Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-18T08:31:28.128Z Has data issue: false hasContentIssue false
  • English
  • Français

Scientific intuitions about the mind are wrong, misled by consciousness

Published online by Cambridge University Press:  30 June 2016

Leonid Perlovsky
Leonid Perlovsky*
Affiliation:
Athinoula A. Martinos Center for Biomedical Imaging, Harvard University, Charlestown, MA 02129. lperl@rcn.comhttp://www.leonid-perlovsky.com/
Get access Rights & Permissions [Opens in a new window]

Abstract

Logic is a fundamental reason why computational accounts of the mind have failed. Combinatorial complexity preventing computational accounts is equivalent to the Gödelian incompleteness of logic. The mind is not logical, but only logical states and processes in the mind are accessible to subjective consciousness. For this reason, intuitions of psychologists, cognitive scientists, and mathematicians modeling the mind are biased toward logic. This is also true about the changes proposed in After Phrenology (Anderson 2014).

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e128
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, M. L. (2014) After phrenology: Neural reuse and the interactive brain. MIT Press.Google Scholar
Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmid, A. M., Dale, A. M., Hämäläinen, M. S., Marinkovic, K., Schacter, D. L., Rosen, B. R. & Halgren, E. (2006) Top-down facilitation of visual recognition. Proceedings of the National Academy of Sciences USA 103:449–54.Google Scholar
Barnes, J., ed. (1984) The complete works of Aristotle: The revised Oxford translation. Princeton University Press.Google Scholar
Barsalou, L. W. (1999) Perceptual symbol systems. Behavioral and Brain Sciences 22(4):577–660.Google Scholar
Gödel, K. (2001) On formally undecidable propositions of Principia mathematica and related systems I. In: Collected works, volume I, publications 1929–1936, ed. Feferman, S., Dawson, J. W. Jr. & Kleene, S. C., pp. 145–95. Oxford University Press.Google Scholar
Perlovsky, L. I. (1998) Conundrum of combinatorial complexity. IEEE Transactions PAMI 20(6):666–70.CrossRefGoogle Scholar
Perlovsky, L. I. (2001) Neural networks and intellect: Using model-based concepts. Oxford University Press (3rd printing).Google Scholar
Perlovsky, L. I. (2006) Toward physics of the mind: Concepts, emotions, consciousness, and symbols. Physics of Life Reviews 3(1):22–55.Google Scholar
Perlovsky, L. I. (2007a) Symbols: Integrated cognition and language. In: Semiotics and intelligent systems development, ed. Gudwin, R. & Queiroz, J., pp. 121–51. Idea Group.CrossRefGoogle Scholar
Perlovsky, L. I. (2007b) The mind vs. logic: Aristotle and Zadeh. Society for mathematics of uncertainty. Critical Review 1(1):30–33.Google Scholar
Perlovsky, L. I. (2009) “Vague-to-Crisp” neural mechanism of perception. IEEE Transactions Neural Networks 20(8):1363–67.Google Scholar
Perlovsky, L. I. (2010a) Intersections of mathematical, cognitive, and aesthetic theories of mind. Psychology of Aesthetics, Creativity, and the Arts 4(1):11–17. doi: 10.1037/a0018147.Google Scholar
Perlovsky, L. I. (2010b) Musical emotions: Functions, origin, evolution. Physics of Life Reviews 7(1):2–27. doi: 10.1016/j.plrev.2009.11.001.Google Scholar
Perlovsky, L. I. (2013a) A challenge to human evolution – cognitive dissonance. Frontiers in Psychology 4:179. doi: 10.3389/fpsyg.2013.00179. Available at: http://www.frontiersin.org/cognitive_science/10.3389/fpsyg.2013.00179/full.Google Scholar
Perlovsky, L. I. (2013b) Language and cognition – joint acquisition, dual hierarchy, and emotional prosody. Frontiers in Behavioral Neuroscience 7:123. doi: 10.3389/fnbeh.2013.00123. Available at: http://www.frontiersin.org/Behavioral_Neuroscience/10.3389/fnbeh.2013.00123/full.CrossRefGoogle ScholarPubMed
Perlovsky, L. I. (2013c) Learning in brain and machine – complexity, Gödel, Aristotle. Frontiers in Neurorobotics 7:23. doi: 10.3389/fnbot.2013.00023. Available at: http://www.frontiersin.org/Neurorobotics/10.3389/fnbot.2013.00023/full.CrossRefGoogle ScholarPubMed
Perlovsky, L. I. (2014) Aesthetic emotions, what are their cognitive functions? Frontiers in Psychology 5:98. doi: 10.3389/fpsyg.2014.0009. Available at: http://www.frontiersin.org/Journal/10.3389/fpsyg.2014.00098/full.CrossRefGoogle ScholarPubMed
Perlovsky, L. I. (2015) Origin of music and the embodied cognition. Frontiers in Psychology 6:538. Available at: http://dx.doi.org/10.3389/fpsyg.2015.00538.Google Scholar
Perlovsky, L. I., Deming, R. W. & Ilin, R. (2011) Emotional cognitive neural algorithms with engineering applications. Dynamic Logic: From vague to crisp. Springer.Google Scholar
Vityaev, E. E., Perlovsky, L. I., Kovalerchuk, B. Y. & Speransky, S. O. (2013) Probabilistic dynamic logic of cognition. Invited Article. Biologically Inspired Cognitive Architectures 6:159–68.Google Scholar