Selective maintenance mechanisms of seen and unseen sensory features in the human brain

Recent studies of “unconscious working memory” have challenged the notion that only visible stimuli can be actively maintained over time. In the present study, we investigated the neural dynamics of subliminal maintenance using multivariate pattern analyses of magnetoencephalography recordings (MEG). Subjects were presented with a masked Gabor patch whose angle had to be briefly memorized. We show with an unprecedented level of precision, that irrelevant sensory features of contrast, frequency and phase are only encoded transiently. Conversely, the relevant feature of angle is encoded and maintained in a distributed and dynamically changing manner throughout the brief retention period. Furthermore, although the visibility of the stimulus correlates with an amplification of late neural codes, we show that unseen stimuli can be partially maintained in the corresponding neural assemblies. Together, these results invalidate several predictions of current neuronal theories of visual awareness and suggest that visual perception relies on a long sequence of neural assemblies that repeatedly recode and maintain task-relevant features at multiple levels of processing, even under unconscious conditions.

[1]  C. Koch,et al.  The Neural Correlates of Consciousness , 2008, Annals of the New York Academy of Sciences.

[2]  Radoslaw Martin Cichy,et al.  Resolving human object recognition in space and time , 2014, Nature Neuroscience.

[3]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[4]  I. Fried,et al.  Neural “Ignition”: Enhanced Activation Linked to Perceptual Awareness in Human Ventral Stream Visual Cortex , 2009, Neuron.

[5]  B. Baars A cognitive theory of consciousness , 1988 .

[6]  Pavan Ramkumar,et al.  Feature-Specific Information Processing Precedes Concerted Activation in Human Visual Cortex , 2013, The Journal of Neuroscience.

[7]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[8]  David Soto,et al.  Neural basis of non-conscious visual working memory , 2014, NeuroImage.

[9]  S. Dehaene,et al.  Characterizing Consciousness: From Cognition to the Clinic? , 2011 .

[10]  H. Lau A higher order Bayesian decision theory of consciousness. , 2008, Progress in brain research.

[11]  Juha Silvanto,et al.  Working memory without consciousness , 2011, Current Biology.

[12]  Nicholas E. Myers,et al.  Revealing hidden states in visual working memory using electroencephalography , 2015, Front. Syst. Neurosci..

[13]  Nicholas E. Myers,et al.  Temporal Dynamics of Attention during Encoding versus Maintenance of Working Memory: Complementary Views from Event-related Potentials and Alpha-band Oscillations , 2015, Journal of Cognitive Neuroscience.

[14]  Michael Snodgrass,et al.  P3b, consciousness, and complex unconscious processing , 2015, Cortex.

[15]  Travis E. Oliphant,et al.  Python for Scientific Computing , 2007, Computing in Science & Engineering.

[16]  David A. Tovar,et al.  Representational dynamics of object vision: the first 1000 ms. , 2013, Journal of vision.

[17]  Juha Silvanto,et al.  Reappraising the relationship between working memory and conscious awareness , 2014, Trends in Cognitive Sciences.

[18]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.

[19]  S. Dehaene,et al.  Converging Intracranial Markers of Conscious Access , 2009, PLoS biology.

[20]  Johannes J. Fahrenfort,et al.  Dissociable Brain Mechanisms Underlying the Conscious and Unconscious Control of Behavior , 2011, Journal of Cognitive Neuroscience.

[21]  J. DiCarlo,et al.  Comparison of Object Recognition Behavior in Human and Monkey , 2014, The Journal of Neuroscience.

[22]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[23]  E. Vogel,et al.  Electrophysiological Evidence for a Postperceptual Locus of Suppression during the Attentional Blink Time-based Attention and the Attentional Blink , 1998 .

[24]  S. Dehaene,et al.  Distinct cortical codes and temporal dynamics for conscious and unconscious percepts , 2015, eLife.

[25]  Michael J. Wolff,et al.  Decoding Rich Spatial Information with High Temporal Resolution , 2015, Trends in Cognitive Sciences.

[26]  Sébastien M. Crouzet,et al.  Taste Quality Decoding Parallels Taste Sensations , 2015, Current Biology.

[27]  E. Bernat,et al.  Event-related brain potentials differentiate positive and negative mood adjectives during both supraliminal and subliminal visual processing. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[28]  S Dehaene,et al.  A model of subjective report and objective discrimination as categorical decisions in a vast representational space , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[29]  Geraint Rees,et al.  The Neural Correlates of Consciousness , 2003 .

[30]  Stanislas Dehaene,et al.  Decoding the Dynamics of Action, Intention, and Error Detection for Conscious and Subliminal Stimuli , 2014, The Journal of Neuroscience.

[31]  David Soto,et al.  Working memory biasing of visual perception without awareness , 2013, Attention, perception & psychophysics.

[32]  S. Taulu,et al.  Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements , 2006, Physics in medicine and biology.

[33]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[34]  J. Changeux,et al.  Experimental and Theoretical Approaches to Conscious Processing , 2011, Neuron.

[35]  Antonio Torralba,et al.  Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition , 2016, ArXiv.

[36]  Floris P. de Lange,et al.  Dissociating sensory from decision processes in human perceptual decision making , 2015, Scientific Reports.

[37]  Jochen Kaiser,et al.  Recurrence of task set-related MEG signal patterns during auditory working memory , 2016, Brain Research.

[38]  S. Dehaene,et al.  Timing of the brain events underlying access to consciousness during the attentional blink , 2005, Nature Neuroscience.

[39]  S. Dehaene,et al.  Characterizing the dynamics of mental representations: the temporal generalization method , 2014, Trends in Cognitive Sciences.

[40]  V. Lamme,et al.  The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.

[41]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[42]  Stanislas Dehaene,et al.  Cortical activity is more stable when sensory stimuli are consciously perceived , 2015, Proceedings of the National Academy of Sciences.

[43]  M. Overgaard,et al.  Introspection and subliminal perception , 2004 .

[44]  M. Stokes ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework , 2015, Trends in Cognitive Sciences.

[45]  D. Knill,et al.  Bayesian sampling in visual perception , 2011, Proceedings of the National Academy of Sciences.

[46]  N. Sigala,et al.  Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.

[47]  S. Dehaene,et al.  Brain Dynamics Underlying the Nonlinear Threshold for Access to Consciousness , 2007, PLoS biology.