Skip to main content
SpringerLink
Log in

Cognitive Adequacy in Brain-Like Intelligence

  • Chapter
Creating Brain-Like Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5436))

Abstract

A variety of disciplines have dealt with the design of intelligent algorithms – among them Artificial Intelligence and Robotics. While some approaches were very successful and have yielded promising results, others have failed to do so which was — at least partly — due to inadequate architectures and algorithms that were not suited to mimic the behavior of biological intelligence. Therefore, in recent years, a quest for ”brain-like” intelligence has arosen. Soft- and hardware are supposed to behave like biological brains — ideally like the human brain. This raises the questions of what exactly defines the attribute ”brain-like”, how can the attribute be implemented and how tested. This chapter suggests the concept of cognitive adequacy in order to get a rough estimate of how ”brain-like” an algorithm behaves.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Russell, S., Norvig, P.: Artificial Intelligence: A modern approach. Prentice-Hall, Englewood Cliffs (1995)

    Google Scholar 

  2. Crevier, D.: AI: The Tumultuous Search for Artificial Intelligence. Basic Books (1993)

    Google Scholar 

  3. Dreyfus, H.: What Computers Can’t Do: The Limits of Artificial Intelligence. Harpercollins (1978)

    Google Scholar 

  4. Rumelhart, D.E., McClelland, J. (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, Cambridge (1987)

    Google Scholar 

  5. Fuster, J.M.: Prefrontal cortex and the bridging of temporal gaps in the perception-action cycle. Ann. N. Y. Acad. Sci. 608, 318–329 (1990)

    Article  CAS  PubMed  Google Scholar 

  6. Merleau–Ponty, M.: The structure of behavior. Beacon (1942)

    Google Scholar 

  7. Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds.): Embodied artificial intelligence. LNCS, vol. 3139. Springer, Heidelberg (2004)

    Google Scholar 

  8. Squire, L.R., Kandel, E.R.: Memory. From mind to molecules. Scientific American Library (1999)

    Google Scholar 

  9. Ohl, F.W., Scheich, H., Freeman, W.J.: Change in pattern of ongoing cortical activity with auditory category learning. Nature 412(6848), 733–736 (2001)

    Article  CAS  PubMed  Google Scholar 

  10. Parasuraman, R.: The attentive brain. MIT Press, Cambridge (1998)

    Google Scholar 

  11. Minsky, M.L.: The emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind. Simon and Schuster (2006)

    Google Scholar 

  12. Bothe, H.W., Samii, M., Eckmiller, R. (eds.): Neurobionics: An Interdisciplinary Approach to Substitute Impaired Functions of the Human Nervous System. Elsevier, Amsterdam (1993)

    Google Scholar 

  13. Ohl, F.W., Scheich, H.: Chips in your head. Scientific American Mind, 64–69 (April/May 2007)

    Google Scholar 

  14. Abbott, A.: Neuroprosthetics: in search of the sixth sense. Nature 442(7099), 125–127 (2006)

    Article  CAS  PubMed  Google Scholar 

  15. Shastri, L., Ajjanagadde, V.: From associations to systematic reasoning: A connectionist representation of rules, variables, and dynamic bindings using temporal synchrony. Behavioural Brain Sciences 16, 417–494 (1993)

    Article  Google Scholar 

  16. Johnson-Laird, P.N., Byrne, R.M.J.: Deduction. Lawrence Erlbaum Ass., Mahwah (1991)

    Google Scholar 

  17. Bibel, W.: Perspectives on automated deduction. In: Automated reasoning: Essays in the honor of Woody Bledsoe, pp. 77–104. Kluwer Academic, Dordrecht (1991)

    Chapter  Google Scholar 

  18. Hölldobler, S., Thielscher, M.: On the adequateness of AI-systems. In: Plander, I. (ed.) International Conference on AIICSR (1994)

    Google Scholar 

  19. Beringer, A., Hölldobler, S.: On the adequateness of the connection method. In: AAAI National Conference on Artificial Intelligence (1993)

    Google Scholar 

  20. Herrmann, C.S., Reine, F.: Considering adequacy in neural network learning. In: Proceedings of ICNN 1996 (IEEE International Conference on Neural Networks) (1996)

