Is neuroimaging measuring information in the brain?

Psychology moved beyond the stimulus response mapping of behaviorism by adopting an information processing framework. This shift from behavioral to cognitive science was partly inspired by work demonstrating that the concept of information could be defined and quantified (Shannon, 1948). This transition developed further from cognitive science into cognitive neuroscience, in an attempt to measure information in the brain. In the cognitive neurosciences, however, the term information is often used without a clear definition. This paper will argue that, if the formulation proposed by Shannon is applied to modern neuroimaging, then numerous results would be interpreted differently. More specifically, we argue that much modern cognitive neuroscience implicitly focuses on the question of how we can interpret the activations we record in the brain (experimenter-as-receiver), rather than on the core question of how the rest of the brain can interpret those activations (cortex-as-receiver). A clearer focus on whether activations recorded via neuroimaging can actually act as information in the brain would not only change how findings are interpreted but should also change the direction of empirical research in cognitive neuroscience.

[1]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[2]  Thomas S. Kuhn,et al.  The Function of Measurement in Modern Physical Science , 1961, Isis.

[3]  D. Navon Forest before trees: The precedence of global features in visual perception , 1977, Cognitive Psychology.

[4]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[5]  K. H. Britten,et al.  Neuronal correlates of a perceptual decision , 1989, Nature.

[6]  R. Näätänen The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive function , 1990, Behavioral and Brain Sciences.

[7]  J. Koenderink The brain a geometry engine , 1990, Psychological research.

[8]  J. B. Levitt,et al.  Receptive fields and functional architecture of macaque V2. , 1994, Journal of neurophysiology.

[9]  J W Belliveau,et al.  Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. , 1995, Science.

[10]  W. Freeman Societies of Brains: A Study in the Neuroscience of Love and Hate. By W. J. Freeman. Erlbaum: Hillsdale, NJ. 1994. , 1997, Psychological Medicine.

[11]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[12]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[13]  Wolf Singer,et al.  Neuronal Synchrony: A Versatile Code for the Definition of Relations? , 1999, Neuron.

[14]  Rajesh P. N. Rao,et al.  Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .

[15]  Karl J. Friston,et al.  Attentional modulation of effective connectivity from V2 to V5/MT in humans. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[16]  R. Zemel,et al.  Information processing with population codes , 2000, Nature Reviews Neuroscience.

[17]  R H Wurtz,et al.  Multielectrode evidence for spreading activity across the superior colliculus movement map. , 2000, Journal of neurophysiology.

[18]  A. T. Smith,et al.  Estimating receptive field size from fMRI data in human striate and extrastriate visual cortex. , 2001, Cerebral cortex.

[19]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[20]  Arnaud Delorme,et al.  Spike-based strategies for rapid processing , 2001, Neural Networks.

[21]  R. Wurtz,et al.  Sequential activity of simultaneously recorded neurons in the superior colliculus during curved saccades. , 2003, Journal of neurophysiology.

[22]  S. Kosslyn,et al.  Neural correlates of different types of deception: an fMRI investigation. , 2003, Cerebral cortex.

[23]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[24]  David J. Field,et al.  How Close Are We to Understanding V1? , 2005, Neural Computation.

[25]  J. Driver,et al.  Visibility Reflects Dynamic Changes of Effective Connectivity between V1 and Fusiform Cortex , 2005, Neuron.

[26]  G. Rees,et al.  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2005, Nature Neuroscience.

[27]  W. Klimesch,et al.  Alpha phase synchronization predicts P1 and N1 latency and amplitude size. , 2005, Cerebral cortex.

[28]  M. Posner,et al.  Timing the Brain: Mental Chronometry as a Tool in Neuroscience , 2005, PLoS biology.

[29]  J. J. Wright,et al.  Measurement of phase gradients in the EEG , 2006, Journal of Neuroscience Methods.

[30]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[31]  Matthew H. Davis,et al.  Detecting awareness in the vegetative state. , 2006, Science.

[32]  D. Heeger,et al.  Two Retinotopic Visual Areas in Human Lateral Occipital Cortex , 2006, The Journal of Neuroscience.

[33]  T. Schenk,et al.  An allocentric rather than perceptual deficit in patient D.F. , 2006, Nature Neuroscience.

[34]  Behzad Mansouri,et al.  The fidelity of the cortical retinotopic map in human amblyopia , 2007, The European journal of neuroscience.

[35]  N. Kanwisher,et al.  Only some spatial patterns of fMRI response are read out in task performance , 2007, Nature Neuroscience.

[36]  Alexander Borst,et al.  How does Nature Program Neuron Types? , 2008, Front. Neurosci..

[37]  Matthew H. Davis,et al.  Detecting Awareness in the Vegetative State , 2006, Science.

[38]  Brian A. Wandell,et al.  Population receptive field estimates in human visual cortex , 2008, NeuroImage.

[39]  N. Logothetis What we can do and what we cannot do with fMRI , 2008, Nature.

[40]  M. Sereno,et al.  Retinotopy and Attention in Human Occipital, Temporal, Parietal, and Frontal Cortex , 2008 .

