Abstract
Ensemble perception refers to the ability to accurately and rapidly perceive summary statistical representations of specific features from a group of similar objects. However, the specific type of representation involved in this perception within a three-dimensional (3-D) environment remains unclear. In the context of perspective viewing with stereopsis, distal stimuli can be projected onto the retina as different forms of proximal stimuli based on their distances, despite sharing similar properties, such as object size and spatial frequency. This study aimed to investigate the effects of distal and proximal stimuli on the perception of summary statistical information related to orientation. In our experiment, we presented multiple Gabor patches in a stereoscopic environment, allowing us to measure the discrimination threshold of the mean orientation. The object size and spatial frequency were fixed for all patches regardless of depth. However, the physical angular size and absolute spatial frequency covaried with the depth. The results revealed the threshold elevation with depth expansion, especially when the patches formed two clusters at near and far distances, leading to large variations in their retinotopic representations. This finding indicates a minor contribution of similarity of the distal stimuli. Subsequent experiments demonstrated that the variability in physical angular size of the patches significantly influenced the threshold elevation in contrast to that of binocular disparity and absolute spatial frequency. These findings highlight the critical role of physical angular size variability in perceiving mean orientations within the 3-D space.
Similar content being viewed by others
Data availability
Experimental data and images of experimental stimuli related to this article can be found via the Open Science Framework online at https://osf.io/92fz6/?view_only=dae1efe2f42f4c5bb28a6dbbf5012eb4.
References
Akerstrom, R. A., & Todd, J. T. (1988). The perception of stereoscopic transparency. Perception and Psychophysics, 44(5), 421–432. https://doi.org/10.3758/BF03210426
Alvarez, G. A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in Cognitive Sciences, 15(3), 122–131. https://doi.org/10.1016/j.tics.2011.01.003
Alvarez, G. A., & Oliva, A. (2008). The representation of simple ensemble visual features outside the focus of attention. Psychological Science, 19(4), 392–398. https://doi.org/10.1111/j.1467-9280.2008.02098.x
Ariely, D. (2001). Seeing sets: representation by statistical properties. Psychological Science, 12(2), 157–162. https://doi.org/10.1111/1467-9280.00327
Bauer, B. (2009). Does Stevens’s power law for brightness extend to perceptual brightness averaging? Psychological Record, 59(2), 171–185. https://doi.org/10.1007/BF03395657
Blake, A., & Marinos, C. (1990). Shape from Texture: Estimation, Isotropy and Moments. Artificial Intelligence, 45(3), 323–380. https://doi.org/10.1016/0004-3702(90)90011-N
Blakemore, C. (1970). The range and scope of binocular depth discrimination in man. Journal of Physiology, 211(3), 599–622. https://doi.org/10.1113/jphysiol.1970.sp009296
Blakemore, C., & Campbell, F. W. (1969). On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. Journal of Physiology, 203(1), 237–260. https://doi.org/10.1113/jphysiol.1969.sp008862
Brady, T. F., Shafer-Skelton, A., & Alvarez, G. A. (2017). Global ensemble texture representations are critical to rapid scene perception. Journal of Experimental Psychology. Human Perception and Performance, 43(6), 1160–1176. https://doi.org/10.1037/xhp0000399
Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10(4), 433–436. https://doi.org/10.1163/156856897X00357
Campbell, F. W., & Robson, J. G. (1968). Application of Fourier analysis to the visibility of gratings. The Journal of Physiology, 197(3), 551–566. https://doi.org/10.1113/jphysiol.1968.sp008574
