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abstract: "In what follows, we introduce the notion of representational information (information conveyed by sets of dimensionally deﬁned objects about their superset of origin) as well as an\r\noriginal deterministic mathematical framework for its analysis and measurement. The framework, based in part on categorical invariance theory [Vigo, 2009], uniﬁes three key constructsof universal science – invariance, complexity, and information. From this uniﬁcation we deﬁne the amount of information that a well-deﬁned set of objects R carries about its ﬁnite superset of origin S, as the rate of change in the structural complexity of S (as determined by its degree of categorical invariance), whenever the objects in R are removed from the set S. The measure captures deterministically the signiﬁcant role that context and category structure play in determining the relative quantity and quality of subjective information conveyed by particular objects in multi-object stimuli."
altloc: []
chapter: ~
commentary: ~
commref: ~
confdates: ~
conference: ~
confloc: ~
contact_email: ~
creators_id:
- vigo@ohio.edu
creators_name:
- family: Vigo
given: Ronaldo
honourific: Professor
lineage: ''
date: 2011
date_type: published
datestamp: 2012-11-09 17:47:35
department: ~
dir: disk0/00/00/79/61
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editors_id: []
editors_name: []
eprint_status: archive
eprintid: 7961
fileinfo: application/pdf;http://cogprints.org/7961/1/Vigo_Information_Sciences.pdf
full_text_status: public
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ispublished: pub
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keywords: 'Representational Information, Concepts, Invariance, Complexity, Information measure, Subjective information'
lastmod: 2012-11-09 17:47:35
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
number: ~
pagerange: 4847-4859
pubdom: TRUE
publication: 'Information Sciences '
publisher: 'Elsevier '
refereed: TRUE
referencetext: "H.H. Aiken, The Staff of the Computation Laboratory at Harvard University, Synthesis of Electronic Computing and Control Circuits, Harvard University\r\nPress, Cambridge, 1951.\r\n[2] David Applebaum, Probability and Information, Cambridge University Press, 1996.\r\n[3] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.\r\n[4] L.E. Bourne, Human Conceptual Behavior, Allyn and Bacon, Boston, 1966.\r\n[5] N. Cowan, The magical number 4 in short-term memory: a reconsideration of mental storage capacity, Behavioral and Brain Sciences 24 (2001) 87–\r\n185.\r\n[6] K. Devlin, Claude Shannon, 1916–2001, Focus: The Newsletter of the Mathematical Association of America. 21 (2001) 20–21.\r\n[7] K. Devlin, Logic and Information, Cambridge University Press, 1991.\r\n[8] W.K. Estes, Classiﬁcation and cognition, Oxford Psychology Series, vol. 22, Oxford University Press, Oxford, 1994.\r\n[9] J. Feldman, A catalog of Boolean concepts, Journal of Mathematical Psychology 47 (1) (2003) 98–112.\r\n[10] D. Fisch, B. Kühbeck, B. Sick, S.J. Ovaska, So near and yet so far: new insight into properties of some well-known classiﬁer paradigms, Information\r\nSciences 180 (2010) 3381–3401.\r\n[11] W.R. Garner, The Processing of Information and Structure, Wiley, New York, 1974.\r\n4858 R. Vigo / Information Sciences 181 (2011) 4847–4859Author's personal copy\r\n[12] S. Guadarrama, A. Ruiz-Mayor, Approximate robotic mapping from sonar data by modeling perceptions with antonyms, Information Sciences 180\r\n(2010) 4164–4188.\r\n[13] R.V.L. Hartley, Transmission of information, Bell System Technical Journal (1928) 535–563.\r\n[14] J.P. Hayes, Introduction to Digital Logic Design, Addison-Wesley, 1993.\r\n[15] R.A. Higonnet, R.A. Grea, Logical Design of Electrical Circuits, McGraw-Hill, New York, 1958.\r\n[16] E. Horowitz, S. Sahni, Computing partitions with applications to the Knapsack problem, JACM 21 (2) (1974) 277–292.\r\n[17] J.K. Kruschke, ALCOVE: an exemplar-based connectionist model of category learning, Psychological Review 99 (1992) 22–44.\r\n[18] W. Lenski, Information: a conceptual investigation, Information 1 (2) (2010) 74–118.\r\n[19] R.D. Luce, Whatever happened to information theory in psychology?, Review of General Psychology 7 (2) (2003) 183–188\r\n[20] G.L. Murphy, The Big Book of Concepts, MIT Press, 2002.\r\n[21] R.M. Nosofsky, M.A. Gluck, T.J. Palmeri, S.C. McKinley, P.G. Glauthier, Comparing models of rule-based classiﬁcation learning: a replication and\r\nextension of Shepard, Hovland, and Jenkins (1961), Memory and Cognition 22 (3) (1994) 352–369.\r\n[22] R. Seising, Cybernetics, system(s) theory, information theory and fuzzy sets and systems in the 1950s and 1960s, Information Sciences 180 (2010)\r\n4459–4476.\r\n[23] C.E. Shannon, A mathematical theory of communication, System Technical Journal 27 (1948) 379–423. 623–656.\r\n[24] C.E. Shannon, W. Weaver, The mathematical theory of communication, University of Illinois Press, Urbana, 1949.\r\n[25] R.N. Shepard, C.L. Hovland, H.M. Jenkins, Learning and memorization of classiﬁcations, Psychological Monographs: General and Applied 75 (13) (1961)\r\n1–42.\r\n[26] B. Skyrms, The ﬂow of information in signaling games, Philosophical Studies 147 (2010) 155–165.\r\n[27] F.S. Tseng, Y. Kuo, Y. Huang, Toward boosting distributed association rule mining by data de-clustering, Information Sciences 80 (2010) 4459–4476.\r\n[28] R. Vigo, A note on the complexity of Boolean concepts, Journal of Mathematical Psychology 50 (5) (2006) 501–510.\r\n[29] R. Vigo, Modal similarity, Journal of Experimental and Artiﬁcial Intelligence 21 (3) (2009) 181–196.\r\n[30] R. Vigo, Categorical invariance and structural complexity in human concept learning, Journal of Mathematical Psychology 53 (4) (2009) 203–221.\r\n[31] R. Vigo, A dialogue on concepts, Think 9 (24) (2010) 109–120.\r\n[32] R. Vigo, Towards a law of invariance in human conceptual behavior, in: L. Carlson, C. Hölscher, T. Shipley (Eds.), Proceedings of the 33rd Annual\r\nConference of the Cognitive Science Society, Austin, TX: Cognitive Science Society (in press)."
relation_type: []
relation_uri: []
reportno: ~
rev_number: 13
series: ~
source: ~
status_changed: 2012-01-24 18:06:09
subjects:
- appl-cog-psy
- comp-sci-art-intel
- comp-sci-complex-theory
- comp-sci-robot
- percep-cog-psy
- psy-phys
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: "Representational information: a new general notion and measure\r\nof information"
type: journalp
userid: 15877
volume: 181