden to conventional analyses [1]
A dynamic perceptual phenomenon suitable to be studied with MVPA is the binocular visual rivalry (BR): two different visual stimuli are presented, one to each eye, and the two conflicting monocular images compete for access to consciousness and the subject usually experiences an alternate perception of the two images
The aim of our study was to provide a method based on artificial neural networks (ANN) [17] able to identify the different neural pattern of activity related to the processing of two classes of visual stimuli during a visual rivalry paradigm, applicable in the absence of behavioral indicators, indicating which stimulus is perceived by participant
Summary
Multivariate pattern analysis (MVPA) is able to process information coming from differently located clusters of voxels and makes it possible to detect particular patterns of neural activity that may remain hidden to conventional analyses (e.g., univariate statistical methods) [1]. In these last years, MVPA has been extensively applied as a ‘‘mind reading’’ tool to decode mental states from functional magnetic resonance imaging (fMRI) data, such as to assess perceptual states [2] or to evaluate deception and differentiate lying from truth-telling [3], [4], [5]. The BR paradigm was shown to be an important tool to explore the neural correlates of visual conscious experience [11], [12]
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