Cha, O., & Chong, S. C. (2018). Perceived average orientation reflects effective gist of the surface. Psychological Science, 29(3), 319–327. https://doi.org/10.1177/0956797617735533
Chong, S. C., & Treisman, A. (2003). Representation of statistical properties. Vision Research, 43(4), 393–404. https://doi.org/10.1016/s0042-6989(02)00596-5
Collett, T. S., Schwarz, U., & Sobel, E. C. (1991). The interaction of oculomotor cues and stimulus size in stereoscopic death constancy. Perception, 20(6), 733–754. https://doi.org/10.1068/p200733
Corbett, J. E., & Oriet, C. (2011). The whole is indeed more than the sum of its parts: perceptual averaging in the absence of individual item representation. Acta Psychologica, 138(2), 289–301. https://doi.org/10.1016/j.actpsy.2011.08.002
Dakin, S. C. (2001). Information limit on the spatial integration of local orientation signals. Journal of the Optical Society of America A, 18(5), 1016–1026. https://doi.org/10.1364/josaa.18.001016
Dakin, S. C., & Hess, R. F. (1998). Spatial-frequency tuning of visual contour integration. Journal of the Optical Society of America A, 15(6), 1486–1499. https://doi.org/10.1364/josaa.15.001486
Dakin, S. C., & Watt, R. J. (1997). The computation of orientation statistics from visual texture. Vision Research, 37(22), 3181–3192. https://doi.org/10.1016/S0042-6989(97)00133-8
De Valois, R. L., Albrecht, D. G., & Thorell, L. G. (1982). Spatial frequency selectivity of cells in macaque visual cortex. Vision Research, 22(5), 545–559. https://doi.org/10.1016/0042-6989(82)90113-4
Driver, J., & Vuilleumier, P. (2001). Perceptual awareness and its loss in unilateral neglect and extinction. Cognition, 79(1–2), 39–88. https://doi.org/10.1016/s0010-0277(00)00124-4
Emmanouil, T. A., & Treisman, A. (2008). Dividing attention across feature dimensions in statistical processing of perceptual groups. Perception & Psychophysics, 70(6), 946–954. https://doi.org/10.3758/pp.70.6.946
Fahle, M., Henke-Fahle, S., & Harris, J. (1994). Definition of thresholds for stereoscopic depth. British Journal of Ophthalmology, 78(7), 572–576. https://doi.org/10.1136/bjo.78.7.572
Fleming, R. W., & Bülthoff, H. H. (2005). Low-level image cues in the perception of translucent materials. ACM Transactions on Applied Perception, 2(3), 346–382. https://doi.org/10.1145/1077399.1077409
Gegenfurtner, K. R., Kiper, D. C., & Fenstemaker, S. B. (1996). Processing of color, form, and motion in macaque area V2. Visual Neuroscience, 13(1), 161–172. https://doi.org/10.1017/s0952523800007203
Geisler, W. S., Perry, J. S., Super, B. J., & Gallogly, D. P. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision Research, 41(6), 711–724. https://doi.org/10.1016/s0042-6989(00)00277-7
Gilinsky, A. S. (1968). Orientation-specific effects of patterns of adapting light on visual acuity. Journal of the Optical Society of America, 58(1), 13–18. https://doi.org/10.1364/josa.58.000013
Gorea, A., & Papathomas, T. V. (1999). Local versus global contrasts in texture segregation. Journal of the Optical Society of America A, 16(3), 728–741. https://doi.org/10.1364/josaa.16.000728
Haberman, J., & Suresh, S. (2021). Ensemble size judgments account for size constancy. Attention, Perception, & Psychophysics, 83(3), 925–933. https://doi.org/10.3758/s13414-020-02144-6
Haberman, J., Brady, T. F., & Alvarez, G. A. (2015). Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation. Journal of Experimental Psychology: General, 144(2), 432–446. https://doi.org/10.1037/xge0000053
Hess, R., & Field, D. (1999). Integration of contours: new insights. Trends in Cognitive Sciences, 3(12), 480–486. https://doi.org/10.1016/S1364-6613(99)01410-2
Hubert-Wallander, B., & Boynton, G. M. (2015). Not all summary statistics are made equal: Evidence from extracting summaries across time. Journal of Vision, 15(4), 5. https://doi.org/10.1167/15.4.5
