ULOGA OBLIKA I BOJE U PREPOZNAVANJU I KLASIFIKACIJI  POZNATIH VIZUELNIH OBJEKATA

Tomislav Ludajić; Sunčica Zdravković

doi:10.19090/pp.2016.3.333-352

Authors

Tomislav Ludajić Department of Psychology, Faculty of Philosophy, University of Novi Sad
Sunčica Zdravković Department of Psychology, Faculty of Philosophy, University of Novi Sad Laboratory for Experimental Psychology, Faculty of Philosophy, University of Novi Sad Laboratory for Experimental Psychology, Faculty of Philosophy, University of Belgrade

DOI:

https://doi.org/10.19090/pp.2016.3.333-352

Keywords:

object recognition, diagnostic colour, classification, visual recognition, verification task

Abstract

There are two distinct approaches concerning the importance of colour and shape in visual recognition and object classification. The first group of theories emphasize the role of shape, while the second propose that the colour is equally important as shape. Hence, the latter group of theories segregate all objects into two categories: 1) HCD, for which colour and shape are equally important visual characteristics and 2) LCD, primary relying on shape. Our goal was to establish the impact of shape and colour in classification and recognition of visual objects. We wanted to determine whether there is a difference in shape and colour contribution when it comes to natural vs. man-made objects. Hence, we used food for our stimuli, given that in this category both natural and man-made objects are equally familiar and frequent, and both possess diagnostic colours. Our results strongly support theories that emphasize the importance of shape both for categorization and object recognition. It seems that colour only plays a role in recognition when shape is so deformed that it is essentially uninformative.

Metrics

Metrics Loading ...

References

Baayen, R. H. (2013). LanguageR: Data sets and functions with Analyzing Linguistic Data: A practical introduction to statistics. R package version 1.4.1. Retrieved from: http//cran.r-project.org/web/packages/languageR/index.html

Barr, D. J. (2013). Random effects structure for testing interactions in linear mixed-effects models. Frontiers in psychology, 4, 328. doi:10.3389/fpsyg.2013.00328

Bates, D., Maechler, M., Bolker, B., Walker, S., Christiansen, R. H. B., Singmann, H., & Bin, D. (2013). Lme4: Linear mixed-effects models using s4 classes and methods [Computer software manual]. R package version 1.1-7. Retrieved from: http://cran.r-project.org/web/packages/lme4/index.html

Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94, 115–147. doi:10.1037/0033-295X.94.2.115

Biederman, I., & Bar, M. (1999). One-shot viewpoint invariance in matching novel objects. Vision Research, 39, 2885–2899. doi:10.1016/S0042-6989(98)00309-5

Biederman, I., & Gerhardstein, P. C. (1993). Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. Journal of Experimental Psychology: Human Perception and Performance, 19, 1162–1182. doi:10.1037/0096-1523.19.6.1162

Biederman, I., & Ju, G. (1988). Surface vs. edge-based determinants of visual recognition. Cognitive Psychology, 20, 38–64. doi:10.1016/0010-0285(88)90024-2

Bramão, I., Reis, A., Petersson, K. M., & Faísca, L. (2011). The role of color information on object recognition: A review and meta-analysis. Acta psychologica, 138, 244–253. doi:10.1016/j.actpsy.2011.06.010

Cave, C. B., Bost, P. R., & Cobb, R. E. (1996). Effects of color and pattern on implicit and explicit picture memory. Journal of Experimental Psychology Learning Memory and Cognition, 22, 639–653. doi:10.1037/0278-7393.22.3.639

Grossberg, S., & Mingolla, E. (1985). Neural dynamics of form perception: Boundary completion, illusory figures, and neon color spreading. Psychological Review, 92, 173–211. doi:10.1037/0033-295X.92.2.173

Hagen, S., Vuong, Q. C., Scott, L. S., Curran, T., & Tanaka, J. W. (2014). The role of color in expert object recognition. Journal of Vision, 14, 9. doi:10.1167/14.9.9

Kuznetsova, A., Brockhoff, P. B, & Christiansen, R. H. B. (2014). LmerTest: Linear mixed-effects [Computer software manual]. R package version 2.0-20. Retrieved from: http://cran.r-project.org/web/packages/lme4/index.html

Landy, M. S., & Graham, N. (2004). Visual perception of texture. In L. M. Chalupa & J. S. Werner (Eds.), The Visual Neurosciences, Volume 2 (pp. 1106–1118). Cambridge: The MIT Press.

Lewis, D. E., Pearson, J., & Khuu, S. K. (2013). The color “fruit”: Object memories defined by color. PloS one, 8, e64960. doi:10.1167/13.9.1009

Li, A., & Zaidi, Q. (2001). Information limitations in perception of shape from texture. Vision Research, 41, 1519–1533. doi:10.1016/S0042-6989(01)00021-9

Marr, D. (1982). Vision. San Francisco: Freeman.

Ostergard, A. L., & Davidoff., I. B. (1985). Some effects of color on naming and recognition of objects. Journal of Experimental Psychology: Learning. Memorv, & Cognition, 11, 579–587. doi:10.1037/0278-7393.11.3.579

Radanović, J. i Vaci, N. (2013). Analiza vremena reakcije modelovanjem linearnih mešovitih efekata. Primenjena Psihologija, 6, 311–332. doi:10.19090/pp.2013.3.311-332

Tanaka J. W., & Presnell, L. M. (1999) Color diagnosticity in object recognition. Perception and Psychophycs, 61, 1140–1153. doi:10.3758/BF03207619

Tanaka J., Weiskopf, D., & Williams, P. (2001). The role of color high-level vision. Trends in Cognitive Science, 5, 211–215. doi: 10.1016/S1364-6613(00)01626-0

Therriault, D. J., Yaxley, R. H., & Zwaan, R. A. (2009). The role of color diagnosticity in object recognition and language comprehension. Cognitive Processing, 10, 335–342. doi:10.1007/s10339-009-0260-4

Ullman, S. (1984). Visual routines. Cognition, 18, 97–159. doi:10.1016/0010-0277(84)90023-4