Skip to main content

Advertisement

Log in

Higher-Level Cognition and Computation: A Survey

  • Technical Contribution
  • Published:
KI - Künstliche Intelligenz Aims and scope Submit manuscript

Abstract

Higher-level cognition is one of the constituents of our human mental abilities and subsumes reasoning, planning, language understanding and processing, and problem solving. A deeper understanding can lead to core insights to human cognition and to improve cognitive systems. There is, however, so far no unique characterization of the processes of human cognition. This survey introduces different approaches from cognitive architectures, artificial neural networks, and Bayesian modeling from a modeling perspective to vibrant fields such as connecting neurobiological processes with computational processes of reasoning, frameworks of rationality, and non-monotonic logics and common-sense reasoning. The survey ends with a set of five core challenges and open questions relevant for future research.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. http://www.act-r.psy.cmu.edu

  2. http://www.sitemaker.umich.edu/soar

  3. http://www.cogsci.rpi.edu/ rsun/clarion.html

  4. http://www.wikipedia.org

  5. http://www.sfbtr8.spatial-cognition.de

  6. http://www.spp1516.de

  7. http://www.humanbrainproject.eu/de

  8. http://www.nih.gov/news/health/sep2013/od-16.html

  9. http://www.brainlinks-braintools.uni-freiburg.de

References

  1. Anderson JR (1983) The Architecture of Cognition. Harvard University Press, Cambridge

    Google Scholar 

  2. Anderson JR (2007) How can the human mind occur in the physical universe?. Oxford University Press, New York

    Book  Google Scholar 

  3. Ariely D (2009) Predictably Irrational. The Hidden Forces That Shape Our Decisions. Harper Collins, Revised and Expanded Edition

  4. Besold T, Hernández-Orallo J, Schmid U (2015) Can machine intelligence be measured in the same way as human intelligence? KI - Künstliche Intelligenz

  5. Brewka G, Polberg S, Woltran S (2014) Generalizations of Dung frameworks and their role in formal argumentation. IEEE Intell Syst 29(1):30–38

    Article  Google Scholar 

  6. Bringsjord S (2011) Psychometric artificial intelligence. J Exp Theor Artif Intell 23(3):271–277

    Article  Google Scholar 

  7. Bringsjord S, Schimanski B (2003) What is Artificial Intelligence? Psychometric AI as an Answer. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI’03). Morgan Kaufmann, pp 887–893

  8. Chater N, Tenenbaum JB, Yuille A (2006) Probabilistic models of cognition: conceptual foundations. Trends Cognitive Sci 10(7):287–291

    Article  Google Scholar 

  9. Chesñevar CI, Maguitman AG, Loui RP (2000) Logical models of argument. ACM Comput Surv 32:337–383

    Article  Google Scholar 

  10. Dietz EA, Hölldobler S, Ragni M (2012) A Computational Approach to the Suppression Task. In: Miyake N, Peebles D, Cooper R (eds) Proceedings of the 34th Annual Conference of the Cognitive Science Society. Cognitive Science Society, Austin, pp 1500–1505

    Google Scholar 

  11. Dietz EA, Hölldobler S, Ragni M (2012) A computational logic approach to the suppression task. In: Proceedings of the 34th Cognitive Science Conference

  12. Dietz EA, Hölldobler S, Ragni M (2013) A Computational Logic Approach to the Abstract and the Social Case of the Selection Task. In: Morgenstern L, Davis E, Williams MA (eds) 11th International Symposium on Logical Formalizations of Commonsense Resaoning

  13. Dung PM, Kowalski RA, Toni F (2006) Dialectic proof procedures for assumption-based, admissible argumentation. Artif Intell 170(2):114–159

    Article  MathSciNet  MATH  Google Scholar 

  14. Eliasmith C (2013) How to build a brain: a neural architecture for biological cognition. Oxford University Press

  15. Funke J (2010) Complex problem solving: a case for complex cognition? Cognitive Process 11:133–142

    Article  Google Scholar 

  16. Furbach U, Schon C, Stolzenburg F (2015) Cognitive systems and question answering. Industrie 4.0. Management 31(1):29–32

    Google Scholar 

  17. Griffiths TL, Tenenbaum JB (2005) Structure and strength in causal induction. Cognitive Psychol 51:354–384

    Article  Google Scholar 

  18. Griffiths TL, Tenenbaum JB (2007) From mere coincidences to meaningful discoveries. Cognition 103(2):180–226

    Article  Google Scholar 

  19. Hegarty M (2004) Mechanical reasoning by mental simulation. Trends Cognitive Sci 8(6):280–285

    Article  Google Scholar 

  20. Hernández-Orallo J, Dowe DL, Hernández-Lloreda MV (2014) Universal psychometrics: measuring cognitive abilities in the machine kingdom. Cognitive Syst Res 27:50–74

    Article  Google Scholar 

  21. Just MA, Carpenter PA, Varma S (1999) Computational modeling of high-level cognition and brain function. Human Brain Mapp 8:128–136

    Article  Google Scholar 

  22. Kahneman D (2011) Thinking. Fast and Slow. Farrar, Straus and Giroux

  23. Khemlani S, Johnson-Laird PN (2012) Theories of the syllogism: a meta-analysis. Psychological Bulletin

  24. Khemlani S, Mackiewicz R, Bucciarelli M, Johnson-Laird P (2013) Kinematic mental simulations in abduction and deduction. Proc Natl Acad Sci 110(42):16766–16771

