--- abstract: "What is information? Although researchers have used the construct of information liberally to refer to pertinent forms of domain-specific knowledge, relatively few have attempted to generalize and standardize the construct. Shannon and Weaver(1949)offered the best known attempt at a quantitative generalization in terms of the number of discriminable symbols required to communicate the state of an uncertain event. This idea, although useful, does not capture the role that structural context and complexity play in the process of understanding an event as being informative. In what follows, we discuss the limitations and futility of any generalization (and particularly, Shannon’s) that is not based on the way that agents extract patterns from their environment. More specifically, we shall argue that agent concept acquisition, and not the communication of\r\nstates of uncertainty, lie at the heart of generalized information, and that the best way of characterizing information is via the relative gain or loss in concept complexity that is experienced when a set of known entities (regardless of their nature or domain of origin) changes. We show that Representational Information Theory perfectly captures this crucial aspect of information and conclude with the first generalization of Representational Information Theory (RIT) to continuous domains." altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: ~ conference: ~ confloc: ~ contact_email: ~ creators_id: - vigo@ohio.edu creators_name: - family: Vigo given: Ronaldo honourific: Professor lineage: '' date: 2013-01 date_type: published datestamp: 2012-12-27 14:54:11 department: ~ dir: disk0/00/00/87/84 edit_lock_since: ~ edit_lock_until: 0 edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 8784 fileinfo: /8784/1.hassmallThumbnailVersion/Vigo%20%282013%29.pdf;/8784/1/Vigo%20%282013%29.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: ~ item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: information theory; representational information; categorization; concepts; invariance; complexity; information measure; subjective information lastmod: 2013-02-18 15:08:53 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: 1 pagerange: 1-30 pubdom: FALSE publication: 'Information ' publisher: MDPI refereed: TRUE referencetext: "1. Devlin, K. Logic and Information; Cambridge University Press: Cambridge, UK, 1991.\r\n2. Luce, R.D. Whatever happened to information theory in psychology? Rev. Gen. Psychol. 2003, 7,\r\n183–188.\r\n3. Floridi, L. The Philosophy of Information; Oxford University Press: Oxford, UK, 2011.\r\n4. Devlin, K. Claude Shannon, 1916–2001. Focus News. Math. Assoc. Am. 2001, 21, 20–21.\r\n5. Hartley, R.V.L. Transmission of information. Bell Syst. Tech. J. 1928, 7, 535–563.\r\n6. Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J. 1948, 27, 379–423.\r\n7. Shannon, C.E.; Weaver, W. The Mathematical Theory of Communication; University of Illinois\r\nPress: Urbana, IL, USA, 1949.\r\n8. Bishop, C.M. Pattern Recognition and Machine Learning; Springer: New York, NY, USA, 2006.\r\n9. Vigo, R. Representational information: A new general notion and measure of information. Inf. Sci.\r\n2011, 181, 4847–4859.\r\n10. Klir, G.J. Uncertainty and Information: Foundations of Generalized Information Theory; John\r\nWiley & Sons, Inc.: Hoboken, NJ, USA, 2006.\r\n11. Miller, G.A. The magical number seven, plus or minus two: Some limits on our capacity for\r\nprocessing information. Psychol. Rev. 1956, 63, 81–97.\r\n12. Laming, D.R.J. Information Theory of Choice-Reaction Times; Academic Press: New York, NY,\r\nUSA, 1968.\r\n13. Deweese, M.R.; Meister, M. How to measure the information gained from one symbol. Network\r\n1999, 10, 325–340.\r\n14. Butts, D.A. How much information is associated with a particular stimulus? Network 2003, 14,\r\n177–187.\r\n15. Laming, D. Statistical information, uncertainty, and Bayes’ theorem: Some applications in\r\nexperimental psychology. In Symbolic and Quantitative Approaches to Reasoning with\r\nUncertainty; Benferhat, S., Besnard, P., Eds.; Springer-Verlag: Berlin, Germany, 2001; pp. 635–646.\r\n16. Dretske, F. Knowledge and the Flow of Information; MIT Press: Cambridge, MA, USA, 1981.\r\n17. Tversky, A; Kahneman, D. Availability: A heuristic for judging frequency and probability. Cogn.\r\nPsychol. 1973, 5, 207–233.\r\n18. Tversky, A.; Kahneman, D. Extension versus intuitive reasoning: The conjunction fallacy in\r\nprobability judgment. Psychol. Rev. 1983, 90, 293–315.\r\n19. Vigo, R. A dialogue on concepts. Think 2010, 9, 109–120.\r\nInformation 2013, 4\r\n29\r\n20. Vigo, R. Categorical invariance and structural complexity in human concept learning. J. Math.\r\nPsychol. 2009, 53, 203–221.\r\n21. Vigo, R. Towards a law of invariance in human conceptual behavior. In Proceedings of the 33rd\r\nAnnual Conference of the Cognitive Science Society, Austin, TX, USA, 21 July 2011; Carlson, L.,\r\nHölscher, C., Shipley, T., Eds.; Cognitive Science Society: Austin, TX, USA, 2011.\r\n22. Vigo, R. The gist of concepts. Cognition 2012, Submitted for publication.\r\n23. Feldman, D.; Crutchfield, J. A survey of “Complexity Measures”; Santa Fe Institute: Santa Fe,\r\nNM, USA, 11 June 1998; pp. 1–15.\r\n24. Vigo, R. A note on the complexity of Boolean concepts. J. Math. Psychol. 2006, 50, 501–510.\r\n25. Vigo, R. Modal similarity. J. Exp. Artif. Intell. 2009, 21, 181–196.\r\n26. Vigo, R.; Basawaraj, B. Will the most informative object stand? Determining the impact of\r\nstructural context on informativeness judgments. J. Cogn. Psychol. 2012, in press.\r\n27. Vigo, R.; Zeigler, D.; Halsey, A. Gaze and informativeness during category learning: Evidence\r\nfor an inverse relation. Vis. Cogn. 2012, Submitted for publication.\r\n28. Bourne, L.E. Human Conceptual Behavior; Allyn and Bacon: Boston, MA, USA, 1966.\r\n29. Estes, W.K. Classification and Cognition; Oxford Psychology Series 22; Oxford University Press:\r\nOxford, UK, 1994.\r\n30. Garner, W.R. The Processing of Information and Structure; Wiley: New York, NY, USA, 1974.\r\n31. Garner, W.R. Uncertainty and Structure as Psychological Concepts; Wiley: New York, NY,\r\nUSA, 1962.\r\n32. Kruschke, J.K. ALCOVE: An exemplar-based connectionist model of category learning. Psychol.\r\nRev. 1992, 99, 22–44.\r\n33. Aiken, H.H. The staff of the Computation Laboratory at Harvard University. In Synthesis of\r\nElectronic Computing and Control Circuits; Harvard University Press: Cambridge, UK, 1951.\r\n34. Higonnet, R.A.; Grea, R.A. Logical Design of Electrical Circuits; McGraw-Hill: New York, NY,\r\nUSA, 1958." relation_type: [] relation_uri: [] reportno: ~ rev_number: 13 series: ~ source: ~ status_changed: 2012-12-27 14:54:11 subjects: - appl-cog-psy - cog-psy - comp-sci-art-intel - comp-sci-complex-theory - percep-cog-psy succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: "Complexity over Uncertainty in Generalized Representational\r\nInformation Theory (GRIT): A Structure-Sensitive General\r\nTheory of Information" type: journalp userid: 15877 volume: 4