Perception of multi-dimensional regularities is driven by salience
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A challenge for the visual system is to detect regularities from multiple dimensions of the environment. Here we examine how regularities in multiple feature dimensions are distinguished from randomness. Participants viewed a matrix containing a structured half and a random half, and judged whether the boundary between the two halves was horizontal or vertical. In Experiments 1 and 2, the cells in the matrix varied independently in the color dimension (red or blue), the shape dimension (circle or square), or both. We found that boundary discrimination accuracy was higher when regularities were present in the color dimension than in the shape dimension, but the accuracy was the same when regularities were present in the color dimension alone or in both dimensions. By adding a third surface dimension (hollow or filled) in Experiments 3 and 4, we found that discrimination accuracy was higher when regularities were present in the surface dimension than in the color dimension, but was the same when regularities were present in the surface dimension alone or in all three dimensions. Moreover, when there were two conflicting boundaries, participants chose the boundary defined by the surface dimension, followed by the color dimension as more visible than the shape dimension (Experiments 5 and 6). Finally, participants were faster at detecting differences in the surface dimension, followed by the color and the shape dimensions (Experiments 7 and 8). These results suggest that perception of regularities in multiple feature dimensions is driven by the presence of regularities in the most salient feature dimension.
KeywordsAttention Detection Feature Randomness Pattern
We thank the Zhao Lab for helpful comments. This work was supported by NSERC Discovery Grant (RGPIN-2014-05617 to JZ), the Canada Research Chairs program (to JZ), the Leaders Opportunity Fund from the Canadian Foundation for Innovation (F14-05370 to JZ), the NSERC Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program (to RY), and Elizabeth Young Lacey Fellowship (to YL).
- Huang, L., & Pashler, H. (2012). Distinguishing different strategies of across-dimension attentional selection. Journal of Experimental Psychology: Human Perception and Performance, 38, 453-464.Google Scholar
- Itti, L., & Koch, C. (1999). Comparison of feature combination strategies for saliency-based visual attention systems. Human Vision and Electronic Imaging (Vol. 3644, pp. 473-482).Google Scholar
- Jiang, Y. V., Swallow, K. M., & Rosenbaum, G. M. (2013). Guidance of spatial attention by incidental learning and endogenous cuing. Journal of Experimental Psychology: Human Perception and Performance, 39, 285.Google Scholar
- Nothelfer, C., Gleicher, M., & Franconeri, S. (2017). Redundant encoding strengthens segmentation and grouping in visual displays of data. Journal of Experimental Psychology: Human Perception and Performance, 43(9), 1667-1676.Google Scholar
- Pavlov, I. P., & Anrep, G. V. (2003). Conditioned reflexes. Courier Corporation.Google Scholar
- Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current theory and research (pp. 64-99). New York: Appleton-Century-Crofts.Google Scholar
- Turk-Browne, N. B., Isola, P. J., Scholl, B. J., & Treat, T. A. (2008). Multidimensional visual statistical learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 399-407.Google Scholar
- Yu, R., Osherson, D., & Zhao, J. (2018b). Alternation blindness in the representation of binary sequences. Journal of Experimental Psychology: Human Perception and Performance, 44, 493-502.Google Scholar
- Zhao, J., Hahn, U., & Osherson, D. (2014). Perception and identification of random events. Journal of Experimental Psychology: Human Perception and Performance, 40, 1358-1371.Google Scholar