Iakovlev, A. U., & Utochkin, I. S. (2021). Roles of saliency and set size in ensemble averaging. Attention, Perception & Psychophysics, 83(3), 1251–1262. https://doi.org/10.3758/s13414-020-02089-w
Im, H. Y., Park, W. J., & Chong, S. C. (2015). Ensemble statistics as units of selection. Journal of Cognitive Psychology, 27(1), 114–127. https://doi.org/10.1080/20445911.2014.985301
Im, H. Y., Tiurina, N. A., & Utochkin, I. S. (2021). An explicit investigation of the roles that feature distributions play in rapid visual categorization. Attention, Perception & Psychophysics, 83(3), 1050–1069. https://doi.org/10.3758/s13414-020-02046-7
Kanaya, S., Hayashi, M. J., & Whitney, D. (2018). Exaggerated groups: amplification in ensemble coding of temporal and spatial features. Proceedings. Biological Sciences, 285(1879), 20172770. https://doi.org/10.1098/rspb.2017.2770
Keshvari, S., & Rosenholtz, R. (2016). Pooling of continuous features provides a unifying account of crowding. Journal of Vision, 16(3), 39. https://doi.org/10.1167/16.3.39
Kristjánsson, A. (2006). Surface assignment modulates object formation for visual short-term memory. Perception, 35(7), 865–881. https://doi.org/10.1068/p5526
Li, V., Herce Castañón, S., Solomon, J. A., Vandormael, H., & Summerfield, C. (2017). Robust averaging protects decisions from noise in neural computations. PLoS Computational Biology, 13(8), e1005723. https://doi.org/10.1371/journal.pcbi.1005723
Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279–281. https://doi.org/10.1038/36846
Marchant, A. P., Simons, D. J., & de Fockert, J. W. (2013). Ensemble representations: Effects of set size and item heterogeneity on average size perception. Acta Psychologica, 142(2), 245–250. https://doi.org/10.1016/j.actpsy.2012.11.002
Markov, Y. A., & Tiurina, N. A. (2021). Size-distance rescaling in the ensemble representation of range: Study with binocular and monocular cues. Acta Psychologica, 213, 103238. https://doi.org/10.1016/j.actpsy.2020.103238
Maule, J., & Franklin, A. (2015). Effects of ensemble complexity and perceptual similarity on rapid averaging of hue. Journal of Vision, 15(4), 6. https://doi.org/10.1167/15.4.6
McKee, S. P., & Verghese, P. (2002). Stereo transparency and the disparity gradient limit. Vision Research, 42(16), 1963–1977. https://doi.org/10.1016/S0042-6989(02)00073-1
Morgan, M. J. (2005). The visual computation of 2-D area by human observers. Vision Research, 45(19), 2564–2570. https://doi.org/10.1016/j.visres.2005.04.004
Morgan, M., Chubb, C., & Solomon, J. A. (2008). A ‘dipper’ function for texture discrimination based on orientation variance. Journal of Vision, 8(11), 1–8. https://doi.org/10.1167/8.11.9
Motoyoshi, I., Nishida, S., Sharan, L., & Adelson, E. H. (2007). Image statistics and the perception of surface qualities. Nature, 447(7141), 206–209. https://doi.org/10.1038/nature05724
Murray, S. O., Boyaci, H., & Kersten, D. (2006). The representation of perceived angular size in human primary visual cortex. Nature Neuroscience, 9(3), 429–434. https://doi.org/10.1038/nn1641
Myczek, K., & Simons, D. J. (2008). Better than average: alternatives to statistical summary representations for rapid judgments of average size. Perception and Psychophysics, 70(5), 772–788. https://doi.org/10.3758/PP.70.5.772
Nachmias, J. (2008). Judging spatial properties of simple figures. Vision Research, 48(11), 1290–1296. https://doi.org/10.1016/j.visres.2008.02.024
Ni, L., & Stocker, A. A. (2022). Efficient sensory encoding predicts robust averaging. Cognition, 232, 105334. https://doi.org/10.1016/j.cognition.2022.105334
Ogle, K. N. (1953). Precision and validity of stereoscopic depth perception from double images. Journal of the Optical Society of America, 43(10), 907–913. https://doi.org/10.1364/JOSA.43.000906
