Advertisement

Do target detection and target localization always go together? Extracting information from briefly presented displays

  • Ann J. CarriganEmail author
  • Susan G. Wardle
  • Anina N. Rich
Article

Abstract

The human visual system is capable of processing an enormous amount of information in a short time. Although rapid target detection has been explored extensively, less is known about target localization. Here we used natural scenes and explored the relationship between being able to detect a target (present vs. absent) and being able to localize it. Across four presentation durations (~ 33–199 ms), participants viewed scenes taken from two superordinate categories (natural and manmade), each containing exemplars from four basic scene categories. In a two-interval forced choice task, observers were asked to detect a Gabor target inserted in one of the two scenes. This was followed by one of two different localization tasks. Participants were asked either to discriminate whether the target was on the left or the right side of the display or to click on the exact location where they had seen the target. Targets could be detected and localized at our shortest exposure duration (~ 33 ms), with a predictable improvement in performance with increasing exposure duration. We saw some evidence at this shortest duration of detection without localization, but further analyses demonstrated that these trials typically reflected coarse or imprecise localization information, rather than its complete absence. Experiment 2 replicated our main findings while exploring the effect of the level of “openness” in the scene. Our results are consistent with the notion that when we are able to extract what objects are present in a scene, we also have information about where each object is, which provides crucial guidance for our goal-directed actions.

