Conceptual similarity in the brain

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Chapter 6 endnote 7 & 8, from How Emotions are Made: The Secret Life of the Brain by Lisa Feldman Barrett.
Some context is:

[note 7] Different instances of the same concept need not share the same neurons, and instances of different concepts need not be located in different groupings of neurons; different instances must be separable, not separate. [...] As you’ve read many times now, neurons are multipurpose; this is true even when it comes to concepts. Neurons alter their firing rate to participate in many different assemblies, so that a single neuron contributes to numerous instances of the same concept, as well as different concepts. Multipurpose does not mean all-purpose, of course. Different instances of the same concept need not share the same neurons, and instances of different concepts need not be located in different groupings of neurons; different instances must be separable, not separate.

[note 8] A child hears the word “sad” spoken in three different situations. These three instances are represented in the child’s brain in bits and pieces. They are not “grouped together” in any concrete way.

Brain imaging evidence shows that concepts themselves are represented widely across the brain.[1] When you focus your attention deliberately on a specific category, whether it’s cars, animals, or fear, the corresponding concept expands across the brain as more neurons represent it.[2] Particular neurons do not “hard code” the concept but are flexible in the information they represent, depending on the assembly of neurons they fire with.[3][4]

More conceptually-similar instances are represented in more overlapping neurons, at least in the part of the neural code that implements the multisensory summaries. There is a variety of evidence for this observation, some of it better than others (e.g., Studies using fMRI adaptation (such as the Bedny et al. paper)[5] provide arguably better evidence than, say, multi-voxel pattern similarity, which may or may not index neuronal overlap (because the same voxel can show a change in activity due to different sets of neurons contained therein).[6][7][8][9][10][11][12]

Grill-Spector & Weiner[13] have proposed that different types of concepts might be represented at different spatial scales, with multisensory summaries (is something animate or not?) represented across a wider swath of tissue in the visual cortex than, say, something more concrete (a body part). Although they were discussing the visual system, this might be a general property of how the brain represents mental vs. concrete concepts, with mental concepts being distributed across the entire brain.

See also

Notes on the Notes

  1. Huth, Alexander G., Shinji Nishimoto, An T. Vu, and Jack L. Gallant. 2012. "A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain." Neuron 76 (6): 1210-1224.
  2. Çukur, Tolga, Shinji Nishimoto, Alexander G. Huth, and Jack L. Gallant. 2013. "Attention During Natural Vision Warps Semantic Representation across the Human Brain." Nature Neuroscience 16 (6): 763-770.
  3. McIntosh, Anthony Randal. 2004. "Contexts and catalysts: A resolution of the localization and integration of function in the brain." Neuroinformatics 2 (2): 175-181.
  4. Gjorgjieva, Julijana, Guillaume Drion, and Eve Marder. 2016. "Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance." Current Ppinion in Neurobiology 37: 44-52.
  5. Bedny, Marina, Megan McGill, and Sharon L. Thompson-Schill. 2008. "Semantic adaptation and competition during word comprehension." Cerebral Cortex 18 (11): 2574-2585.
  6. Weber, Matthew, Sharon L. Thompson-Schill, Daniel Osherson, James Haxby, and Lawrence Parsons. 2009. "Predicting judged similarity of natural categories from their neural representations." Neuropsychologia 47 (3): 859-868.
  7. Kiani, Roozbeh, Hossein Esteky, Koorosh Mirpour, and Keiji Tanaka. 2007. "Object category structure in response patterns of neuronal population in monkey inferior temporal cortex." Journal of Neurophysiology 97 (6): 4296-4309.
  8. Devereux, Barry J., Alex Clarke, Andreas Marouchos, and Lorraine K. Tyler. 2013. "Representational similarity analysis reveals commonalities and differences in the semantic processing of words and objects." Journal of Neuroscience 33 (48): 18906-18916.
  9. Bruffaerts, Rose, Patrick Dupont, Ronald Peeters, Simon De Deyne, Gerrit Storms, and Rik Vandenberghe. 2013. "Similarity of fMRI activity patterns in left perirhinal cortex reflects semantic similarity between words." Journal of Neuroscience 33 (47): 18597-18607.
  10. Haxby, James V., Andrew C. Connolly, and J. Swaroop Guntupalli. 2014. "Decoding neural representational spaces using multivariate pattern analysis." Annual Review of Neuroscience 37: 435-456.
  11. Haynes, John-Dylan. 2015. "A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives." Neuron 87 (2): 257-270.
  12. Kriegeskorte, Nikolaus, and Rogier A. Kievit. 2013. "Representational geometry: integrating cognition, computation, and the brain." Trends in Cognitive Sciences 17 (8): 401-412.
  13. Grill-Spector, Kalanit, and Kevin S. Weiner. 2014. "The functional architecture of the ventral temporal cortex and its role in categorization." Nature Reviews Neuroscience 15 (8): 536-548.