Oliva, A., & Torralba, A. (2006). Building the gist of a scene: the role of global image features in recognition. Progress in Brain Research, 155, 23–36. https://doi.org/10.1016/S0079-6123(06)55002-2
Olzak, L. A., & Thomas, J. P. (1999). Neural recoding in human pattern vision: model and mechanisms. Vision Research, 39(2), 231–256. https://doi.org/10.1016/s0042-6989(98)00122-9
Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4(7), 739–744. https://doi.org/10.1038/89532
Parsons, L. M., & Shimojo, S. (1987). Perceived spatial organization of cutaneous patterns on surfaces of the human body in various positions. Journal of Experimental Psychology: Human Perception and Performance, 13(3), 488–504. https://doi.org/10.1037//0096-1523.13.3.488
Pascucci, D., Ruethemann, N., & Plomp, G. (2021). The anisotropic field of ensemble coding. Scientific Reports, 11(1), 8212. https://doi.org/10.1038/s41598-021-87620-1
Polat, U. (2009). Effect of spatial frequency on collinear facilitation. Spatial Vision, 22(2), 179–193. https://doi.org/10.1163/156856809787465609
Polat, U., & Sagi, D. (1993). Lateral interactions between spatial channels: Suppression and facilitation revealed by lateral masking experiments. Vision Research, 33(7), 993–999. https://doi.org/10.1016/0042-6989(93)90081-7
Polat, U., & Sagi, D. (1994). Spatial interactions in human vision: From near to far via experience-dependent cascades of connections. Proceedings of the National Academy of Sciences, 91(4), 1206–1209. https://doi.org/10.1073/pnas.91.4.1206
Rajendran, S., Maule, J., Franklin, A., & Webster, M. A. (2021). Ensemble coding of color and luminance contrast. Attention, Perception, & Psychophysics, 83(3), 911–924. https://doi.org/10.3758/s13414-020-02136-6
Regan, D., & Beverley, K. I. (1983). Spatial-frequency discrimination and detection: Comparison of postadaptation thresholds. Journal of the Optical Society of America, 73(12), 1684–1690. https://doi.org/10.1364/JOSA.73.001684
Rosenholtz, R. (2016). Capabilities and limitations of peripheral vision. Annual Review of Vision Science, 2, 437–457. https://doi.org/10.1146/annurev-vision-082114-035733
Ross, M. G., & Oliva, A. (2010). Estimating perception of scene layout properties from global image features. Journal of Vision, 10(1), 1–25. https://doi.org/10.1167/10.1.2
Solomon, J. A., Morgan, M., & Chubb, C. (2011). Efficiencies for the statistics of size discrimination. Journal of Vision, 11(12), 13. https://doi.org/10.1167/11.12.13
Sousa, R., Smeets, J. B. J., & Brenner, E. (2012). The effect of variability in other objects’ sizes on the extent to which people rely on retinal image size as a cue for judging distance. Journal of Vision, 12(10), 6. https://doi.org/10.1167/12.10.6
Sterzer, P., & Rees, G. (2006). Perceived size matters. Nature Neuroscience, 9(3), 302–304. https://doi.org/10.1038/nn0306-302b
Sweeny, T. D., Haroz, S., & Whitney, D. (2013). Perceiving group behavior: Sensitive ensemble coding mechanisms for biological motion of human crowds. Journal of Experimental Psychology, 39(2), 329–337. https://doi.org/10.1037/a0028712
Takebayashi, H., & Saiki, J. (2022). Restriction of orientation variability and spatial frequency on the perception of average orientation. Perception, 51(7), 464–476. https://doi.org/10.1177/03010066221099356
Tanaka, S., & Fujita, I. (2015). Computation of object size in visual cortical area V4 as a neural basis for size constancy. The Journal of Neuroscience, 35(34), 12033–12046. https://doi.org/10.1523/JNEUROSCI.2665-14.2015
Tanaka, Y., & Sagi, D. (1998). Long-lasting, long-range detection facilitation. Vision Research, 38(17), 2591–2599. https://doi.org/10.1016/S0042-6989(97)00465-3
Thomas, J. P., & Shimamura, K. K. (1974). Perception of size at the detection threshold: its accuracy and possible mechanisms. Vision Research, 14(7), 535–543. https://doi.org/10.1016/0042-6989(74)90043-1