    Article  Google Scholar 

  25. Knauff M (2013) Space to reason: a spatial theory of human thought. MIT Press

  26. Knauff M, Wolf AG (2010) Complex cognition: the science of human reasoning, problem-solving, and decision-making. Cognitive Process 11(2):99–102

    Article  Google Scholar 

  27. Laird JE (2012) The Soar Cognitive Architecture. MIT Press

  28. Marr D (1982) Vision: a computational investigation into the human representation and processing of visual information. Freeman, New York

    Google Scholar 

  29. Matuszek C, Cabral J, Witbrock MJ, DeOliveira J (2006) An introduction to the syntax and content of Cyc. In: AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering. Citeseer, pp 44–49

  30. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  31. Mueller ET (2014) Commonsense Reasoning, 2nd edn. Morgan Kaufmann, San Francisco

    Google Scholar 

  32. Newell A (1990) Unified theories of cognition. Harvard University Press, Cambridge

    Google Scholar 

  33. Newell A, Simon HA (1972) Human problem solving. Prentice-Hall

  34. Nute D (2003) Defeasible logic. In: Bartenstein O, Geske U, Hannebauer M, Yoshie O (eds) Web Knowledge Management and Decision Support, lecture Notes in Computer Science, vol. 2543. Springer, Berlin, Heidelberg, pp 151–169

  35. Oaksford M, Chater N (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press, USA

    Book  Google Scholar 

  36. Ragni M, Klein A (2011) Predicting numbers: an AI approach to solving number series. In: Edelkamp S, Bach J (eds) Proceedings of the 34th German Conference on Artificial Intelligence (KI-2011), LNCS. Springer (2011)

  37. Ragni M, Klein A (2011) Solving Number Series—architectural properties of successful artificial neural networks. In: Madani K, Kacprzyk J, Filipe J (eds) NCTA 2011—Proceedings of the International Conference on Neural Computation Theory and Applications. SciTePress, pp 224–229

  38. Ragni M, Steffenhagen F, Klein A (2011) Generalized dynamic stock and flow systems: an AI approach. Cognitive Syst Res 12(3–4):309–320

    Article  Google Scholar 

  39. Robinson A, Voronkov A (eds) (2001) Handbook of Automated Reasoning. North-Holland, Amsterdam

    MATH  Google Scholar 

  40. Schmid U, Kitzelmann E (2011) Inductive rule learning on the knowledge level. Cognitive Syst Res 12(3):237–248

    Article  Google Scholar 

  41. Simon H, Wallach D (1999) Cognitive modeling in perspective. Kognitionswissenschaft 8:1–4

    Article  Google Scholar 

  42. Spohn W (2012) The laws of belief: Ranking theory and its philosophical applications. Oxford University Press

  43. Stenning K, Lambalgen M (2008) Human reasoning and cognitive science. Bradford Books. MIT Press, Cambridge

    Google Scholar 

  44. Steyvers M, Tenenbaum JB, Wagenmakers EJ, Blum B (2003) Inferring causal networks from observations and interventions. Cognitive Sci 27(3):453–489

    Article  Google Scholar 

  45. Störing G (1908) Experimentelle Untersuchungen über einfache Schlussprozesse. W. Engelmann

  46. Strube G (2000) Generative theories in cognitive psychology. Theory Psychol 10(1):117–125

    Article  Google Scholar 

  47. Strube G, Ferstl E, Konieczny L, Ragni M (2013) Kognition. In: Görz G, Schneeberger J, Schmid U (eds) Handbuch der Künstlichen Intelligenz. Oldenbourg, München

  48. Suchanek FM, Kasneci G, Weikum G (2008) Yago: A large ontology from Wikipedia and WordNet. Web Semant Sci Serv Agents World Wide Web 6(3):203–217

    Article  Google Scholar 

  49. Sun R (2001) Duality of the mind—a bottom-up approach toward cognition. Lawrence Erlbaum

  50. Tenenbaum JB, Griffiths TL, Kemp C (2006) Theory-based Bayesian models of inductive learning and reasoning. Trends Cognitive Sci 10:309–318

    Article  Google Scholar 

  51. Tenenbaum JB, Kemp C, Griffiths TL, Goodman ND (2011) How to Grow a Mind: Statistics, Structure, and Abstraction. Science 331(6022):1279–1285

    Article  MathSciNet  MATH  Google Scholar 

  52. Wang P (2013) Non-Axiomatic Logic: a model of intelligent reasoning. World Scientific Publishing Co

  53. Wason PC (1968) Reasoning about a rule. Quart J Exp Psychol 20(3):273–281

    Article  Google Scholar 

  54. Xu F, Tenenbaum JB (2007) Word learning as Bayesian inference. Psychol Rev 114(2):245–272

    Article  Google Scholar 

Download references

Acknowledgments

This work has been partially supported by a Heisenberg scholarship to the first author under Grant No. RA 1934/3-1 and within a project in the DFG-SPP New Frameworks of Rationality under Grant No. RA 1934/2-1.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Frieder Stolzenburg.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ragni, M., Stolzenburg, F. Higher-Level Cognition and Computation: A Survey. Künstl Intell 29, 247–253 (2015). https://doi.org/10.1007/s13218-015-0375-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13218-015-0375-y

Keywords

Navigation