Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-28T03:03:59.370Z Has data issue: false hasContentIssue false
  • English
  • Français

Visuo: A model of visuospatial instantiation of quantitative magnitudes

Published online by Cambridge University Press:  24 July 2013

Jonathan Gagné  and
Jim Davies
Jonathan Gagné
Affiliation:
Systems Design Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1; e-mail: [email protected]
Jim Davies
Affiliation:
Institute of Cognitive Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada K1S 5B6; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Visuo is an implemented Python program that models visual reasoning. It takes as input a description of a scene in words (e.g. ‘small dog on a sunny street’) and produces estimates of the quantitative magnitudes of the qualitative input (e.g. the size of the dog and the brightness of the street). We claim that reasoners transfer quantitative knowledge to new concepts from distributions of familiar concepts in memory. We also claim that visuospatial magnitudes should be stored as distributions over fuzzy sets. We show that Visuo successfully predicts quantitative knowledge to new concepts.

Type
Articles
Information
The Knowledge Engineering Review , Volume 28 , Special Issue 3: Visualization, Visual Representation and Reasoning , September 2013 , pp. 347 - 366
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bird, S. G., Loper, E. 2004. NLTK: The Natural Language Toolkit. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics (Demonstration Track), Barcelona, Spain, 214–217.Google Scholar
Daugman, J. P. 1985. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A 2, 1160–1169.CrossRefGoogle ScholarPubMed
Dehaene, S., Izard, V., Spelke, E., Pica, P. 2008. Log or linear? Distinct intuitions of the number scale in Western and Amazonian Indigene cultures. Science 320(5880), 1217–1220.CrossRefGoogle ScholarPubMed
Dubois, D., Prade, H. 1987. Fuzzy numbers: an overview. In Analysis of Fuzzy Information Vol. I: Mathematics and Logic, Bezdek, J. C. (ed.). CRC Press.Google Scholar
Fellbaum, C. (ed.) 1998. WordNet: An Electronic Lexical Database. MIT Press.CrossRefGoogle Scholar
Hampton, J. A. 2007. Typicality, graded membership, and vagueness. Cognitive Science 31(3), 355–384.CrossRefGoogle ScholarPubMed
Hubel, D. A., Wiesel, T. N. 1965. Receptive fields and functional architecture in two non-striate visual areas (18 and 19) of the cat. Journal of Neurophysiology 28, 229–289.CrossRefGoogle Scholar
Jones, J. P., Palmer, L. A. 1987. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1233–1258.CrossRefGoogle ScholarPubMed
Kreiman, G., Koch, C., Fried, I. 2000a. Category-specific visual responses of single neurons in the human medial temporal lobe. Nature Neuroscience 3, 946–953.CrossRefGoogle ScholarPubMed
Kreiman, G., Koch, C., Fried, I. 2000b. Imagery neurons in the human brain. Nature 408, 357–361.CrossRefGoogle ScholarPubMed
Negnevitsky, M. 2005. Artificial Intelligence: A Guide to Intelligent Systems, 2nd edition. Addison-Wesley.Google Scholar
Perret, D. I., Hietanen, J. K., Oram, M. W., Benson, P. J. 1992. Organization and function of cells responsive to faces in the temporal cortex. Philosophical Transactions of the Royal Society of London Series B. 335, 1273, 23–30.Google Scholar
Pollen, D. A., Ronner, S. F. 1981. Phase relationships between adjacent simple cells in the visual cortex. Science 212, 1409–1411.CrossRefGoogle ScholarPubMed
Rosch, E. H. 1973. Natural categories. Cognitive Psychology 4, 328–350.CrossRefGoogle Scholar
Tanaka, K. 1993. Neuronal mechanisms of object recognition. Science 262, 684–688.CrossRefGoogle ScholarPubMed
Tulving, E. 1984. Precis of elements of episodic memory. Behavioral and Brain Sciences 7, 223–268.CrossRefGoogle Scholar
von Ahn, L., Liu, R., Blum, M. 2006. Peekaboom: A Game for Locating Objects In Images. Computer–Human Interaction Conference. Montréal, Québec, Canada, 55–64.Google Scholar
Wu, Z., Palmer, M. 1994. Verb semantics and lexical selection. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico, 133–138.Google Scholar
Zadeh, L. A. 1965. Fuzzy sets. Information and Control 8(3), 338–353.CrossRefGoogle Scholar