Tiurina, N. A., & Utochkin, I. S. (2019). Ensemble perception in depth: correct size-distance rescaling of multiple objects before averaging. Journal of Experimental Psychology: General, 148(4), 728–738. https://doi.org/10.1037/xge0000485
Tiurina, N. A., Markov, Y. A., Choung, O. H., Herzog, M. H., & Pascucci, D. (2022). Unlocking crowding by ensemble statistics. Current Biology: CB, 32(22), 4975-4981.e3. https://doi.org/10.1016/j.cub.2022.10.003
Ungerleider, L., Ganz, L., & Pribram, K. H. (1977). Size constancy in rhesus monkeys: Effects of pulvinar, prestriate, and inferotemporal lesions. Experimental Brain Research, 27(3–4), 251–269. https://doi.org/10.1007/BF00235502
Utochkin, I. S., & Tiurina, N. A. (2014). Parallel averaging of size is possible but range-limited: a reply to Marchant, Simons, and De Fockert. Acta Psychologica, 146, 7–18. https://doi.org/10.1016/j.actpsy.2013.11.012
Utochkin, I. S., & Vostrikov, K. O. (2017). The numerosity and mean size of multiple objects are perceived independently and in parallel. PloS One, 12(9), e0185452. https://doi.org/10.1371/journal.pone.0185452
Utochkin, I. S., & Yurevich, M. A. (2016). Similarity and heterogeneity effects in visual search are mediated by “segmentability.” Journal of Experimental Psychology: Human Perception and Performance, 42(7), 995–1007. https://doi.org/10.1037/xhp0000203
Utochkin, I. S., Khvostov, V. A., & Stakina, Y. M. (2018). Continuous to discrete: Ensemble-based segmentation in the perception of multiple feature conjunctions. Cognition, 179, 178–191. https://doi.org/10.1016/j.cognition.2018.06.016
Watson, A. B., & Robson, J. G. (1981). Discrimination at threshold: Labelled detectors in human vision. Vision Research, 21(7), 1115–1122. https://doi.org/10.1016/0042-6989(81)90014-6
Whitney, D., & Leib, A. Y. (2018). Ensemble perception. Annual Review of Psychology, 69(1), 105–129. https://doi.org/10.1146/annurev-psych-010416-044232
Witkin, A. P. (1981). Recovering surface shape and orientation from texture. Artificial Intelligence, 17, 17–45. https://doi.org/10.1016/0004-3702(81)90019-9
Xu, Y., & Nakayama, K. (2007). Visual short-term memory benefit for objects on different 3-D surfaces. Journal of Experimental Psychology, 136(4), 653–662. https://doi.org/10.1037/0096-3445.136.4.653
Zeng, H., Fink, G. R., & Weidner, R. (2020). Visual size processing in early visual cortex follows lateral occipital cortex involvement. The Journal of Neuroscience, 40(22), 4410–4417. https://doi.org/10.1523/JNEUROSCI.2437-19.2020
Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233–235. https://doi.org/10.1038/nature06860
Acknowledgements
The authors thank Natalia Tiurina and an anonymous reviewer for their helpful comments on earlier drafts of the article.
Funding
This research was supported by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science [16H01727] and [20J20010].
Author information
Authors and Affiliations
Contributions
Takebayashi and Saiki developed the concept of the study and contributed to its design. H. Takebayashi also performed the experimental programs, data collection, data analysis, interpretation, and drafting of the manuscript under the supervision of J. Saiki. All authors have approved the final version of the manuscript for submission.
Corresponding author
Ethics declarations
Declarations
The research complied with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All the participants provided written informed consent. All experiments were approved by the institutional review board of Kyoto University.
Conflict of interest
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Takebayashi, H., Saiki, J. Mean orientation discrimination based on proximal stimuli. Atten Percept Psychophys (2024). https://doi.org/10.3758/s13414-024-02881-y
Accepted:
Published:
DOI: https://doi.org/10.3758/s13414-024-02881-y