Literature DB >> 16051174

Predicting the stream of consciousness from activity in human visual cortex.

John-Dylan Haynes1, Geraint Rees.   

Abstract

Can the rapid stream of conscious experience be predicted from brain activity alone? Recently, spatial patterns of activity in visual cortex have been successfully used to predict feature-specific stimulus representations for both visible and invisible stimuli. However, because these studies examined only the prediction of static and unchanging perceptual states during extended periods of stimulation, it remains unclear whether activity in early visual cortex can also predict the rapidly and spontaneously changing stream of consciousness. Here, we used binocular rivalry to induce frequent spontaneous and stochastic changes in conscious experience without any corresponding changes in sensory stimulation, while measuring brain activity with fMRI. Using information that was present in the multivariate pattern of responses to stimulus features, we could accurately predict, and therefore track, participants' conscious experience from the fMRI signal alone while it underwent many spontaneous changes. Prediction in primary visual cortex primarily reflected eye-based signals, whereas prediction in higher areas reflected the color of the percept. Furthermore, accurate prediction during binocular rivalry could be established with signals recorded during stable monocular viewing, showing that prediction generalized across viewing conditions and did not require or rely on motor responses. It is therefore possible to predict the dynamically changing time course of subjective experience with only brain activity.

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Year:  2005        PMID: 16051174     DOI: 10.1016/j.cub.2005.06.026

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  108 in total

1.  Predicting vocal emotion expressions from the human brain.

Authors:  Sonja A Kotz; Christian Kalberlah; Jörg Bahlmann; Angela D Friederici; John-D Haynes
Journal:  Hum Brain Mapp       Date:  2012-02-27       Impact factor: 5.038

2.  Probing principles of large-scale object representation: category preference and location encoding.

Authors:  Radoslaw Martin Cichy; Philipp Sterzer; Jakob Heinzle; Lloyd T Elliott; Fernando Ramirez; John-Dylan Haynes
Journal:  Hum Brain Mapp       Date:  2012-02-27       Impact factor: 5.038

3.  Tracking children's mental states while solving algebra equations.

Authors:  John R Anderson; Shawn Betts; Jennifer L Ferris; Jon M Fincham
Journal:  Hum Brain Mapp       Date:  2011-09-20       Impact factor: 5.038

4.  Right parietal brain activity precedes perceptual alternation during binocular rivalry.

Authors:  Juliane Britz; Michael A Pitts; Christoph M Michel
Journal:  Hum Brain Mapp       Date:  2010-08-05       Impact factor: 5.038

5.  Neural imaging to track mental states while using an intelligent tutoring system.

Authors:  John R Anderson; Shawn Betts; Jennifer L Ferris; Jon M Fincham
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-24       Impact factor: 11.205

6.  Exploring predictive and reproducible modeling with the single-subject FIAC dataset.

Authors:  Xu Chen; Francisco Pereira; Wayne Lee; Stephen Strother; Tom Mitchell
Journal:  Hum Brain Mapp       Date:  2006-05       Impact factor: 5.038

7.  A neural basis for inference in perceptual ambiguity.

Authors:  Philipp Sterzer; Andreas Kleinschmidt
Journal:  Proc Natl Acad Sci U S A       Date:  2006-12-26       Impact factor: 11.205

8.  Real-time fMRI using brain-state classification.

Authors:  Stephen M LaConte; Scott J Peltier; Xiaoping P Hu
Journal:  Hum Brain Mapp       Date:  2007-10       Impact factor: 5.038

9.  Perceptual experience modulates cortical circuits involved in visual awareness.

Authors:  Maartje C de Jong; Zoe Kourtzi; Raymond van Ee
Journal:  Eur J Neurosci       Date:  2012-10-03       Impact factor: 3.386

Review 10.  Machine learning classifiers and fMRI: a tutorial overview.

Authors:  Francisco Pereira; Tom Mitchell; Matthew Botvinick
Journal:  Neuroimage       Date:  2008-11-21       Impact factor: 6.556

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