Parallels between Machine and Brain Decoding

  • Lorenzo Dell’Arciprete
  • Brian Murphy
  • Fabio Massimo Zanzotto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7670)

Abstract

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies.

Keywords

Autism Spectrum Disorder Activation Image Semantic Space Distributional Vector Informational State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Wiener, N.: Cybernetics: Or the Control and Communication in the Animal and the Machine. MIT Press, Cambridge (1948)Google Scholar
  2. 2.
    von Neumann, J.: First draft of a report on the EDVAC. IEEE Ann. Hist. Comput. 15, 27–75 (1993)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Turing, A.: Intelligent machinery. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, vol. 5, pp. 3–23. Edinburgh University Press, Edinburgh (1969)Google Scholar
  4. 4.
    Putnam, H.: Minds and Machines. In: Hook, S. (ed.) Dimensions of Mind, pp. 130–164. Collier Books, New York (1960)Google Scholar
  5. 5.
    Chomsky, N.: Syntactic Structures. The Hague, Mouton (1957)Google Scholar
  6. 6.
    Chomsky, N.: A Review of B. F. Skinner’s Verbal Behavior. Language 35, 26–58 (1959)CrossRefGoogle Scholar
  7. 7.
    Pinker, S.: How the Mind Works. Norton and Company (2009)Google Scholar
  8. 8.
    Zanzotto, F.M., Croce, D.: Reading What Machines “Think”. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds.) BI 2009. LNCS (LNAI), vol. 5819, pp. 159–170. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Zanzotto, F.M., Croce, D.: Comparing EEG/ERP-Like and fMRI-Like Techniques for Reading Machine Thoughts. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 133–144. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Prezioso, S., Croce, D., Zanzotto, F.M.: Reading what machines ”think”: a challenge for nanotechnology. Journal of Computational and Theoretical Nanoscience 8, 1–6 (2011)CrossRefGoogle Scholar
  11. 11.
    Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001)CrossRefGoogle Scholar
  12. 12.
    Murphy, B., Baroni, M., Poesio, M.: EEG responds to conceptual stimuli and corpus semantics. In: Proceedings of EMNLP, pp. 619–627. ACL (2009)Google Scholar
  13. 13.
    Murphy, B., Poesio, M., Bovolo, F., Bruzzone, L., Dalponte, M., Lakany, H.: EEG decoding of semantic category reveals distributed representations for single concepts. Brain and Language 117, 12–22 (2011)CrossRefGoogle Scholar
  14. 14.
    Chan, A.M., Halgren, E., Marinkovic, K., Cash, S.S.: Decoding word and category-specific spatiotemporal representations from MEG and EEG. NeuroImage 54, 3028–3039 (2011)CrossRefGoogle Scholar
  15. 15.
    Sudre, G., Pomerleau, D., Palatucci, M., Wehbe, L., Fyshe, A., Salmelin, R., Mitchell, T.: Tracking Neural Coding of Perceptual and Semantic Features of Concrete Nouns. NeuroImage 62, 451–463 (2012)CrossRefGoogle Scholar
  16. 16.
    Montague, R.: English as a formal language. In: Thomason, R. (ed.) Formal Philosophy: Selected Papers of Richard Montague, pp. 188–221. Yale University Press, New Haven (1974)Google Scholar
  17. 17.
    Plate, T.A.: Distributed Representations and Nested Compositional Structure. PhD thesis (1994)Google Scholar
  18. 18.
    Mitchell, J., Lapata, M.: Vector-based models of semantic composition. In: Proceedings of ACL 2008: HLT, pp. 236–244. Association for Computational Linguistics, Columbus (2008)Google Scholar
  19. 19.
    Jones, M.N., Mewhort, D.J.K.: Representing word meaning and order information in a composite holographic lexicon. Psychological Review 114, 1–37 (2007)CrossRefGoogle Scholar
  20. 20.
    Zanzotto, F.M., Korkontzelos, I., Fallucchi, F., Manandhar, S.: Estimating linear models for compositional distributional semantics. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING (2010)Google Scholar
  21. 21.
    Baroni, M., Zamparelli, R.: Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1183–1193. Association for Computational Linguistics, Cambridge (2010)Google Scholar
  22. 22.
    Guevara, E.: A regression model of adjective-noun compositionality in distributional semantics. In: Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics, pp. 33–37. Association for Computational Linguistics, Uppsala (2010)Google Scholar
  23. 23.
    McCarthy, D., Carroll, J.: Disambiguating nouns, verbs, and adjectives using automatically acquired selectional preferences. Comput. Linguist. 29, 639–654 (2003)MATHCrossRefGoogle Scholar
