Advertisement

Task-driven and flexible mean judgment for heterogeneous luminance ensembles

  • Yusuke Takano
  • Eiji KimuraEmail author
40 Years of Feature Integration: Special Issue in Memory of Anne Treisman
  • 20 Downloads

Abstract

Spatial averaging of luminances over a variegated region has been assumed in visual processes such as light adaptation, texture segmentation, and lightness scaling. Despite the importance of these processes, how mean brightness can be computed remains largely unknown. We investigated how accurately and precisely mean brightness can be compared for two briefly presented heterogeneous luminance arrays composed of different numbers of disks. The results demonstrated that mean brightness judgments can be made in a task-dependent and flexible fashion. Mean brightness judgments measured via the point of subjective equality (PSE) exhibited a consistent bias, suggesting that observers relied strongly on a subset of the disks (e.g., the highest- or lowest-luminance disks) in making their judgments. Moreover, the direction of the bias flexibly changed with the task requirements, even when the stimuli were completely the same. When asked to choose the brighter array, observers relied more on the highest-luminance disks. However, when asked to choose the darker array, observers relied more on the lowest-luminance disks. In contrast, when the task was the same, observers’ judgments were almost immune to substantial changes in apparent contrast caused by changing the background luminance. Despite the bias in PSE, the mean brightness judgments were precise. The just-noticeable differences measured for multiple disks were similar to or even smaller than those for single disks, which suggested a benefit of averaging. These findings implicated flexible weighted averaging; that is, mean brightness can be judged efficiently by flexibly relying more on a few items that are relevant to the task.

Keywords

Ensemble perception Summary statistics Averaging Brightness Lightness 

Notes

Acknowledgments

This work was partly supported by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science to E.K. (Grant Nos. 26285162 and 25285197). We are grateful to Sang Chul Chong, Charlie Chubb, and an anonymous reviewer for their helpful comments and suggestions on earlier versions of the manuscript.

Open Practices Statement

The data and materials for all experiments are available upon request from the authors.

Supplementary material

13414_2019_1862_MOESM1_ESM.pdf (521 kb)
ESM 1 (PDF 521 kb)

