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Reason for optimism: How a shifting focus on neural population codes is moving cognitive neuroscience beyond phrenology

Published online by Cambridge University Press:  30 June 2016

Carolyn Parkinson  and
Thalia Wheatley
Carolyn Parkinson
Affiliation:
Department of Psychology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095. [email protected]://csnlab.org
Thalia Wheatley
Affiliation:
Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755. [email protected]://www.wheatlab.com
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Abstract

Multivariate pattern analysis can address many of the challenges for cognitive neuroscience highlighted in After Phrenology (Anderson 2014) by illuminating the information content of brain regions and by providing insight into whether functional overlap reflects the recruitment of common or distinct computational mechanisms. Further, failing to consider submaximal but reliable population responses can lead to an overly modular account of brain function.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e126
Copyright
Copyright © Cambridge University Press 2016 

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References

Anderson, M. L. (2010) Neural reuse: A fundamental organizational principle of the brain. Behavioral and Brain Sciences 33(4):245–66. doi: 10.1017/S0140525X10000853.Google Scholar
Anderson, M. L. (2014) After phrenology: Neural reuse and the interactive brain. MIT Press.Google Scholar
Chang, S. W. C., Brent, L. J. N., Adams, G. K., Klein, J. T., Pearson, J. M., Watson, K. K. & Platt, M. L. (2013) Neuroethology of primate social behavior. Proceedings of the National Academy of Sciences of the United States of America 110(Suppl.):10387–94. doi: 10.1073/pnas.1301213110.CrossRefGoogle ScholarPubMed
Connolly, A. C., Gobbini, M. I. & Haxby, J. V. (2012) Three virtues of similarity-based multivariate pattern analysis: An example from the human object vision pathway. In: Understanding visual population codes: Toward a common multivariate framework for cell recording and functional imaging, ed. Kriegeskorte, N. & Kreiman, G., pp. 335–55. MIT Press.Google Scholar
Dehaene, S. & Cohen, L. (2007) Cultural recycling of cortical maps. Neuron 56(2):384–98. doi: 10.1016/j.neuron.2007.10.004.CrossRefGoogle ScholarPubMed
Georgopoulos, A., Kettner, R. & Schwartz, A. (1988) Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. Journal of Neuroscience 8(8):2928–37.CrossRefGoogle ScholarPubMed
Haxby, J. V, Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L. & Pietrini, P. (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539):2425–30. doi: 10.1126/science.1063736.Google Scholar
Knops, A., Thirion, B., Hubbard, E. M., Michel, V. & Dehaene, S. (2009) Recruitment of an area involved in eye movements during mental arithmetic. Science 324(5934):1583–85. doi: 10.1126/science.1171599.Google Scholar
Kriegeskorte, N., Mur, M. & Bandettini, P. (2008a) Representational similarity analysis: Connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience 2:4. doi: 10.3389/neuro.06.004.2008.Google Scholar
Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., Tanaka, K. & Bandettini, P. A. (2008b) Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60(6):1126–41. doi: 10.1016/j.neuron.2008.10.043.CrossRefGoogle ScholarPubMed
Lin, L., Osan, R. & Tsien, J. Z. (2006) Organizing principles of real-time memory encoding: Neural clique assemblies and universal neural codes. Trends in Neurosciences 29(1):48–57. doi: 10.1016/j.tins.2005.11.004.Google Scholar
Marcus, G. F. (2006) Cognitive architecture and descent with modification. Cognition 101(2):443–65. doi: 10.1016/j.cognition.2006.04.009.Google Scholar
Parkinson, C., Liu, S. & Wheatley, T. (2014) A common cortical metric for spatial, temporal, and social distance. Journal of Neuroscience 34(5):1979–87. doi: 10.1523/JNEUROSCI.2159-13.2014.Google Scholar
Parkinson, C. & Wheatley, T. (2013) Old cortex, new contexts: Re-purposing spatial perception for social cognition. Frontiers in Human Neuroscience 7:645. doi: 10.3389/fnhum.2013.00645.Google Scholar
Parkinson, C. & Wheatley, T. (2015) The repurposed social brain. Trends in Cognitive Sciences 19(3):133–41. doi: 10.1016/j.tics.2015.01.003.Google Scholar
Peelen, M. V. & Downing, P. E. (2007) Using multi-voxel pattern analysis of fMRI data to interpret overlapping functional activations. Trends in Cognitive Sciences 11(1):4–5. doi: 10.1016/j.tics.2006.10.009.CrossRefGoogle ScholarPubMed
Pouget, A., Dayan, P. & Zemel, R. (2000) Information processing with population codes. Nature Reviews Neuroscience 1(2):125–32. doi: 10.1038/35039062.CrossRefGoogle ScholarPubMed
Simon, S. A., de Araujo, I. E., Gutierrez, R. & Nicolelis, M. A. L. (2006) The neural mechanisms of gustation: A distributed processing code. Nature Reviews Neuroscience 7(11):890–901. doi: 10.1038/nrn2006.Google Scholar