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Confabulation (neural networks)

A confabulation, also known as a false, degraded, or corrupted memory, is a stable pattern of activation in an artificial neural network or neural assembly that does not correspond to any previously learned patterns. The same term is also applied to the (nonartificial) neural mistake-making process leading to a false memory (confabulation).

Cognitive science

In cognitive science, the generation of confabulatory patterns is symptomatic of some forms of brain trauma.[1] In this, confabulations relate to pathologically induced neural activation patterns depart from direct experience and learned relationships. In computational modeling of such damage, related brain pathologies such as dyslexia and hallucination result from simulated lesioning[2] and neuron death.[3] Forms of confabulation in which missing or incomplete information is incorrectly filled in by the brain are generally modelled by the well known neural network process called pattern completion.[4]

Neural networks

See also: Hallucination (artificial intelligence)

Confabulation is central to a theory of cognition and consciousness by S. L. Thaler in which thoughts and ideas originate in both biological and synthetic neural networks as false or degraded memories nucleate upon various forms of neuronal and synaptic fluctuations and damage.[5][6] Such novel patterns of neural activation are promoted to ideas as other neural nets perceive utility or value to them (i.e., the thalamo-cortical loop).[7][8] The exploitation of these false memories by other artificial neural networks forms the basis of inventive artificial intelligence systems currently utilized in product design,[9][10] materials discovery[11] and improvisational military robots.[12] Compound, confabulatory systems of this kind[13] have been used as sensemaking systems for military intelligence and planning,[12] self-organizing control systems for robots and space vehicles,[14] and entertainment.[12] The concept of such opportunistic confabulation grew out of experiments with artificial neural networks that simulated brain cell apoptosis.[15] It was discovered that novel perception, ideation, and motor planning could arise from either reversible or irreversible neurobiological damage.[16][17]

Large language models

In March 2023, technology journalist Benj Edwards proposed "confabulation" as a more accurate alternative to "hallucination" for describing factual errors generated by large language models (LLMs) like those used with ChatGPT.[18][19][20] Edwards argued that in the context of LLMs, "confabulation" better captures the "creative gap-filling principle" at work when these models generate plausible-sounding but factually incorrect information without implying deception.[citation needed]

Unlike the term "hallucination," which suggests perceiving something that isn't there, "confabulation" describes how LLMs fill in missing information with fabricated content that appears coherent and convincing. As Edwards noted, "In human psychology, a 'confabulation' occurs when someone's memory has a gap and the brain convincingly fills in the rest without intending to deceive others." While LLMs don't function like human brains, this metaphor helps explain how these models produce false information that appears credible within their generated text.[citation needed]

The term has since gained traction in technical literature,[21] and also in clinical discourse[22] where researchers argue it more accurately describes how LLMs generate plausible but incorrect information without implying sensory perception or consciousness.

Computational inductive reasoning

The term confabulation is also used by Robert Hecht-Nielsen in describing inductive reasoning accomplished via Bayesian networks.[23] Confabulation is used to select the expectancy of the concept that follows a particular context. This is not an Aristotelian deductive process, although it yields simple deduction when memory only holds unique events. However, most events and concepts occur in multiple, conflicting contexts and so confabulation yields a consensus of an expected event that may only be minimally more likely than many other events. However, given the winner take all constraint of the theory, that is the event/symbol/concept/attribute that is then expected. This parallel computation on many contexts is postulated to occur in less than a tenth of a second. Confabulation grew out of vector analysis of data retrieval like that of latent semantic analysis and support vector machines. It is being implemented computationally on parallel computers.[citation needed]