References

  1. Allik, J., Toom, M., Raidvee, A., Averin, K., & Kreegipuu, K. (2013). An almost general theory of mean size perception. Vision Research, 83, 25–39.  https://doi.org/10.1016/j.visres.2013.02.018 CrossRefGoogle Scholar
  2. Alvarez, G. A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in Cognitive Sciences, 15, 122–131.  https://doi.org/10.1016/j.tics.2011.01.003 CrossRefGoogle Scholar
  3. Anderson, B. L., & Winawer, J. (2005). Image segmentation and lightness perception. Nature, 434, 79–83.CrossRefGoogle Scholar
  4. Anderson, B. L., & Winawer, J. (2008). Layered image representations and the computation of surface lightness. Journal of Vision, 8(7), 18:1–22. http://journalofvision.org/8/7/18/,  https://doi.org/10.1167/8.7.18
  5. Ariely, D. (2001). Seeing sets: Representation by statistical properties. Psychological Science, 12, 157–162.  https://doi.org/10.1111/1467-9280.00327 CrossRefGoogle Scholar
  6. Ariely, D. (2008). Better than average? When can we say that subsampling of items is better than statistical summary representations? Perception & Psychophysics, 70, 1325–1326.  https://doi.org/10.3758/PP.70.7.1325 CrossRefGoogle Scholar
  7. Bauer, B. (2009). Does Stevens’s power law for brightness extend to perceptual brightness averaging? Psychological Record, 59, 171–186.CrossRefGoogle Scholar
  8. Bauer, B. (2015). A selective summary of visual averaging research and issues up to 2000. Journal of Vision, 15(4), 14:1–15,  https://doi.org/10.1167/15.4.14 CrossRefGoogle Scholar
  9. Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10, 433–436.  https://doi.org/10.1163/156856897X00357 CrossRefGoogle Scholar
  10. Bressan, P. (2006). The place of white in a world of grays: A double-anchoring theory of lightness perception. Psychological Review, 113, 526–553.  https://doi.org/10.1037/0033-295X.113.3.526 CrossRefGoogle Scholar
  11. Buchsbaum, G. (1980). A spatial processor model for object colour perception. Journal of the Franklin Institute, 310, 1–26.  https://doi.org/10.1016/0016-0032(80)90058-7 CrossRefGoogle Scholar
  12. Chong, S., Joo, S., Emmmanouil, T.-A., & Treisman, A. (2008). Statistical processing: Not so implausible after all. Perception & Psychophysics, 70, 1327–1334.  https://doi.org/10.3758/PP.70.7.1327 CrossRefGoogle Scholar
  13. Chong, S. C., & Treisman, A. (2003). Representation of statistical properties. Vision Research, 43, 393–404.  https://doi.org/10.1016/S0042-6989(02)00596-5 CrossRefGoogle Scholar
  14. Chong, S. C., & Treisman, A. (2005). Statistical processing: Computing the average size in perceptual groups. Vision Research, 45, 891–900.  https://doi.org/10.1016/j.visres.2004.10.004 CrossRefGoogle Scholar
  15. Chubb, C., Econopouly, J., & Landy, M. S. (1994). Histogram contrast analysis and the visual segregation of IID textures. Journal of the Optical Society of America A, 11, 2350–2374.  https://doi.org/10.1364/JOSAA.11.002350 CrossRefGoogle Scholar
  16. Chubb, C., Landy, M. S., & Econopouly, J. (2004). A visual mechanism tuned to black. Vision Research, 44, 3223–3232.  https://doi.org/10.1016/j.visres.2004.07.019 CrossRefGoogle Scholar
  17. Corbett, J. E., Oriet, C., & Rensink, R. A. (2006). The rapid extraction of numeric meaning. Vision Research, 46, 1559–1573.  https://doi.org/10.1016/j.visres.2005.11.015 CrossRefGoogle Scholar
  18. Dakin, S. C., & Watt, R. J. (1997). The computation of orientation statistics from visual texture. Vision Research, 37, 3181–3192.  https://doi.org/10.1016/S0042-6989(97)00133-8 CrossRefGoogle Scholar
  19. de Fockert, J. W., & Marchant, A. P. (2008). Attention modulates set representation by statistical properties. Perception & Psychophysics, 70, 789–794.  https://doi.org/10.3758/PP.70.5.789 CrossRefGoogle Scholar
  20. de Gardelle, V., & Summerfield, C. (2011). Robust averaging during perceptual judgment. Proceedings of the National Academy of Sciences, 108, 13341–13346.  https://doi.org/10.1073/pnas.1104517108 CrossRefGoogle Scholar
  21. Duncan, J., Ward, R., & Shapiro, K. (1994). Direct measurement of attentional dwell time in human vision. Nature, 369, 313–315.  https://doi.org/10.1038/369313a0 CrossRefGoogle Scholar
  22. Fan, J. E., Turk-Browne, N. B., & Taylor, J. A. (2016). Error-driven learning in statistical summary perception. Journal of Experimental Psychology: Human Perception and Performance, 42, 266–280.  https://doi.org/10.1037/xhp0000132 Google Scholar
  23. Haberman, J., & Whitney, D. (2007). Rapid extraction of mean emotion and gender from sets of faces. Current Biology, 17, R751–R753.  https://doi.org/10.1016/j.cub.2007.06.039 CrossRefGoogle Scholar
  24. Haberman, J., & Whitney, D. (2009). Seeing the mean: Ensemble coding for sets of faces. Journal of Experimental Psychology: Human Perception and Performance, 35, 718–734.  https://doi.org/10.1037/a0013899 Google Scholar
  25. Haberman, J., & Whitney, D. (2010). The visual system discounts emotional deviants when extracting average expression. Attention, Perception, & Psychophysics, 72, 1825–1838.  https://doi.org/10.3758/APP.72.7.1825 CrossRefGoogle Scholar
  26. Im, H. Y., Park, W. J., & Chong, S. C. (2015). Ensemble statistics as units of selection. Journal of Cognitive Psychology, 27, 114–127.  https://doi.org/10.1080/20445911.2014.985301 CrossRefGoogle Scholar
  27. Kanaya, S., Hayashi, M. J., & Whitney, D. (2018). Exaggerated groups: Amplification in ensemble coding of temporal and spatial features. Proceedings of the Royal Society B, 285, 20172770:1–9,  https://doi.org/10.1098/rspb.2017.2770 Google Scholar
  28. Kimura, E. (2018). Averaging colors of multicolor mosaics. Journal of the Optical Society of America A, 35, B43–B54.  https://doi.org/10.1364/JOSAA.35.000B43 CrossRefGoogle Scholar
  29. Kleiner, M., Brainard, D., & Pelli, D. (2007). What’s new in Psychtoolbox-3? Perception, 36(ECVP Abstract Suppl.), 14.Google Scholar
  30. Kuriki, I. (2004). Testing the possibility of average-color perception from multi-colored patterns. Optical Review, 11, 249–257.  https://doi.org/10.1007/s10043-004-0249-2 CrossRefGoogle Scholar
  31. Lau, J. S.-H., & Brady, T. F. (2018). Ensemble statistics accessed through proxies: Range heuristic and dependence on low-level properties in variability discrimination. Journal of Vision, 18(9), 3:1–18,  https://doi.org/10.1167/18.9.3 CrossRefGoogle Scholar
  32. 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, 245–250.  https://doi.org/10.1016/j.actpsy.2012.11.002 CrossRefGoogle Scholar
  33. Maule, J., & Franklin, A. (2015). Effects of ensemble complexity and perceptual similarity on rapid averaging of hue. Journal of Vision, 15(4), 6:1–18.  https://doi.org/10.1167/15.4.6 CrossRefGoogle Scholar
  34. Maule, J., & Franklin, A. (2016). Accurate rapid averaging of multihue ensembles is due to a limited capacity subsampling mechanism. Journal of the Optical Society of America A, 33, A22–A29.  https://doi.org/10.1364/JOSAA.33.000A22 CrossRefGoogle Scholar
  35. Milojevic, Z., Ennis, R., Toscani, M., & Gegenfurtner, K. R. (2018). Categorizing natural color distributions. Vision Research, 151, 18–30.  https://doi.org/10.1016/j.visres.2018.01.008 CrossRefGoogle Scholar
  36. Myczek, K., & Simons, D. (2008). Better than average: Alternatives to statistical summary representations for rapid judgments of average size. Perception & Psychophysics, 70, 772–788.  https://doi.org/10.3758/pp.70.5.772
  37. Nam, J.-H., & Chubb, C. (2000). Texture luminance judgments are approximately veridical. Vision Research, 40, 1695–1709.  https://doi.org/10.1016/S0042-6989(00)00006-7 CrossRefGoogle Scholar
  38. Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4, 739–744.  https://doi.org/10.1038/89532 CrossRefGoogle Scholar
  39. Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437–442.  https://doi.org/10.1163/156856897X00366 CrossRefGoogle Scholar
  40. Sakuma, N., Kimura, E., & Goryo, K. (2017). Rapid proportion comparison with spatial arrays of frequently used meaningful visual symbols. Quarterly Journal of Experimental Psychology, 70, 2371–2385.  https://doi.org/10.1080/17470218.2016.1239747 CrossRefGoogle Scholar
  41. Schütt, H. H., Harmeling, S., Macke, J. H., & Wichmann, F. A. (2016). Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research, 122, 105–123.  https://doi.org/10.1016/j.visres.2016.02.002 CrossRefGoogle Scholar
  42. Shapley, R., & Enroth-Cugell, C. (1984). Visual adaptation and retinal gain controls. In N. Osborne & G. Chader (Eds.), Progress in retinal research (Vol. 3, pp. 263–346). Oxford, UK: Pergamon Press.Google Scholar