Keywords

Visual perception Scene perception Object recognition 

Notes

References

  1. Adamo, S. H., Cain, M. S., & Mitroff, S. R. (2015). Targets need their own personal space: Effects of clutter on multiple-target search accuracy. Perception, 44, 1203–1214.  https://doi.org/10.1177/0301006615594921 Google Scholar
  2. Asher, M. F., Tolhurst, D. J., Troscianko, T., & Gilchrist, I. D. (2013). Regional effects of clutter on human target detection performance. Journal of Vision, 13, 1–15.  https://doi.org/10.1167/13.5.25 Google Scholar
  3. Borji, A., & Itti, L. (2013). State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence,35, 185–207.Google Scholar
  4. Brennan, P. C., Gandomkar, Z., Ekpo, E. U., Tapia, K., Trieu, P. D., Lewis, S. J., . . . Evans, K. K. (2018). Radiologists can detect the “gist” of breast cancer before any overt signs of cancer appear. Scientific Reports, 8, 8717.Google Scholar
  5. Carrigan, A. J., Wardle, S. G., & Rich, A. N. (2018). Finding cancer in mammograms: If you know it’s there, do you know where? Cognitive Research: Principles and Implications, 3.  https://doi.org/10.1186/s41235-018-0096-5
  6. Davenport, J. L., & Potter, M. C. (2004). Scene consistency in object and background perception. Psychological Science, 15, 559–564.  https://doi.org/10.1111/j.0956-7976.2004.00719.x Google Scholar
  7. Drew, T., Evans, K., Võ, M. L.-H., Jacobson, F. L., & Wolfe, J. M. (2013). Informatics in radiology: What can you see in a single glance and how might this guide visual search in medical images? RadioGraphics, 33, 263–274.  https://doi.org/10.1148/rg.331125023 Google Scholar
  8. Evans, K. K., Georgian-Smith, D., Tambouret, R., Birdwell, R. L., & Wolfe, J. M. (2013). The gist of the abnormal: Above-chance medical decision making in the blink of an eye. Psychonomic Bulletin & Review, 20, 1170–1175.  https://doi.org/10.3758/s13423-013-0459-3 Google Scholar
  9. Evans, K. K., Haygood, T. M., Cooper, J., Culpan, A.-M., & Wolfe, J. M. (2016). A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast. Proceedings of the National Academy of Sciences, 113, 10292–10297.  https://doi.org/10.1073/pnas.1606187113 Google Scholar
  10. Greene, M. R., & Oliva, A. (2009). Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive Psychology, 58, 137–176.  https://doi.org/10.1016/j.cogpsych.2008.06.001 Google Scholar
  11. Henderson, J. M., Chanceaux, M., & Smith, T. J. (2009). The influence of clutter on real-world scene search: Evidence from search efficiency and eye movements. Journal of Vision, 9(1):1–8.  https://doi.org/10.1167/9.1.32 Google Scholar
  12. Howe, P. D. L., & Webb, M. E. (2014). Detecting unidentified changes. PLoS ONE, 9, e84490.  https://doi.org/10.1371/journal.pone.0084490 Google Scholar
  13. Joubert, O. R., Rousselet, G. A., Fize, D., & Fabre-Thorpe, M. (2007). Processing scene context: Fast categorisation and object interference. Vision Research, 47, 3286–3297.Google Scholar
  14. Kleiner, M., Brainard, D., & Pelli, D. (2007). What’s new in Psychtoolbox-3? Perception 36(ECVP Abstract Suppl), 14.Google Scholar
  15. Kundel, H. L., & Nodine, C. F. (1975). Interpreting chest radiographs without visual search. Radiology, 116, 527–532.Google Scholar
  16. Loftus, G. R., & Mackworth, N. H. (1978). Cognitive determinants of fixation location during picture viewing. Journal of Experimental Psychology: Human Perception and Performance, 4, 565–572.  https://doi.org/10.1037/0096-1523.4.4.565 Google Scholar
  17. Mitroff, S. R., & Simons, D. J. (2002). Changes are not localized before they are explicitly detected. Visual Cognition, 9, 937–968.  https://doi.org/10.1080/13506280143000476 Google Scholar
  18. Oliva, A. (2005). Gist of the scene. Neurobiology of Attention, 696, 251–258.Google Scholar
  19. Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene. A holistic representation of the spatial envelope. International Journal of Computer Vision, 42, 145–175. Retrieved from http://cvcl.mit.edu/database.htm Google Scholar
  20. Oliva, A., Torralba, A., Castelhano, M. S., & Henderson, J. M. (2003). Top-down control of visual attention in object detection. In Proceedings 2003 International Conference on Image Processing (Vol. 1, pp. 251–253). Piscataway, NJ: IEEE Press.  https://doi.org/10.1109/ICIP.2003.1246946
  21. Potter, M. C. (1976). Short-term conceptual memory for pictures. Journal of Experimental Psychology: Human Learning and Memory, 2, 509–522.  https://doi.org/10.1037/0278-7393.2.5.509 Google Scholar
  22. Potter, M. C., & Faulconer, B. A. (1975). Time to understand pictures and words. Nature, 253, 437–438.Google Scholar
  23. Rensink, R. A., O’Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention to perceive changes in scenes. Psychological Science, 8, 368–373.  https://doi.org/10.1111/j.1467-9280.1997.tb00427.x Google Scholar
  24. Rosenholtz, R., Li, Y., Mansfield, J., & Jin, Z. (2005). Feature congestion: A measure of display clutter. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 761–770). New York: ACM Press.Google Scholar
  25. Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of Vision,7, 17.  https://doi.org/10.1167/7.2.17
  26. Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520–522.  https://doi.org/10.1038/381520a0 Google Scholar
  27. VanRullen, R., & Thorpe, S. J. (2001). Is it a bird? Is it a plane? Ultra-rapid visual categorisation of natural and artefactual objects. Perception, 30, 655–688.Google Scholar
  28. 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 Google Scholar
  29. Wolfe, J. M. (1994). Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1, 202–238.  https://doi.org/10.3758/BF03200774 Google Scholar
  30. Wolfe, J. M., & Horowitz, T. S. (2017). Five factors that guide attention in visual search. Nature Human Behavior, 1, 0058.  https://doi.org/10.1038/s41562-017-0058 Google Scholar
  31. Wolfe, J. M., Võ, M. L.-H., Evans, K. K., & Greene, M. R. (2011). Visual search in scenes involves selective and nonselective pathways. Trends in Cognitive Science, 15, 77–84.  https://doi.org/10.1016/j.tics.2010.12.001 Google Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  • Ann J. Carrigan
    • 1
    • 2
    • 3
    Email author
  • Susan G. Wardle
    • 1
    • 2
  • Anina N. Rich
    • 1
    • 2
    • 3
  1. 1.Perception in Action Research Centre & Department of Cognitive ScienceMacquarie UniversitySydneyAustralia
  2. 2.ARC Centre of Excellence in Cognition & Its DisordersMacquarie UniversitySydneyAustralia
  3. 3.Centre for Elite Performance, Expertise, and TrainingMacquarie UniversitySydneyAustralia

Personalised recommendations