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Algorithmic Cognition and the Computational Nature of the Mind

  • Hector ZenilEmail author
  • Nicolas Gauvrit
Reference work entry
Part of the Encyclopedia of Complexity and Systems Science Series book series (ECSSS)

Glossary

Algorithmic coding theorem (not to confuse with Shannon’s coding theorem)

A theorem that formally establishes an inversely proportional relationship between Kolmogorov-Chaitin complexity and algorithmic probability.

Algorithmic cognition

The study of animal, human, and artificial cognition based on the theory of algorithmic probability.

Algorithmic information theory

The literature based on the concept of Kolmogorov-Chaitin complexity and related concepts such as algorithmic probability, compression, optimal inference, the Universal Distribution, and Levin’s semi-measure.

Algorithmic probability

The probability to produce an object from a random digital computer program whose program binary digits are chosen by chance. The calculation of algorithmic probability is a lower semi-computable problem.

Algorithmic randomness

How removed the length of the shortest generating program is from the size of the uncompressed data that such program generates.

Block decomposition method (BDM)

Bibliography

  1. Casali AG, Gosseries O, Rosanova M, Boly M, Sarasso S, Casali KR, Casarotto S, Bruno M-A, Laureys S, Tononi G et al (2013) A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med 5(198):105CrossRefGoogle Scholar
  2. Chater N (1999) The search for simplicity: a fundamental cognitive principle? Quart J Exp Psychol A 52(2):273–302CrossRefGoogle Scholar
  3. Chekaf M, Gauvrit N, Mathy F (2014) Chunking on the fly in working memory and its relationship to intelligence. In: 55th Annual meeting of the psychonomic societyGoogle Scholar
  4. Cowan N (2010) The magical mystery four: How is working memory capacity limited, and why? Curr Dir Psychol Sci 19(1):51–57CrossRefGoogle Scholar
  5. Dieguez S, Wagner-Egger P, Gauvrit N (2015) Nothing happens by accident, or does it? a low prior for randomness does not explain belief in conspiracy theories. Psychol Sci 26(11):1762–1770CrossRefGoogle Scholar
  6. Gauvrit N, Kinga M (2014) The equiprobability bias from a mathematical and psychological perspective. Adv Cogn Psychol 10(4):119–130Google Scholar
  7. Gauvrit N, Soler-Toscano F, Zenil H (2014a) Natural scene statistics mediate the perception of image complexity. Vis Cogn 22(8):1084–1091CrossRefGoogle Scholar
  8. Gauvrit N, Zenil H, Delahaye J-P, Soler-Toscano F (2014b) Algorithmic complexity for short binary strings applied to psychology: a primer. Behav Res Methods 46(3):732–744CrossRefGoogle Scholar
  9. Gauvrit N, Zenil H, Tegnér J (2015) The information-theoretic and algorithmic approach to human, animal and artificial cognition. arXiv preprint arXiv:1501.04242.Google Scholar
  10. Gauvrit N, Singmann H, Soler-Toscano F, Zenil H (2016) Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method. Behav Res Methods 48(1):314–329CrossRefGoogle Scholar
  11. Gauvrit N, Soler-Toscano F, Guida A (2017a) A preference for some types of complexity comment on perceived beauty of random texture patterns: a preference for complexity. Acta Psychol 174:48–53CrossRefGoogle Scholar
  12. Gauvrit N, Zenil H, Soler-Toscano F, Delahaye J-P, Brugger P (2017b) Human behavioral complexity peaks at age 25. PLoS Comput Biol 13(4):e1005408CrossRefGoogle Scholar
  13. Hsu AS, Griffiths TL, Schreiber E (2010) Subjective randomness and natural scene statistics. Psychon Bull Rev 17(5):624–629CrossRefGoogle Scholar
  14. Kahneman D, Slovic P, Tversky A (1982) Judgment under uncertainty: heuristics and biases. Cambridge University Press, New York and Cambridge.Google Scholar
