Detecting distortions of peripherally presented letter stimuli under crowded conditions
When visual features in the periphery are close together they become difficult to recognize: something is present but it is unclear what. This is called “crowding”. Here we investigated sensitivity to features in highly familiar shapes (letters) by applying spatial distortions. In Experiment 1, observers detected which of four peripherally presented (8 deg of retinal eccentricity) target letters was distorted (spatial 4AFC). The letters were presented either isolated or surrounded by four undistorted flanking letters, and distorted with one of two types of distortion at a range of distortion frequencies and amplitudes. The bandpass noise distortion (“BPN”) technique causes spatial distortions in Cartesian space, whereas radial frequency distortion (“RF”) causes shifts in polar coordinates. Detecting distortions in target letters was more difficult in the presence of flanking letters, consistent with the effect of crowding. The BPN distortion type showed evidence of tuning, with sensitivity to distortions peaking at approximately 6.5 c/deg for unflanked letters. The presence of flanking letters causes this peak to rise to approximately 8.5 c/deg. In contrast to the tuning observed for BPN distortions, RF distortion sensitivity increased as the radial frequency of distortion increased. In a series of follow-up experiments, we found that sensitivity to distortions is reduced when flanking letters were also distorted, that this held when observers were required to report which target letter was undistorted, and that this held when flanker distortions were always detectable. The perception of geometric distortions in letter stimuli is impaired by visual crowding.
Keywords2D shape and form Spatial vision Reading Distortion Metamorphopsia
Designed the experiments: TSAW, ST, FAW, MB. Programmed the experiments: ST, TSAW. Collected the data: ST, TSAW. Analyzed the data: TSAW, ST. Wrote the paper: TSAW. Revised the paper: ST, FAW, MB. We thank Peter Bex and William Harrison for helpful comments on the manuscript.
- Arnold, J B (2016). Ggthemes: Extra Themes, Scales and Geoms for ‘ggplot2’. (R package version 3.0., 3.Google Scholar
- Auguie, B (2016). Gridextra: Miscellaneous functions for “grid” graphics. (r package version 2.2.1).Google Scholar
- Bernard, J B, & Chung, S T L (2011). The dependence of crowding on flanker complexity and target-flanker similarity. Journal of Vision, 11(8), 1.Google Scholar
- Chakravarthi, R, & Pelli, D G (2011). The same binding in contour integration and crowding. Journal of Vision, 11(8).Google Scholar
- Dickinson, J E, Mighall, H K, Almeida, R A, Bell, J, & Badcock, D R (2012). Rapidly acquired shape and face aftereffects are retinotopic and local in origin. Vision Research, 65, 1–11. doi:10.1016/j.visres.2012.05.012.
- Greenwood, J A, Bex, P J, & Dakin, S C (2012). Crowding follows the binding of relative position and orientation. Journal of Vision, 12(3).Google Scholar
- Herzog, M H, Sayim, B, Chicherov, V, & Manassi, M (2015). Crowding, grouping, and object recognition: A matter of appearance. Journal of Vision, 15(6).Google Scholar
- Kleiner, M, Brainard, D H, & Pelli, D G (2007). What’s new in Psychtoolbox-3? Perception, 36(ECVP Abstract Supplement).Google Scholar
- Morey, R D, & Rouder, J N. (2015). BayesFactor.Google Scholar
- Core Development Team, R. (2016). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
- Rovamo, J, Mäkelä, P., Näsänen, R., & Whitaker, D (1997). Detection of geometric image distortions at various eccentricities. Investigative Ophthalmology & Visual Science, 38(5), 1029–1039.Google Scholar
- Sayim, B, & Cavanagh, P. (2013). Grouping and crowding affect target appearance over different spatial scales: PLoS ONE.Google Scholar
- Schuchard, R A (1993). Validity and interpretation of Amsler grid reports. Archives of Ophthalmology, 111 (6), 776. 10.1001/archopht.1993.01090060064024.Google Scholar
- Suchow, J W, & Pelli, D G. (2012). Learning to detect and combine the features of an object. Proceedings of the National Academy of Sciences of the United States of America.Google Scholar
- Wickham, H, & Francois, R (2016). Dplyr: A grammar of data manipulation. (R package version 0.5.0).Google Scholar
- Xie, Y (2013). Knitr: A comprehensive tool for reproducible research in R. BT - Implementing Reproducible Computational. In V. Stodden, F. Leisch, & R. D. Peng (Eds.) Implementing Reproducible Computational Research: Chapman & Hall/CRC.Google Scholar
- Xie, Y. (2015). Dynamic documents with r and knitr, 2nd ed.: Chapman & Hall/CRC.Google Scholar