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Integrating holism and reductionism in the science of art perception

Published online by Cambridge University Press:  18 March 2013

Daniel J. Graham
Daniel J. Graham*
Affiliation:
Faculty of Psychology, Department of Psychological Basic Research, University of Vienna, Vienna 1010, Austria; Department of Psychology, Hobart and William Smith Colleges, Geneva, NY 14456. [email protected]://homepage.univie.ac.at/daniel.graham/
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Abstract

The contextualist claim that universalism is irrelevant to the proper study of art can be evaluated by examining an analogous question in neuroscience. Taking the reductionist-holist debate in visual neuroscience as a model, we see that the analog of orthodox contextualism is untenable, whereas integrated approaches have proven highly effective. Given the connection between art and vision, unified approaches are likewise more germane to the scientific study of art.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 2 , April 2013 , pp. 145 - 146
Copyright
Copyright © Cambridge University Press 2013 

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References

Chave, A. (1999) Pollock and Krasner: Script and postscript. In Jackson Pollock: Interviews, articles, and reviews, 1943–1993, pp. 262–279. ed. Karmel, P.. Museum of Modern Art.Google Scholar
David, S. V. & Gallant, J. L. (2005) Predicting neuronal responses during natural vision. Network 16:239–60.CrossRefGoogle ScholarPubMed
David, S. V., Vinje, W. E. & Gallant, J. L. (2004) Natural stimulus statistics alter the receptive field structure of V1 neurons. Journal of Neuroscience 24:6991–7006.CrossRefGoogle ScholarPubMed
Felsen, G. & Dan, Y. (2005) A natural approach to studying vision. Nature Neuroscience 8:1643–46.CrossRefGoogle ScholarPubMed
Field, D. J. (1994) What is the goal of sensory coding? Neural Computation 6:559–601.CrossRefGoogle Scholar
Field, D. J. (1987) Relations between the statistics of natural images and the response profiles of cortical cells. Journal of the Optical Society of America A 4:2379–94.CrossRefGoogle Scholar
Geisler, W. S. (2008) Visual perception and the statistical properties of natural scenes. Annual Review of Psychology 59:167–92.CrossRefGoogle ScholarPubMed
Graham, D. J. & Field, D. J. (2007) Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. Spatial Vision 21:149–64.CrossRefGoogle ScholarPubMed
Graham, D. J. & Field, D. J. (2008) Variations in intensity statistics for representational and abstract art, and for art from the eastern and western hemispheres. Perception 37:1341–52.CrossRefGoogle ScholarPubMed
Graham, D. J. & Field, D. J. (2009) Natural images: Coding efficiency. In: Encyclopedia of neuroscience, Vol. VI, ed. Squire, L. R., pp. 19–27. Academic Press.CrossRefGoogle Scholar
Lewicki, M. S. (2002) Efficient coding of natural sounds. Nature Neuroscience 5:356–63.CrossRefGoogle ScholarPubMed
Olshausen, B. A. & Field, D. J. (2004) Sparse coding of sensory inputs. Current Opinion in Neurobiology 14:481–87.CrossRefGoogle ScholarPubMed
Olshausen, B. A. & Field, D. J. (2005) How close are we to understanding V1? Neural Computation 17:1665–99.CrossRefGoogle ScholarPubMed
Pinto, N., Cox, D. D. & DiCarlo, J. J. (2008) Why is real-world visual object recognition hard? PLoS Computational Biology 41:e27.Google Scholar
Redies, C., Hasenstein, J. & Denzler, J. (2007) Fractal-like image statistics in visual art: Similarity to natural scenes. Spatial Vision 21:137–48.CrossRefGoogle ScholarPubMed
Rust, N. C. & Movshon, J. A. (2005) In praise of artifice. Nature Neuroscience 8:1647–50.CrossRefGoogle ScholarPubMed
Simoncelli, E. P. & Olshausen, B. A. (2001) Natural image statistics and neural representation. Annual Review of Neuroscience 24:1193–215.CrossRefGoogle ScholarPubMed