  15. Kempe V, Gauvrit N, Forsyth D (2015) Structure emerges faster during cultural transmission in children than in adults. Cognition 136:247–254CrossRefGoogle Scholar
  16. Lecoutre M-P (1992) Cognitive models and problem spaces in “purely random” situations. Educ Stud Math 23(6):557–568CrossRefGoogle Scholar
  17. Mach E (1914) The analysis of sensations, and the relation of the physical to the psychical. Open Court Publishing Company, ChicagoGoogle Scholar
  18. Maguire P, Moser P, Maguire R, Griffith V 2014 Is consciousness computable? quantifying integrated information using algorithmic information theory. arXiv preprint arXiv:1405.0126Google Scholar
  19. Mathy F, Feldman J (2012) Whats magic about magic numbers? chunking and data compression in short-term memory. Cognition 122(3):346–362CrossRefGoogle Scholar
  20. Masafumi Oizumi, Larissa Albantakis, and Giulio Tononi. From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0. PLoS computational biology, 10(5):e1003588, 2014.Google Scholar
  21. Peng Z, Genewein T, Braun DA (2014) Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences. Front Hum Neurosci 8:168CrossRefGoogle Scholar
  22. Reznikova Z, Ryabko B (2011) Numerical competence in animals, with an insight from ants. Behaviour:405–434Google Scholar
  23. Reznikova Z, Ryabko B (2012) Ants and bits. IEEE Inform Theor Soc News 62(5):17–20Google Scholar
  24. Ryabko B, Reznikova Z (2009) The use of ideas of information theory for studying “language” and intelligence in ants. Entropy 11(4):836–853CrossRefGoogle Scholar
  25. Soler-Toscano F, Zenil H, Delahaye J-P, Gauvrit N (2014) Calculating Kolmogorov complexity from the output frequency distributions of small Turing machines. PLoS One 9(5):e96223CrossRefGoogle Scholar
  26. Tversky A, Kahneman D (1975) Judgment under uncertainty: heuristics and biases. In: Utility, probability, and human decision making. Springer, New York, pp 141–162CrossRefGoogle Scholar
  27. Ze W, Li Y, Childress AR, Detre JA (2014) Brain entropy mapping using fmri. PLoS One 9(3):e89948CrossRefGoogle Scholar
  28. Zenil H (2013) Algorithmic complexity of animal behaviour: from communication to cognition. In: Theory and practice of natural computing second international conference proceedings, Cáceres, Spain TPNC 2013Google Scholar
  29. Zenil H, Hernandez-Quiroz F (2007) On the possible computational power of the human mind. In: C. Gershenson, D. Aerts, and B. Edmonds (eds) Worldviews, science and us: philosophy and complexity. World Scientific, Singapore, pp 315–334Google Scholar
  30. Zenil H, Gershenson C, Marshall JAR, Rosenblueth DA (2012) Life as thermodynamic evidence of algorithmic structure in natural environments. Entropy 14(11):2173–2191MathSciNetCrossRefGoogle Scholar
  31. Zenil H Marshall JAR, Tegnér J (2015a) Approximations of algorithmic and structural complexity validate cognitive-behavioural experimental results. arXiv preprint arXiv:1509.06338Google Scholar
  32. Zenil H, Soler-Toscano F, Delahaye J-P, Gauvrit N (2015b) Two-dimensional kolmogorov complexity and an empirical validation of the coding theorem method by compressibility. Peer J Comput Sci 1:e23CrossRefGoogle Scholar
  33. Zenil H, Soler-Toscano F, Kiani NA, Hernández-Orozco S, Rueda-Toicen A (2016) A decomposition method for global evaluation of Shannon entropy and local estimations of algorithmic complexity. arXiv preprint arXiv:1609.00110Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Algorithmic Dynamics Lab, Unit of Computational Medicine and SciLifeLab, Center for Molecular Medicine, Department of Medicine SolnaKarolinska InstitutetStockholmSweden
  2. 2.Human and Artificial Cognition LabEPHEParisFrance

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