  24. 24.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)MATHCrossRefGoogle Scholar
  25. 25.
    Harris, Z.: Distributional structure. In: Katz, J.J., Fodor, J.A. (eds.) The Philosophy of Linguistics. Oxford University Press, New York (1964)Google Scholar
  26. 26.
    Firth, J.R.: Papers in Linguistics. Oxford University Press, Oxford (1957)Google Scholar
  27. 27.
    Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Processes VI, 1–28 (1991)Google Scholar
  28. 28.
    Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers 28, 203–208 (1996)CrossRefGoogle Scholar
  29. 29.
    Pado, S., Lapata, M.: Dependency-based construction of semantic space models. Computational Linguistics 33, 161–199 (2007)MATHCrossRefGoogle Scholar
  30. 30.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41, 391–407 (1990)CrossRefGoogle Scholar
  31. 31.
    Lin, D., Pantel, P.: DIRT-discovery of inference rules from text. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD 2001), San Francisco, CA (2001)Google Scholar
  32. 32.
    Pakkenberg, B., Gundersen, H.J.: Neocortical neuron number in humans: effect of sex and age. Journal of Comparative Neurology 384, 312–320 (1997)CrossRefGoogle Scholar
  33. 33.
    Stark, A., Toft, M., Pakkenberg, H., Fabricius, K., Eriksen, N., Pelvig, D., Møller, M., Pakkenberg, B.: The effect of age and gender on the volume and size distribution of neocortical neurons. Neuroscience 150, 121–130 (2007)CrossRefGoogle Scholar
  34. 34.
    Azevedo, F.A.C., Carvalho, L.R.B., Grinberg, L.T., Farfel, J.M., Ferretti, R.E.L., Leite, R.E.P., Jacob Filho, W., Lent, R., Herculano-Houzel, S.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology 513, 532–541 (2009)CrossRefGoogle Scholar
  35. 35.
    Buxhoeveden, D.P., Casanova, M.F.: The minicolumn hypothesis in neuroscience. Brain: A Journal of Neurology 125, 935–951 (2002)CrossRefGoogle Scholar
  36. 36.
    Mountcastle, V.B.: The columnar organization of the neocortex. Brain: A Journal of Neurology 120, 701–722 (1997)CrossRefGoogle Scholar
  37. 37.
    Raz, N., Gunning-Dixon, F., Head, D., Rodrigue, K.M., Williamson, A., Acker, J.D.: Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume. Neurobiol. Aging. 25, 377–396 (2004)CrossRefGoogle Scholar
  38. 38.
    Gur, R.C., Turetsky, B.I., Matsui, M., Yan, M., Bilker, W., Hughett, P., Gur, R.E.: Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance. J. Neurosci. 19, 4065–4072 (1999)Google Scholar
  39. 39.
    Mahon, B.Z., Anzellotti, S., Schwarzbach, J., Zampini, M., Caramazza, A.: Category-specific organization in the human brain does not require visual experience. Neuron 63, 397–405 (2009)CrossRefGoogle Scholar
  40. 40.
    Hagoort, P.: On Broca, brain, and binding: a new framework. Trends in Cognitive Sciences 9, 416–423 (2005)CrossRefGoogle Scholar
  41. 41.
    Yamasaki, S., Yamasue, H., Abe, O., Suga, M., Yamada, H., Inoue, H., Kuwabara, H., Kawakubo, Y., Yahata, N., Aoki, S., Kano, Y., Kato, N., Kasai, K.: Reduced gray matter volume of pars opercularis is associated with impaired social communication in high-functioning autism spectrum disorders. Biol. Psychiatry 68, 1141–1147 (2010)CrossRefGoogle Scholar
  42. 42.
    Alvarado, P., Doerfler, P., Wickel, J.: Axon2 - a visual object recognition system for non-rigid objects. In: IASTED International Conference-Signal Processing, Pattern Recognition and Applications (SPPRA), pp. 235–240. Rhodes, IASTED (2001)Google Scholar
  43. 43.
    Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann (1993)Google Scholar
  44. 44.
    John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers, pp. 338–345 (1995)Google Scholar
  45. 45.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)Google Scholar
  46. 46.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, Chicago (1999)Google Scholar
  47. 47.
    Miller, G.A.: WordNet: A lexical database for English. Communications of the ACM 38, 39–41 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lorenzo Dell’Arciprete
    • 1
  • Brian Murphy
    • 2
  • Fabio Massimo Zanzotto
    • 1
  1. 1.Artificial Intelligence ResearchUniversity of Rome Tor VergataRomeItaly
  2. 2.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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