References

  1. ^ Gazzaniga, Michael S., ed. (1996). Conversations in the Cognitive Neurosciences. doi:10.7551/mitpress/2162.001.0001. ISBN 978-0-262-28689-3. OCLC 42328595.[page needed]
  2. ^ Plaut, David C.; Shallice, Tim (November 1993). "Deep dyslexia: A case study of connectionist neuropsychology". Cognitive Neuropsychology. 10 (5): 377–500. doi:10.1080/02643299308253469. OCLC 4645580590.
  3. ^ Yam, Philip (May 1993). "Daisy, Daisy". Scientific American. 268 (5): 32–33. doi:10.1038/scientificamerican0593-32.
  4. ^ "Neural associative memory". rni.org. Archived from the original on 2008-10-20. Retrieved 2009-07-22.[self-published source?]
  5. ^ US granted 5659666A, Thaler, Stephen L, "Device for the autonomous generation of useful information", issued 19 August 1997 
  6. ^ Thaler, S. L. (1997b). "A Quantitative Model of Seminal Cognition: the creativity machine paradigm", Proceedings of the Mind II Conference, Dublin, Ireland, 1997.
  7. ^ Thaler, Stephen L. (2012). "The creativity machine paradigm: Withstanding the argument from consciousness" (PDF). APA Newsletters: APA Newsletter on Philosophy and Computers. 11 (2): 19–30.
  8. ^ Thaler, Stephen (2013). "Creativity Machine® Paradigm". Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship. pp. 447–456. doi:10.1007/978-1-4614-3858-8_396. ISBN 978-1-4614-3857-1.
  9. ^ Pickover, Clifford A. (2005). Sex, Drugs, Einstein, and Elves: Sushi, Psychedelics, Parallel Universes, and the Quest for Transcendence. Smart Publications. ISBN 978-1-890572-17-4.[page needed]
  10. ^ Plotkin, Robert (2009). The Genie in the Machine: How Computer-Automated Inventing Is Revolutionizing Law and Business. Stanford University Press. ISBN 978-0-8047-5699-0.[page needed]
  11. ^ Thaler, Stephen L. (September 1998). "Predicting ultra-hard binary compounds via cascaded auto- and hetero-associative neural networks". Journal of Alloys and Compounds. 279 (1): 47–59. doi:10.1016/S0925-8388(98)00611-2.
  12. ^ a b c Hesman, Tina (25 January 2004). "The Machine That Invents". St. Louis Post-Dispatch. p. A.1. ProQuest 402356128.
  13. ^ Thaler, S. L. (1996). "A Proposed Symbolism for Network-Implemented Discovery Processes". WCNN'96, San Diego, California, U.S.A.: World Congress on Neural Networks : International Neural Network Society 1996 Annual Meeting : The Town & Country Hotel San Diego, California, U.S.A., September 15-18, 1996. Psychology Press. pp. 1265–1268. ISBN 978-0-8058-2608-1.
  14. ^ Patrick, M. Clinton; Thaler, Stephen L.; Stevenson-Chavis, Katherine (2007). "Demonstration of Self-Training Autonomous Neural Networks in Space Vehicle Docking Simulations". 2007 IEEE Aerospace Conference. pp. 1–6. doi:10.1109/AERO.2007.352649. ISBN 978-1-4244-0524-4.
  15. ^ Yam, Philip (May 1995). "As they Lay Dying". Scientific American. 272 (5): 24–25. Bibcode:1995SciAm.272e..24Y. doi:10.1038/scientificamerican0595-24b.
  16. ^ Thaler, S. L. (Spring 1995). "Death of a gedanken creature". Journal of Near-Death Studies. 13 (3): 149–166. OCLC 197953879.
  17. ^ Thaler, Stephen (2020). "Creativity Machine® Paradigm". Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship. pp. 650–658. doi:10.1007/978-3-319-15347-6_396. ISBN 978-3-319-15346-9.
  18. ^ Edwards, Benj (6 April 2023). "Why ChatGPT and Bing Chat are so good at making things up". Ars Technica.
  19. ^ Hicks, Michael Townsen; Humphries, James; Slater, Joe (June 2024). "ChatGPT is bullshit". Ethics and Information Technology. 26 (2). doi:10.1007/s10676-024-09775-5.
  20. ^ Liu, Kevin; Casper, Stephen; Hadfield-Menell, Dylan; Andreas, Jacob (2023). "Cognitive Dissonance: Why do Language Model Outputs Disagree with Internal Representations of Truthfulness?". Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 4791–4797. arXiv:2312.03729. doi:10.18653/v1/2023.emnlp-main.291.
  21. ^ Sui, Peiqi; Duede, Eamon; Wu, Sophie; So, Richard (2024). "Confabulation: The Surprising Value of Large Language Model Hallucinations". Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 14274–14284. arXiv:2406.04175. doi:10.18653/v1/2024.acl-long.770.
  22. ^ Smith, Andrew L.; Greaves, Felix; Panch, Trishan (November 2023). "Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models". PLOS Digital Health. 2 (11): e0000388. doi:10.1371/journal.pdig.0000388. PMC 10619792. PMID 37910473.
  23. ^ Hecht-Nielsen, Robert (March 2005). "Cogent confabulation". Neural Networks. 18 (2): 111–115. doi:10.1016/j.neunet.2004.11.003. PMID 15795109.