Perceptual learning of oriented gratings as revealed by classication images

Journal of Vision

Volumn 10, Issue 13, 2010, Pages 1-11

  Dobres, Jonathan ^a   Seitz, Aaron R ^b  

^a BOSTON UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Classication images; Orientation discrimination; Perceptual learning

Indexed keywords

ARTICLE; ARTIFACT; DISCRIMINATION LEARNING; FEEDBACK SYSTEM; HUMAN; METHODOLOGY; MONTE CARLO METHOD; ORIENTATION; PATTERN RECOGNITION; PERCEPTION; PERCEPTIVE THRESHOLD; PHOTOSTIMULATION; PHYSIOLOGY; PSYCHOPHYSICS; REACTION TIME;

ARTIFACTS; DISCRIMINATION LEARNING; FEEDBACK; FORM PERCEPTION; HUMANS; MONTE CARLO METHOD; ORIENTATION; PERCEPTUAL MASKING; PHOTIC STIMULATION; PSYCHOPHYSICS; REACTION TIME; SENSORY THRESHOLDS;

EID: 78449248563     PISSN: None     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/10.13.8     Document Type: Article

References (27)

1. Aberg, K. C., & Herzog, M. H. (2009). Interleaving bisection stimuliVrandomly or in sequenceVdoes not disrupt perceptual learning, it just makes it more difficult. Vision Research, 49, 2591-2598.
2. Ahissar, M., & Hochstein, S. (2004). The reverse hierarchy theory of visual perceptual learning. Trends in Cognitive Sciences, 8, 457-464.
3. Ahumada, A. J. (2002). Classification image weights and internal noise level estimation. Journal of Vision, 2(1):8, 121-131, http://www.journalofvision.org/content/2/1/8, doi:10.1167/2.1.8.
4. Ball, K., & Sekuler, R. (1982). A specific and enduring improvement in visual motion discrimination. Science, 218, 697-698.
5. Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10, 433-436.
6. Dosher, B. A., & Lu, Z. L. (1998). Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences of the United States of America, 95, 13988-13993.
7. Dupuis-Roy, N., & Gosselin, F. (2007). Perceptual learning without signal. Vision Research, 47, 349-356.
8. Fahle, M. (2004). Perceptual learning: A case for early selection. Journal of Vision, 4(10):4, 879-890, http://www.journalofvision.org/content/4/10/4, doi:10.1167/ 4.10.4.
9. Fiorentini, A., & Berardi, N. (1980). Perceptual learning specific for orientation and spatial frequency. Nature, 287, 43-44.
10. Gibson, E. J. (1963). Perceptual learning. Annual Reviews in Psychology, 14, 29-56.
11. Gold, J., Bennett, P. J., et al. (1999). Signal but not noise changes with perceptual learning. Nature, 402, 176-178.
12. Keane, B. P., Lu, H., et al. (2007). Classification images reveal spatiotemporal contour interpolation. Vision Research, 47, 3460-3475.
13. Law, C. T., & Gold, J. I. (2008). Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nature Neuroscience, 11, 505-513.
14. Li, R. W., Levi, D. M., et al. (2004). Perceptual learning improves efficiency by re-tuning the decision 'template' for position discrimination. Nature Neuroscience, 7, 178-183.
15. Lu, H. J., & Liu, Z. L. (2006). Computing dynamic classification images from correlation maps. Journal of Vision, 6(4):12, 475-483, http://www.journalofvision.org/content/6/4/12, doi:10.1167/6.4.12.
16. Mareschal, I., Dakin, S. C., et al. (2006). Dynamic properties of orientation discrimination assessed by using classification images. Proceedings of the National Academy of Sciences of the United States of America, 103, 5131-5136.
17. Mollon, J. D., & Danilova, M. V. (1996). Three remarks on perceptual learning. Spatial Vision, 10, 51-58.
18. Nishina, S., Seitz, A. R., et al. (2007). Effect of spatial distance to the task stimulus on task-irrelevant perceptual learning of static Gabors. Journal of Vision, 7(13):2, 1-10, http://www.journalofvision.org/content/7/13/2, doi:10.1167/7.13.2.
19. Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437-442.
20. Reid, R. C., Victor, J. D., et al. (1997). The use of m-sequences in the analysis of visual neurons: Linear receptive field properties. Visual Neuroscience, 14, 1015-1027.
21. Schoups, A., Vogels, R., et al. (2001). Practising orientation identification improves orientation coding in V1 neurons. Nature, 412, 549-553.
22. Seitz, A. R., & Dinse, H. R. (2007). A common frame-work for perceptual learning. Current Opinion in Neurobiology, 17, 148-153.
23. Seitz, A. R., Kim, D., et al. (2009). Rewards evoke learning of unconsciously processed visual stimuli in adult humans. Neuron, 61, 700-707.
24. Sekuler, A. B., Gaspar, C. M., et al. (2004). Inversion leads to quantitative, not qualitative, changes in face processing. Current Biology, 14, 391-396.
25. Shimozaki, S. S., Chen, K. Y., et al. (2007). The temporal dynamics of selective attention of the visual periphery as measured by classification images. Journal of Vision, 7(12):10, http://www.journalofvision.org/content/7/12/10, doi:10.1167/7.12.10.
26. Victor, J. D. (2005). Analyzing receptive fields, classification images and functional images: Challenges with opportunities for synergy. Nature Neuroscience, 8, 1651-1656.
27. Xiao, L. Q., Zhang, J. Y., et al. (2008). Complete transfer of perceptual learning across retinal locations enabled by double training. Current Biology, 18, 1922-1926.