Characterizing Information Processing in Visual Search According to Probability of Target Prevalence

표적 출현확률에 따른 시각탐색 정보처리 특성

  • Received : 2015.08.12
  • Accepted : 2015.09.22
  • Published : 2015.09.30


In our daily life, the probability of target prevalence in visual search varies from very low to high. However, most laboratory studies of visual search used a fixed probability of target prevalence at 50%. The present study examined the properties of information processing during visual search where the probability of target prevalence was manipulated to vary from low (20%), medium (50%), to high (80%). The search items were made of simple shape stimuli, and search accuracy, signal detection measures, and reaction times (RTs) were analyzed for characterizing the effect of target prevalence on the information processing strategies for visual search. The analyses showed that the rates of misses increased whereas those of false alarms decreased in the search condition of low target prevalence, whereas the pattern was reversed in the high prevalence condition. Signal detection measures revealed that the target prevalence shifted response criterion (c) without affecting sensitivity (d'). In addition, RTs for correct rejection responses in the target-absent trials became delayed as the prevalence increased, whereas those for hits in the target-present trials were relatively constant regardless of the prevalence. The RT delay in the target-absent trials indicates that increased target prevalence made the 'quitting threshold' for search termination more conservative. These results support an account that the target prevalence effect in visual search arises from a shift of decision criteria and the subsequent changes in search information processing, while rejecting the account of a speed-accuracy tradeoff.


  1. 박형범 & 현주석 (2014). ex-Gaussian 모형을 활용한 인지적 과제의 반응시간 분포 분석. 감성과학, 17(2), 63-76.
  2. Andrews, S., & Heathcote, A. (2001). Distinguishing common and task-specific processes in word identification: A matter of some moment?. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(2), 514-544.
  3. Blanchette, I. (2006). Snakes, spiders, guns, and syringes: How specific are evolutionary constraints on the detection of threatening stimuli?. The Quarterly Journal of Experimental Psychology, 59(8), 1484-1504.
  4. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10(4), 433-436.
  5. Bundesen, C. (1990). A theory of visual attention. Psychological review, 97(4), 523-547.
  6. Chun, M. M., & Wolfe, J. M. (1996). Just say no: How are visual searches terminated when there is no target present?. Cognitive psychology, 30(1), 39-78.
  7. Fleck, M. S., & Mitroff, S. R. (2007). Rare targets are rarely missed in correctable search. Psychological Science, 18(11), 943-947.
  8. Fox, E., Griggs, L., & Mouchlianitis, E. (2007). The detection of fear-relevant stimuli: Are guns noticed as quickly as snakes?. Emotion, 7(4), 691-696.
  9. Godwin, H. J., Menneer, T., Cave, K. R., & Donnelly, N. (2010). Dual-target search for high and low prevalence X-ray threat targets. Visual Cognition, 18(10), 1439-1463.
  10. Godwin, H. J., Menneer, T., Cave, K. R., Thaibsyah, M., & Donnelly, N. (2014). The effects of increasing target prevalence on information processing during visual search. Psychonomic bulletin & review, 22(2), 469-475.
  11. Gur, D., Rockette, H. E., Armfield, D. R., Blachar, A., Bogan, J. K., Brancatelli, G., et al. (2003). Prevalence Effect in a Laboratory Environment. Radiology, 228(1), 10-14.
  12. Macmillan, N. A., & Creelman, C. D. (2005). Detection theory: A user's guide (2nd ed.). Mahwah, NJ: Erlbaum
  13. Palmer, J., Huk, A. C., & Shadlen, M. N. (2005). The effect of stimulus strength on the speed and accuracy of a perceptual decision. Journal of vision, 5(5), 376-404.
  14. Penner-Wilger, M., Leth-Steensen, C., & LeFevre, J. A. (2002). Decomposing the problem-size effect: A comparison of response time distributions across cultures. Memory & Cognition, 30(7), 1160-1167.
  15. Pourtois, G., Schwartz, S., Seghier, M. L., Lazeyras, F., & Vuilleumier, P. (2006). Neural systems for orienting attention to the location of threat signals: an event-related fMRI study. Neuroimage, 31(2), 920-933.
  16. Ratcliff, R. (1978). A theory of memory retrieval. Psychological review, 85(2), 59-108.
  17. Ratcliff, R. (1979). Group reaction time distributions and an analysis of distribution statistics. Psychological Bulletin, 86(3), 446-461.
  18. Ratcliff, R., & Rouder, J. N. (2000). A diffusion model account of masking in two-choice letter identification. Journal of Experimental Psychology: Human perception and performance, 26(1), 127-140.
  19. Ratcliff, R., & Smith, P. L. (2004). A Comparison of Sequential Sampling Models for Two-Choice Reaction Time. Psychological Review, 111(2), 333-367.
  20. Ratcliff, R., Schmiedek, F., & McKoon, G. (2008). A diffusion model explanation of the worst performance rule for reaction time and IQ. Intelligence, 36(1), 10-17.
  21. Rich, A. N., Kunar, M. A., Van Wert, M. J., Hidalgo-Sotelo, B., Horowitz, T. S., & Wolfe, J. M. (2008). Why do we miss rare targets? Exploring the boundaries of the low prevalence effect. Journal of Vision, 8(15), 1-17.
  22. Schwarz, W. (2001). The ex-Wald distribution as a descriptive model of response times. Behavior Research Methods, Instruments, & Computers, 33(4), 457-469.
  23. Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive psychology, 12(1), 97-136.
  24. Treisman, A. (1999). Solutions to the binding problem: Progress through controversy and convergence. Neuron, 24(1), 105-125.
  25. Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic bulletin & review, 7(3), 424-465.
  26. Verghese, P. (2001). Visual search and attention: A signal detection theory approach. Neuron, 31(4), 523-535.
  27. Vincent, S. B. (1912). The function of the viborissae in the behavior of the white rat. Behavioral Monographs, 1(5).
  28. Wagenmakers, E. J., Van Der Maas, H. L., & Grasman, R. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic bulletin & review, 14(1), 3-22.
  29. Wagenmakers, E. J., Ratcliff, R., Gomez, P., & McKoon, G. (2008). A diffusion model account of criterion shifts in the lexical decision task. Journal of Memory and Language, 58(1), 140-159.
  30. Wolfe, J. M. (1994). Guided search 2.0 a revised model of visual search. Psychonomic bulletin & review, 1(2), 202-238.
  31. Wolfe, J. M. (1998). What can 1 million trials tell us about visual search?. Psychological Science, 9(1), 33-39.
  32. Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Cognitive psychology: rare items often missed in visual searches. Nature, 435(7041), 439-440.
  33. Wolfe, J. M., Horowitz, T. S., Van Wert, M. J., Kenner, N. M., Place, S. S., & Kibbi, N. (2007). Low target prevalence is a stubborn source of errors in visual search tasks. Journal of Experimental Psychology: General, 136(4), 623-638.
  34. Wolfe, J. M., & Van Wert, M. J. (2010). Varying target prevalence reveals two dissociable decision criteria in visual search. Current Biology, 20(2), 121-124.
  35. Woodman, G. F., & Luck, S. J. (2003). Serial deployment of attention during visual search. Journal of Experimental Psychology: Human Perception and Performance, 29(1), 121-138.