    Google Scholar 

  21. Posner, M.I.: Chronometric explorations of mind. Oxford University Press, Oxford (1986)

    Google Scholar 

  22. Gazzaniga, M.S.: Cognitive neuroscience. The biology of the mind. Norton (2002)

    Google Scholar 

  23. Anderson, J.R.: Rules of the mind. Lawrence Earlbaum Associates (1993)

    Google Scholar 

  24. Luce, R.D.: Response times. Oxford Science Publications (1986)

    Google Scholar 

  25. Herrmann, C.S., Mecklinger, A.: Magnetoencephalographic responses to illusory figures: early evoked gamma is affected by processing of stimulus features. Int. J. Psychophysiol. 38(3), 265–281 (2000)

    Article  CAS  PubMed  Google Scholar 

  26. Herrmann, C.S., Mecklinger, A.: Gamma activity in human EEG is related to high-speed memory comparisons during object selective attention. Visual Cognition 8(3/4/5), 593–608 (2001)

    Article  Google Scholar 

  27. Ohl, F.W., Scheich, H.: Learning-induced plasticity in the auditory cortex. Current Opinion in Neurobiology 15, 470–477 (2005)

    Article  CAS  PubMed  Google Scholar 

  28. Stroop, J.R.: Studies of interference in serial verbal reactions. Journal of Experimental Psychology 18, 643–662 (1935)

    Article  Google Scholar 

  29. Senkowski, D., Herrmann, C.S.: Effects of task difficulty on evoked gamma activity and erps in a visual discrimination task. Clin. Neurophysiol. 113(11), 1742–1753 (2002)

    Article  PubMed  Google Scholar 

  30. Kraft, J.M., Brainard, D.H.: Mechanisms of color constancy under nearly natural viewing. Proc. Natl. Acad. Sci. USA 96(1), 307–312 (1999)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Block, J.R., Yuker, H.E.: Can you believe your eyes? Over 250 illusions and other visual oddities. Tylor and Francis (1989)

    Google Scholar 

  32. Strüber, D., Herrmann, C.S.: Meg alpha activity decrease reflects destabilization of multistable percepts. Brain Res. Cogn. Brain Res. 14(3), 370–382 (2002)

    Article  PubMed  Google Scholar 

  33. Freeman, W.J.: Neurodynamics. Springer, Heidelberg (2000)

    Google Scholar 

  34. Haken, H.: Synergetic Computers and Cognition. A top-down approach to neural nets. Springer, Heidelberg (2004)

    Book  Google Scholar 

  35. Fürstenau, N.: A chaotic attractor model of cognitive multistability. IEEE International Conference on Systems, Man, and Cybernetics 1, 853–859 (2004)

    Google Scholar 

  36. Wilson, H.R.: Spikes, decisions, and actions. Oxford University Press, Oxford (1999)

    Google Scholar 

  37. Searle, J.R.: Minds, Brains and Science. Hardvard University Press (1984)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Psychology Biological Psychology Lab, Otto-von-Guericke-University Magdeburg, PO box 4120, 39016, Magdeburg, Germany

    Christoph S. Herrmann

  2. Leibniz Institute for Neurobiology, Brenneckestrasse 6, 39118, Magdeburg, Germany

    Frank W. Ohl

Authors
  1. Christoph S. Herrmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank W. Ohl
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Honda Research Institute Europe GmbH, 63073 Offenbach/Main, Germany

    Bernhard Sendhoff

  2. Honda Research Institute Europe GmbH, Carl-Legien-Strasse 30, 63073, Offenbach/Main, Germany

    Edgar Körner

  3. Dept. of Psychological and Brain Sciences, Indiana University, IN 47405, Bloomington, USA

    Olaf Sporns

  4. Faculty of Technology, Neuroinformatics Group, Bielefeld University, Universitätsstr. 25, 33615, Bielefeld, Germany

    Helge Ritter

  5. Okinawa Institute of Science and Technology, Neural Computation Unit,, 12-22 Suzaki, Uruma, 904-2234, Okinawa, Japan

    Kenji Doya

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Herrmann, C.S., Ohl, F.W. (2009). Cognitive Adequacy in Brain-Like Intelligence. In: Sendhoff, B., Körner, E., Sporns, O., Ritter, H., Doya, K. (eds) Creating Brain-Like Intelligence. Lecture Notes in Computer Science(), vol 5436. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00616-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-00616-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00615-9

  • Online ISBN: 978-3-642-00616-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation