Advertisement

Behavior Research Methods

, Volume 50, Issue 4, pp 1686–1693 | Cite as

Correcting “confusability regions” in face morphs

  • Emma ZeeAbrahamsen
  • Jason Haberman
Article

Abstract

The visual system represents summary statistical information from a set of similar items, a phenomenon known as ensemble perception. In exploring various ensemble domains (e.g., orientation, color, facial expression), researchers have often employed the method of continuous report, in which observers select their responses from a gradually changing morph sequence. However, given their current implementation, some face morphs unintentionally introduce noise into the ensemble measurement. Specifically, some facial expressions on the morph wheel appear perceptually similar even though they are far apart in stimulus space. For instance, in a morph wheel of happy–sad–angry–happy expressions, an expression between happy and sad may not be discriminable from an expression between sad and angry. Without accounting for this confusability, observer ability will be underestimated. In the present experiments we accounted for this by delineating the perceptual confusability of morphs of multiple expressions. In a two-alternative forced choice task, eight observers were asked to discriminate between anchor images (36 in total) and all 360 facial expressions on the morph wheel. The results were visualized on a “confusability matrix,” depicting the morphs most likely to be confused for one another. The matrix revealed multiple confusable images between distant expressions on the morph wheel. By accounting for these “confusability regions,” we demonstrated a significant improvement in performance estimation on a set of independent ensemble data, suggesting that high-level ensemble abilities may be better than has been previously thought. We also provide an alternative computational approach that may be used to determine potentially confusable stimuli in a given morph space.

Keywords

Ensemble perception Faces Morphs Discriminability 

References

  1. Alvarez, G. A., & Oliva, A. (2008). The representation of simple ensemble visual features outside the focus of attention. Psychological Science, 19, 392–398.  https://doi.org/10.1111/j.1467-9280.2008.02098.x CrossRefPubMedPubMedCentralGoogle Scholar
  2. Andrews, D. (1967). Perception of contour orientation in the central fovea part I: Short lines. Vision Research, 7, 975–997.CrossRefPubMedGoogle Scholar
  3. Ariely, D. (2001). Seeing sets: Representation by statistical properties. Psychological Science, 12, 157–162.CrossRefPubMedGoogle Scholar
  4. Attarha, M., Moore, C. M., & Vecera, S. P. (2014). Summary statistics of size: Fixed processing capacity for multiple ensembles but unlimited processing capacity for single ensembles. Journal of Experimental Psychology: Human Perception and Performance, 40, 1440–1449.  https://doi.org/10.1037/a0036206 PubMedGoogle Scholar
  5. Berens, P. (2009). CircStat: A MATLAB toolbox for circular statistics. Journal of Statistical Software, 31(10), 1–21.  https://doi.org/10.18637/jss.v031.i10 CrossRefGoogle Scholar
  6. Brady, T. F., & Alvarez, G. A. (2011). Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items. Psychological Science, 22, 384–392.  https://doi.org/10.1177/0956797610397956 CrossRefPubMedGoogle Scholar
  7. Cant, J. S., & Xu, Y. (2012). Object ensemble processing in human anterior-medial ventral visual cortex. Journal of Neuroscience, 32, 7685–7700.CrossRefPubMedGoogle Scholar
  8. Demeyere, N., Rzeskiewicz, A., Humphreys, K. A., & Humphreys, G. W. (2008). Automatic statistical processing of visual properties in simultanagnosia. Neuropsychologia, 46, 2861–2864.CrossRefPubMedGoogle Scholar
  9. Emmanouil, T. A., & Treisman, A. (2008). Dividing attention across feature dimensions in statistical processing of perceptual groups. Perception & Psychophysics, 70, 946–954.  https://doi.org/10.3758/PP.70.6.946 CrossRefGoogle Scholar
  10. Fischer, J., & Whitney, D. (2014). Serial dependence in visual perception. Nature Neuroscience, 17, 738–743.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Folstein, J. R., Gauthier, I., & Palmeri, T. J. (2012). How category learning affects object representations: Not all morphspaces stretch alike. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38, 807–820.  https://doi.org/10.1037/a0025836 PubMedGoogle Scholar
  12. Haberman, J., Brady, T. F., & Alvarez, G. A. (2015a). Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation. Journal of Experimental Psychology: General, 144, 432–446.  https://doi.org/10.1037/xge0000053 CrossRefGoogle Scholar
  13. Haberman, J., Lee, P., & Whitney, D. (2015b). Mixed emotions: Sensitivity to facial variance in a crowd of faces. Journal of Vision, 15(4), 16.  https://doi.org/10.1167/15.4.16 CrossRefPubMedGoogle Scholar
  14. Haberman, J., & Whitney, D. (2007). Rapid extraction of mean emotion and gender from sets of faces. Current Biology, 17, R751–R753.CrossRefPubMedGoogle Scholar
  15. 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
  16. Haberman, J., & Whitney, D. (2011). Efficient summary statistical representation when change localization fails. Psychonomic Bulletin & Review, 18, 855–859.CrossRefGoogle Scholar
  17. Leib, A. Y., Puri, A. M., Fischer, J., Bentin, S., Whitney, D., & Robertson, L. (2012). Crowd perception in prosopagnosia. Neuropsychologia, 50, 1698–1707.CrossRefPubMedGoogle Scholar
  18. Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska Directed Emotional Faces (KDEF) (CD ROM). Stockholm: Karolinska Institutet, Department of Clinical Neuroscience, Psychology section.Google Scholar
  19. Manassi, M., Liberman, A., Chaney, W., & Whitney, D. (2017). The perceived stability of scenes: Serial dependence in ensemble representations. Scientific Reports, 7, 1971.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Margalit, E., Biederman, I., Herald, S. B., Yue, X., & von der Malsburg, C. (2016). An applet for the Gabor similarity scaling of the differences between complex stimuli. Attention, Perception, & Psychophysics, 78, 2298–2306.  https://doi.org/10.3758/s13414-016-1191-7 CrossRefGoogle Scholar
  21. Simons, D. J., & Levin, D. T. (1998). Failure to detect changes to people during a real-world interaction. Psychonomic Bulletin & Review, 5, 644–649.  https://doi.org/10.3758/BF03208840 CrossRefGoogle Scholar
  22. Suchow, J. W., Brady, T. F., Fougnie, D., & Alvarez, G. A. (2013). Modeling visual working memory with the MemToolbox. Journal of Vision, 13(10), 9.  https://doi.org/10.1167/13.10.9 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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. 685–709). Cambridge, MA: MIT Press.Google Scholar
  24. Whitney, D., & Levi, D. M. (2011). Visual crowding: A fundamental limit on conscious perception and object recognition. Trends in Cognitive Sciences, 15, 160–168.  https://doi.org/10.1016/j.tics.2011.02.005 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Yue, X., Biederman, I., Mangini, M. C., von der Malsburg, C., & Amir, O. (2012). Predicting the psychophysical similarity of faces and non-face complex shapes by image-based measures. Vision Research, 55, 41–46.CrossRefPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2018

Authors and Affiliations

  1. 1.Department of PsychologyRhodes CollegeMemphisUSA

Personalised recommendations