[41]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[42]  Donald D. Hoffman,et al.  Object Categorization: The Interface Theory of Perception: Natural Selection Drives True Perception to Swift Extinction , 2009 .

[43]  Nikolaus Kriegeskorte,et al.  Relating Population-Code Representations between Man, Monkey, and Computational Models , 2009, Front. Neurosci..

[44]  Colin W. G. Clifford,et al.  Discrimination of the local orientation structure of spiral Glass patterns early in human visual cortex , 2009, NeuroImage.

[45]  Jeffrey S Bowers,et al.  On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience. , 2009, Psychological review.

[46]  D. Alexander,et al.  Mapping of contextual modulation in the population response of primary visual cortex , 2010, Cognitive Neurodynamics.

[47]  Bruce C Hansen,et al.  Disrupted retinotopic maps in amblyopia. , 2009, Investigative ophthalmology & visual science.

[48]  D. McCormick,et al.  Endogenous Electric Fields May Guide Neocortical Network Activity , 2010, Neuron.

[49]  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2010 .

[50]  Jascha D. Swisher,et al.  Multiscale Pattern Analysis of Orientation-Selective Activity in the Primary Visual Cortex , 2010, The Journal of Neuroscience.

[51]  Justin L. Gardner,et al.  Is cortical vasculature functionally organized? , 2010, NeuroImage.

[52]  Hans P. Op de Beeck,et al.  Against hyperacuity in brain reading: Spatial smoothing does not hurt multivariate fMRI analyses? , 2010, NeuroImage.

[53]  Dwight J. Kravitz,et al.  Real-World Scene Representations in High-Level Visual Cortex: It's the Spaces More Than the Places , 2011, The Journal of Neuroscience.

[54]  Konrad P Kording,et al.  How advances in neural recording affect data analysis , 2011, Nature Neuroscience.

[55]  J. Gallant,et al.  Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies , 2011, Current Biology.

[56]  Jeremy Freeman,et al.  Orientation Decoding Depends on Maps, Not Columns , 2011, The Journal of Neuroscience.

[57]  P. Schyns,et al.  Cracking the Code of Oscillatory Activity , 2011, PLoS biology.

[58]  Jakob Heinzle,et al.  Topographically specific functional connectivity between visual field maps in the human brain , 2011, NeuroImage.

[59]  Gregor Thut,et al.  Rhythmic TMS over Parietal Cortex Links Distinct Brain Frequencies to Global versus Local Visual Processing , 2011, Current Biology.

[60]  J. Haynes Brain Reading: Decoding Mental States From Brain Activity In Humans , 2011 .

[61]  Sergei Silvestrov,et al.  Analysis for Science, Engineering and Beyond : The Tribute Workshop in Honour of Gunnar Sparr held in Lund, May 8-9, 2008 , 2012 .

[62]  Konrad P. Körding,et al.  Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons , 2012, PLoS Comput. Biol..

[63]  A. Parker,et al.  Structural and Functional Changes across the Visual Cortex of a Patient with Visual Form Agnosia , 2013, The Journal of Neuroscience.

[64]  N. Turk-Browne Functional Interactions as Big Data in the Human Brain , 2013, Science.

[65]  Jeremy Freeman,et al.  Coarse-Scale Biases for Spirals and Orientation in Human Visual Cortex , 2013, The Journal of Neuroscience.

[66]  Nikolaus Kriegeskorte,et al.  fMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli , 2012, Front. Psychol..

[67]  Thomas F. Collura,et al.  Technical Foundations of Neurofeedback , 2013 .

[68]  Johan Wagemans,et al.  The distributed representation of random and meaningful object pairs in human occipitotemporal cortex: The weighted average as a general rule , 2013, NeuroImage.

[69]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[70]  Dwight J. Kravitz,et al.  The ventral visual pathway: an expanded neural framework for the processing of object quality , 2013, Trends in Cognitive Sciences.

[71]  Johanna Ruescher,et al.  Traveling waves and trial averaging: The nature of single-trial and averaged brain responses in large-scale cortical signals , 2013, NeuroImage.

[72]  B. Rogers Delusions about Illusions , 2014, Perception.

[73]  P. Roelfsema,et al.  Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex , 2014, Proceedings of the National Academy of Sciences.

[74]  Eero P. Simoncelli,et al.  Partitioning neuronal variability , 2014, Nature Neuroscience.

[75]  Case study of unexplained visual field loss and perceptual deficits in the presence of normal early visual function , 2014 .

[76]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[77]  Li Su,et al.  A Toolbox for Representational Similarity Analysis , 2014, PLoS Comput. Biol..

[78]  J. Koenderink The All Seeing Eye? , 2014, Perception.

[79]  Doris Y. Tsao,et al.  Single-Unit Recordings in the Macaque Face Patch System Reveal Limitations of fMRI MVPA , 2015, The Journal of Neuroscience.

[80]  Donald D. Hoffman,et al.  The Interface Theory of Perception , 2015, Psychonomic bulletin & review.

[81]  CHERYL A. OLMAN,et al.  What insights can fMRI offer into the structure and function of mid-tier visual areas? , 2015, Visual Neuroscience.