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

Abstract

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.

References

  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. https://doi.org/10.1037/0278-7393.27.2.514
  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. https://doi.org/10.1080/02724980543000204
  4. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10(4), 433-436. https://doi.org/10.1163/156856897X00357
  5. Bundesen, C. (1990). A theory of visual attention. Psychological review, 97(4), 523-547. https://doi.org/10.1037/0033-295X.97.4.523
  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. https://doi.org/10.1006/cogp.1996.0002
  7. Fleck, M. S., & Mitroff, S. R. (2007). Rare targets are rarely missed in correctable search. Psychological Science, 18(11), 943-947. https://doi.org/10.1111/j.1467-9280.2007.02006.x
  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. https://doi.org/10.1037/1528-3542.7.4.691
  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. https://doi.org/10.1080/13506285.2010.500605
  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. https://doi.org/10.3758/s13423-014-0686-2
  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. https://doi.org/10.1148/radiol.2281020709
  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. https://doi.org/10.3758/BF03194333
  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. https://doi.org/10.1016/j.neuroimage.2005.12.034
  16. Ratcliff, R. (1978). A theory of memory retrieval. Psychological review, 85(2), 59-108. https://doi.org/10.1037/0033-295X.85.2.59
  17. Ratcliff, R. (1979). Group reaction time distributions and an analysis of distribution statistics. Psychological Bulletin, 86(3), 446-461. https://doi.org/10.1037/0033-2909.86.3.446
  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. https://doi.org/10.1037/0096-1523.26.1.127
  19. Ratcliff, R., & Smith, P. L. (2004). A Comparison of Sequential Sampling Models for Two-Choice Reaction Time. Psychological Review, 111(2), 333-367. https://doi.org/10.1037/0033-295X.111.2.333
  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. https://doi.org/10.1016/j.intell.2006.12.002
  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. https://doi.org/10.3758/BF03195403
  23. Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive psychology, 12(1), 97-136. https://doi.org/10.1016/0010-0285(80)90005-5
  24. Treisman, A. (1999). Solutions to the binding problem: Progress through controversy and convergence. Neuron, 24(1), 105-125. https://doi.org/10.1016/S0896-6273(00)80826-0
  25. Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic bulletin & review, 7(3), 424-465. https://doi.org/10.3758/BF03214357
  26. Verghese, P. (2001). Visual search and attention: A signal detection theory approach. Neuron, 31(4), 523-535. https://doi.org/10.1016/S0896-6273(01)00392-0
  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. https://doi.org/10.3758/BF03194023
  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. https://doi.org/10.1016/j.jml.2007.04.006
  30. Wolfe, J. M. (1994). Guided search 2.0 a revised model of visual search. Psychonomic bulletin & review, 1(2), 202-238. https://doi.org/10.3758/BF03200774
  31. Wolfe, J. M. (1998). What can 1 million trials tell us about visual search?. Psychological Science, 9(1), 33-39. https://doi.org/10.1111/1467-9280.00006
  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. https://doi.org/10.1038/435439a
  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. https://doi.org/10.1037/0096-3445.136.4.623
  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. https://doi.org/10.1016/j.cub.2009.11.066
  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. https://doi.org/10.1037/0096-1523.29.1.121