The Influence of Change Prevalence on Visual Short-Term Memory-Based Change Detection Performance

변화출현확률이 시각단기기억 기반 변화탐지 수행에 미치는 영향

  • Received : 2021.09.10
  • Accepted : 2021.09.10
  • Published : 2021.09.30


The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

짧은 시차를 두고 출현하는 기억과 검사배열 사이에 차이 항목의 유무를 찾아내는 변화탐지 원리는 검사배열 출현 시 기억항목들과 견주어 차이가 있는 한 항목을 탐색한다는 점에서 시각탐색 원리와 닮아있다. 본 연구는 두 과제 사이의 이러한 유사성을 배경으로, 시각단기기억 기반 변화탐지 과제에서 변화의 출현 가능성 증감이 변화탐지 반응의사결정에 미치는 영향 즉 변화출현확률 효과의 양상을 조사했다. 이를 위해 네 개의 색상 사각형에 뒤이어 출현한 또 다른 네 개의 색상 사각형 사이의 색상들을 비교해 색상 변화 항목의 유무를 판단하는 단순세부특징 변화탐지 과제를 실시했다. 변화 항목의 출현 가능성은 전체 시행 대비 20, 50 및 80% 확률로 처치되었으며 그에 따른 변화탐지 수행 오류와 탐지민감도 및 반응시간을 분석했다. 그 결과 변화 항목의 출현 가능성이 증가할수록 오경보는 증가하고 실수 반응은 감소했으며 정기각 반응시간 또한 지연된 것이 관찰되었다. 이 변화출현확률 효과는 시각탐색 과제에서 표적의 출현 가능성 증감에 따라 관찰되는 표적출현확률 효과와 매우 유사했으며 이는 두 효과를 초래하는 배경 원리가 서로 닮아있을 가능성을 시사한다.



이 논문은 2020년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업이며(NRF-2020R1F1A1071424)


