Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-11T04:34:44.120Z Has data issue: false hasContentIssue false
  • English
  • Français

On the quantum principles of cognitive learning

Published online by Cambridge University Press:  14 May 2013

Alexandre de Castro
Alexandre de Castro*
Affiliation:
Laboratório de Matemática Computacional – CNPTIA/Embrapa – Campinas, 13083-886 SP, Brazil. alexandre.castro@embrapa.brhttp://www.cnptia.embrapa.br
Get access Rights & Permissions [Opens in a new window]

Abstract

Pothos & Busemeyer's (P&B's) query about whether quantum probability can provide a foundation for the cognitive modeling embodies so many underlying implications that the subject is far from exhausted. In this brief commentary, however, I suggest that the conceptual thresholds of the meaningful learning give rise to a typical Boltzmann's weighting measure, which indicates a statistical verisimilitude of quantum behavior in the human cognitive ensemble.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 281 - 282
Copyright
Copyright © Cambridge University Press 2013 

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

Ausubel, D. P. (1963) The psychology of meaningful verbal learning. Grune & Stratton.Google Scholar
Ausubel, D. P. (1968) Educational psychology: A cognitive view. Holt, Rinehart & Winston.Google Scholar
Bawden, D. (2008) Smoother pebbles and the shoulders of giants: The developing foundations of information science. Journal of Information Science 34(4), 415–26.Google Scholar
Bawden, D. (2011) Brookes equation: The basis for a qualitative characterization of information behaviours. Journal of Information Science 37(1):101–108.Google Scholar
Brookes, B. C. (1980) The foundations of information science, Part 1. Philosophical aspects. Journal of Information Science 2(3/4):125–34.Google Scholar
Brookes, B. C. (1981) Information technology and the science of information. In: Information retrieval research, ed. Oddy, R. N., Robertson, S. E., van Rijsbergen, C. J. & Williams, P. W., pp. 1–8. Butterworths.Google Scholar
Brown, H. D. (2000) Principles of language learning and teaching. Addison Wesley Longman, Inc.Google Scholar
Carter, A. H. (2001) Classical and statistical thermodynamics. Prentice-Hall.Google Scholar
Castro, A. (2013) The thermodynamic cost of fast thought. Minds and Machines. DOI: 10.1007/s11023-013-9302-x.Google Scholar
Cole, C. (1997) Calculating the information content of an information process for a domain expert using Shannon's mathematical theory of communication: A preliminary analysis. Information Processing and Management 33(6):715–26.Google Scholar
Cole, C. (2011) A theory of information need for information retrieval that connects information to knowledge. Journal of the American Society for Information Science and Technology 62(7):1216–31.CrossRefGoogle Scholar
Cornelius, I. (2002) Theorizing information for information science. Annual Review of Information Science and Technology 36:393–425.Google Scholar
Dirac, P. A. M. (1999) The principles of quantum mechanics (International series of monographs on physics). Oxford University Press.Google Scholar
Gasset, J. O. (1998) Meditaciones del Quijote. Catedra.Google Scholar
Gokhale, A. A. (2004) Introduction to telecommunications (2nd ed.). Thomson Delmar Learning.Google Scholar
Moreira, M. A. (2011) Meaningful learning: From the classical to the critical view. Meaningful Learning Review 1:1–15.Google Scholar
Neill, S. D. (1982) Brookes, Popper and objective knowledge. Journal of Information Science 4(1):33–39.Google Scholar
Novak, J. D. (2011) A theory of education: Meaningful learning underlies the constructive integration of thinking, feeling, and acting leading to empowerment for commitment and responsibility. Meaningful Learning Review 1(2):1–14.Google Scholar
Seel, N. M. (2012) Assimilation theory of learning. In: Encyclopedia of the Sciences of Learning, ed. Seel, N. M., pp. 324–26. Springer.Google Scholar
Seising, S. (2010) Cybernetics, system(s) theory, information theory and fuzzy sets and systems in the 1950s and 1960s. Information Sciences 180:4459–76.Google Scholar
Todd, R. J. (1999) Back to our beginnings: Information utilization, Bertram Brookes and the fundamental equation of information science. Information Processing and Management 35(6):851–70.Google Scholar
Vitiello, G. (1995) Dissipation and memory capacity in the quantum brain model. International Journal of Modern Physics B9:973–89.CrossRefGoogle Scholar