  43. Silva, A. E., & Chubb, C. (2014). The 3-dimensional, 4-channel model of human visual sensitivity to grayscale scrambles. Vision Research, 101, 94–107.  https://doi.org/10.1016/j.visres.2014.06.001 CrossRefGoogle Scholar
  44. Simons, D. J., & Myczek, K. (2008). Average size perception and the allure of a new mechanism. Perception & Psychophysics, 70, 1335–1336.  https://doi.org/10.3758/PP.70.7.1335 CrossRefGoogle Scholar
  45. Solomon, J. A., Morgan, M., & Chubb, C. (2011). Efficiencies for the statistics of size discrimination. Journal of Vision, 11(12), 13:1–11, https://www.ncbi.nlm.nih.gov/pubmed/22011381,  https://doi.org/10.1167/11.12.13
  46. Stevens, J. C., Mack, J. D., & Stevens, S. S. (1960). Growth of sensation on seven continua as measured by force of handgrip. Journal of Experimental Psychology, 59, 60–67.  https://doi.org/10.1037/h0040746 CrossRefGoogle Scholar
  47. Stevens, S. S. (1975). Psychophysics: Introduction to its perceptual, neural, and social prospects. New York, NY: Wiley.Google Scholar
  48. Sunaga, S., & Yamashita, Y. (2007). Global color impressions of multicolored textured patterns with equal unique hue elements. Color Research and Application, 32, 267–277.  https://doi.org/10.1002/col.20330 CrossRefGoogle Scholar
  49. 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: Human Perception and Performance, 39, 329–337.  https://doi.org/10.1037/a0028712 Google Scholar
  50. Toscani, M., Gegenfurtner, K. R., & Valsecchi, M. (2017). Foveal to peripheral extrapolation of brightness within objects. Journal of Vision, 17(9), 14:1–14.  https://doi.org/10.1167/17.9.14 CrossRefGoogle Scholar
  51. Toscani, M., Valsecchi, M., & Gegenfurtner, K. R. (2013a). Optimal sampling of visual information for lightness judgments. Proceedings of the National Academy of Sciences, 110, 11163–11168.  https://doi.org/10.1073/pnas.1216954110 CrossRefGoogle Scholar
  52. Toscani, M., Valsecchi, M., & Gegenfurtner, K. R. (2013b). Selection of visual information for lightness judgements by eye movements. Philosophical Transactions of the Royal Society B, 368, 20130056.  https://doi.org/10.1098/rstb.2013.0056 CrossRefGoogle Scholar
  53. Treisman, A., & Gormican, S. (1988). Feature analysis in early vision: Evidence from search asymmetries. Psychological Review, 95, 15–48.  https://doi.org/10.1037/0033-295X.95.1.15 CrossRefGoogle Scholar
  54. 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 CrossRefGoogle Scholar
  55. Van Opstal, F., de Lange, F. P., & Dehaene, S. (2011). Rapid parallel semantic processing of numbers without awareness. Cognition, 120, 136–147.  https://doi.org/10.1016/j.cognition.2011.03.005 CrossRefGoogle Scholar
  56. Watamaniuk, S. N. J., & Duchon, A. (1992). The human visual system averages speed information. Vision Research, 32, 931–941.  https://doi.org/10.1016/0042-6989(92)90036-I CrossRefGoogle Scholar
  57. Watamaniuk, S. N. J., Sekuler, R., & Williams, D. W. (1989). Direction perception in complex dynamic displays: The integration of direction information. Vision Research, 29, 47–59.  https://doi.org/10.1016/0042-6989(89)90173-9 CrossRefGoogle Scholar
  58. Webster, J., Kay, P., & Webster, M. A. (2014). Perceiving the average hue of color arrays. Journal of the Optical Society of America A, 31, A283–A292.  https://doi.org/10.1364/JOSAA.31.00A283 CrossRefGoogle Scholar
  59. Whitney, D., Haberman, J., & Sweeny, T. D. (2014). From textures to crowds: Multiple levels of summary statistical perception. In J. S. Werner & L. M. Chalupa (Eds.), The new visual neurosciences (pp. 695–709). Cambridge, MA: MIT Press.Google Scholar
  60. Wolfe, J. M. (2003). Moving towards solutions to some enduring controversies in visual search. Trends in Cognitive Sciences, 7, 70–76.  https://doi.org/10.1016/S1364-6613(02)00024-4 CrossRefGoogle Scholar
  61. Wollschläger, D., & Anderson, B. L. (2009). The role of layered scene representations in color appearance. Current Biology, 19, 430–435.  https://doi.org/10.1016/j.cub.2009.01.053 CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Graduate School of Science and EngineeringChiba UniversityChiba-shiJapan
  2. 2.Department of Psychology, Graduate School of HumanitiesChiba UniversityChiba-shiJapan

Personalised recommendations