  1. 강해인, 현주석 (2011). 시각작업기억 처리 단계에 따른 주의 자원 활용 특성. 한국심리학회지: 인지 및 생물, 23(4), 487-504.
  2. 박형범, 손한결, 현주석 (2015). 표적 출현확률에 따른 시각탐색 정보처리 특성 인지과학, 26(3), 357-375.
  3. 현주석 (2017). 시각작업기억 연구를 위한 변화탐지 과제의 방법론적 제약 및 이론적 시사점에 대한 고찰. 한국심리학회지: 인지 및 생물, 29(3), 287-373.
  4. Alvarez, G. A., & Cavanagh, P. (2004). The capacity of visual short-term memory is set both by information load and by number of objects. Psychological Science, 15(2), 106-111.
  5. Beanland, V., Le, R. K., & Byrne, J. E. M. (2016). Object-scene relationships vary the magnitude of target prevalence effects in visual search. Journal of Experimental Psychology: Human Perception and Performance, 42(6), 766-775.
  6. Bunting, M. (2006). Proactive interference and item similarity in working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(2), 183-196.
  7. Chun, M. M., & Jiang, Y. (1998). Contextual cueing: Implicit learning and memory of visual context guides spatial attention. Cognitive Psychology, 36(1), 28-71.
  8. 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.
  9. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87-185.
  10. Duncan, J. (1985). Visual search and selective attention. In M. I. P. a. O. S. M. Marin (Ed.), Attention and Performance (Vol. XI, pp. 85-106). Hillsdale: Erlbaum.
  11. Duncan, J., & Humphreys, G. (1989). Visual search and stimulus similarity. Psychological Review, 96, 433-458.
  12. Eckstein, M. P., Thomas, J. P., Palmer, J., & Shimozaki, S. S. (2000). A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays. Perception and Psychophysics, 62, 425-451.
  13. Fleck, M. S., & Mitroff, S. R. (2007). Rare targets are rarely missed in a correctable search. Psychological Science, 18(11), 943-947.
  14. Gepshtein, S., Lesmes, L. A., & Albright, T. D. (2013). Sensory adaptation as optimal resource allocation. Procedings of the National Academy of Sciences, 110(11), 4368-4373.
  15. Girden, E. (1992). ANOVA: Repeated mesures. Newbury Park: CA: Sage.
  16. Godwin, H. J., Menneer, T., Cave, K. R., Thaibsyah, M., & Donnelly, N. (2010). Dual-target search for high and low prevalence X-ray threat targets. Visual Cognition, 18(10), 1439-1463.
  17. 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.
  18. Godwin, H. J., Walenchok, S. C., Houpt, J. W., & Hout, M. C. (2015). Faster than the speed of rejection: Object identification processes during visual search for multiple targets. Journal of Experimental Psychology: Human Perception & Performance, 41(4), 1007-1020.
  19. Gur, D., Rockette, H. E., Armfield, D. R., Blachar, A., Bogan, J. K., Brancetelli, G., . . . Warfel, T. E. (2003). The prevalence effect in a laboratory environment. Radiology, 228, 10-14.
  20. Hartshorne, J. K. (2008). Visual working memory capacity and proactive interference. PLoS One, 3(7): e2716. Retrieved from
  21. Harvey Jr., L. O. (2003). Detection sensitivity and response bias. Department of Psychology, University of Colorado.
  22. Hollingworth, A. (2003). Failures of retrieval and comparison constrain change detection in natural scenes. Journal of Experimental Psychology: Human Perception and Performance, 29(2), 388-403.
  23. Hyun, J.-S., Woodman, G. F., Vogel, E. K., Hollingworth, A., & Luck, S. J. (2009). The comparison of visual working memory representations with perceptual inputs. Journal of Experimental Psychology: Human Perception and Performance, 35(4), 1140-1160.
  24. Ishibashi, K., Kita, S., & Wolfe, J. M. (2011). The effects of local prevalence and explicit expectations on search termination times. Attention, Perception & Psychophysics, 74, 115-123.
  25. Jiang, Y., Zhou, K., & He, S. (2007). Human visual cortex responds to invisible chromatic flicker. Nature Neuroscience, 10, 657-662.
  26. Kane, M. J., & Engle, R. W. (2000). Working-memory capacity, proactive interference and divided attention: Limits on long-term memory retrieval. Journal of Experimental Psychology: Learning, Memory & Cognition, 26(2), 336-358.
  27. Lau, J. S. H., & Haung, L. (2010). The prevalence effect is determined by past experience, not future prospects. Vision Research, 50(15), 1469-1474.
  28. Luck, S. J. (2008). Visual short-term memory. In S. J. Luck & A. Hollingworth (Eds.), Visual Memory: Oxford University Press.
  29. Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279-281.
  30. Macmillan, N. A. (1993). Singal detection theory as data analysis method and psychological decision model.
  31. Macmillan, N. A., & Creelman, C. D. (2004). Detection theory: A user's guide: Psychology Press.
  32. O'Regan, J. K., Rensink, R. A., & Clark, J. J. (1999). Change-blindness as a result of "mudsplashes". Nature, 398(6722), 34.
  33. Palmer, J., Verghese, P., & Pavel, M. (2000). The psychophysics of visual search. Vision Research, 40, 1227-1268.
  34. Peterson, M. S., Kramer, A. F., Wang, R. F., Irwin, D. E., & McCarley, J. S. (2001). Visual search has memory. Psychological Science, 12, 287-292.
  35. Rensink, R. A. (2000). Visual search for change: A probe into the nature of attentional processing. Visual Cognition, 7, 345-376.
  36. Rensink, R. A. (2002). Change detection. Annual Review of Psychology, 53, 245-277.
  37. 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(5), 368-373.
  38. 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.
  39. Rouder, J. N., Morey, R. D., Morey, C. C., & Cowan, N. (2011). How to measure working memory capacity in the change detection paradigm. Psychonomic Bulletin & Review, 18(2), 324-330.
  40. Schankin, A., Bergmann, K., Schubert, A.-L., & Hagemann, D. (2017). The allocation of attention in change detection and change blindness. Journal of Psychophysiology, 31(3), 94-106.
  41. Schwark, J., Sandray, J., & Dolgov, I. (2013). Evidence for a Positive Relationship between Working-Memory Capacity and Detection of Low-Prevalence Targets in Visual Search. Perception, 42(1), 112-114.
  42. Simons, D. J., & Rensink, R. A. (2005). Change blindness: Past, present, and future. Trends in Cognitive Sciences, 9(1), 16-20.
  43. Stanislaw, H., & Todorov, N. (1999). Calculation of signal detection theory measures. Behavior Research Methods, Instruments, & Computers, 31(1), 137-149.
  44. van Lamsweerde, A. E., & Beck, M. R. (2011). The change probability effect: Incidental learning, adaptability, and shared visual working memory resources. Consciousness & Cognition, 20(4), 1676-1689.
  45. Vickery, T. J., King, L.-W., & Jiang, Y. (2005). Setting up the target template in visual search. Journal of Vision, 5, 81-92.
  46. Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature, 438, 500-503.
  47. Vogel, E. K., Woodman, G. F., & Luck, S. J. (2001). Storage of features, conjunctions and objects in visual working memory. Journal of Experimental Psychology: Human Perception & Performance, 27(1), 92-114.
  48. Vogel, E. K., Woodman, G. F., & Luck, S. J. (2006). The time course of consolidation in visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 32(6), 1436-1451.
  49. von Grunau, M. W., Faubert, J., lordanova, M., & Rajska, D. (1999). Flicker and the efficiency of cues for capturing attention. Vision Research, 39(19), 3241-3252.
  50. Webster, M. A. (2012). Evolving concepts of sensory adaptation. F1000 Biology Reports, 4, 21. Retrieved from
  51. Webster, M. A. (2015). Visual adaptation. Annual Review of Vision Science, 1, 547-567.
  52. Wickens, T. D. (2002). Elementary signal detection theory. U.S.A.: Oxford University Press.
  53. Wolfe, J. M. (1994). Guided search 2.0: A revised model of visual search. Psychonomic Bulletin & Review, 1, 202-238.
  54. Wolfe, J. M. (1998). Visual search. In H. Pashler (Ed.), Attention (pp. 13-73). Hove, England UK: Psychology Press/Erlbaum (Uk) Taylor & Francis.
  55. Wolfe, J. M., Horowitz, T. S., & Kenner, N. M. (2005). Rare items often missed in visual searches. Nature, 435, 439-440.
  56. 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, 623-638.
  57. 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.
  58. Woodman, G. F., & Chun, M. M. (2006). The role of working memory long-term memory in visual search. Visual Cognition, 14(4-8), 808-830.