Advertisement

Visual search asymmetry depends on target-distractor feature similarity: Is the asymmetry simply a result of distractor rejection speed?

  • Yichi (Raven) Zhang
  • Serge OnyperEmail author
40 Years of Feature Integration: Special Issue in Memory of Anne Treisman

Abstract

Previous studies have shown that in visual search, varying the target and distractor familiarity produces a search asymmetry: Detecting a novel target among familiar distractors is more efficient than detecting a familiar target among novel distractors. One explanation is that novel targets have enhanced salience and are detected preattentively. Conversely, familiar distractors may be easier to reject. The current study postulates that target–distractor feature similarity, in addition to target or distractor familiarity, is a key determinant of visual search efficiency. The results of two experiments reveal that visual search is more efficient when distractors are familiar regardless of target familiarity, but only when the target–distractor similarity is high. When similarity is low, the visual search asymmetry disappears and the search times become highly efficient, with search slopes not different from zero regardless of target or distractor familiarity. However, although distractor familiarity plays an important role in inducing the search asymmetry, comparisons of search efficiency in target-present and target-absent trials reveal that search asymmetries cannot be explained solely by the faster speed of rejecting familiar distractors, as proposed by previous studies. Rather, distractor familiarity influences processes outside of stimulus selection, such as search monitoring and termination decisions. Competition among bottom-up item salience effects and top-down shape recognition processes is proposed to account for these findings.

Keywords

Visual search Asymmetry Familiarity Similarity Target Distractor Feature 

Notes

References

  1. Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Sciences, 16(8), 437–443.  https://doi.org/10.1016/j.tics.2012.06.010 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Buetti, S., Cronin, D. A., Madison, A. M., Wang, Z., & Lleras, A. (2016). Towards a better understanding of parallel visual processing in human vision? Evidence for exhaustive analysis of visual information. Journal of Experimental Psychology: General, 145, 672–707.  https://doi.org/10.1037/xge0000163 CrossRefGoogle Scholar
  3. Carrasco, M., Evert, D. L., Chang, I., & Katz, S. M. (1995). The eccentricity effect: Target eccentricity affects performance on conjunction searches. Perception & Psychophysics, 57, 1241–1261.CrossRefGoogle Scholar
  4. Chun, M. M., & Wolfe, J. M. (1996). Just say no: How are visual searches terminated when there is no target present? Cognitive Psychology, 30, 39–78.CrossRefGoogle Scholar
  5. Dent, K., Allen, H. A., Braithwaite, J. J., & Humphreys, G. W. (2012). Parallel distractor rejection as a binding mechanism in search. Frontiers in Psychology, 3, 278.  https://doi.org/10.3389/fpsyg.2012.00278 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96, 433–458.  https://doi.org/10.1037/0033-295X.96.3.433 CrossRefPubMedGoogle Scholar
  7. Fincannon, T., Keepber, J. R., Jentsch, F., & Curtis, M. (2013). The influence of camouflage, obstruction, familiarity and spatial ability on target identification from an unmanned ground vehicle. Ergonomics, 56(5), 739–751.  https://doi.org/10.1080/00140139.2013.771218 CrossRefPubMedGoogle Scholar
  8. Fiset, D., Blais, C., Arguin, M., Tadros, K., Éthier-Majcher, C., Bub, D., & Gosselin, F. (2009). The spatio-temporal dynamics of visual letter recognition. Cognitive Neuropsychology, 26, 23–35.CrossRefGoogle Scholar
  9. Geyer, L. H., & DeWald, C. G. (1973). Feature lists and confusion matrices. Perception & Psychophysics, 14, 471–482.CrossRefGoogle Scholar
  10. Greene, H. H., & Rayner, K. (2001). Eye movements and familiarity effects in visual search. Vision Research, 41, 3763–3773.CrossRefGoogle Scholar
  11. Horstmann, G. (2009). Visual search for affective faces: Stability and variability of search slopes with different instances? Cognition and Emotion, 23, 355–379.CrossRefGoogle Scholar
  12. Horstmann, G., Becker, S. I., Bergmann, S. I., & Burghaus, L. (2010). A reversal of the search asymmetry favouring negative schematic faces. Visual Cognition, 18, 981–1016.CrossRefGoogle Scholar
  13. Hout, M. C., & Goldinger, S. D. (2012). Incidental learning speeds visual search by lowering response thresholds, not by improving efficiency: Evidence from eye movements. Journal of Experimental Psychology: Human Perception & Performance, 38(1), 90–112.  https://doi.org/10.1037/a0023894 CrossRefGoogle Scholar
  14. Kunar, M. A., Flusberg, S., Horowitz, T. S., & Wolfe, J. M. (2007). Does contextual cuing guide the deployment of attention? Journal of Experimental Psychology: Human Perception & Performance, 33, 816–828.Google Scholar
  15. Lavie, N. (2005). Distracted and confused?: Selective attention under load. Trends in Cognitive Sciences, 9, 75–82.CrossRefGoogle Scholar
  16. Lavie, N., & Cox, S. (1997). On the efficiency of visual selective attention: Efficient visual search leads to inefficient distractor rejection. Psychological Science, 8, 395–398.CrossRefGoogle Scholar
  17. Lee, K. R., Sobel, K. V., York, A. K., & Puri, A. M. (2018). Dissociating parallel and serial processing of numerical value. Journal of Numerical Cognition, 4(2), 360–379.  https://doi.org/10.5964/jnc.v4i2.133 CrossRefGoogle Scholar
  18. Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279–281.Google Scholar
  19. Luck, S. J. (2008). Visual short-term memory. In S. J. Luck & A. Hollingworth (Eds.), Visual memory (pp. 43–85). New York, NY: Oxford University Press.CrossRefGoogle Scholar
  20. Malinowski, P., & Hübner, R. (2001). The effect of familiarity on visual-search performance: Evidence for learned basic features. Perception & Psychophysics, 63, 458–463.CrossRefGoogle Scholar
  21. Mruczek, R. B., & Sheinberg, D. L. (2005). Distractor familiarity leads to more efficient visual search for complex stimuli. Perception & Psychophysics, 67, 1016–1031.CrossRefGoogle Scholar
  22. Navalpakkam, V., & Itti, L. (2005). Modeling the influence of task on attention. Vision Research, 45, 205–231.CrossRefGoogle Scholar
  23. Purcell, B. A., Heitz, R. P., Cohen, J. Y., Schall, J. D., Logan, G. D., & Palmeri, T. J. (2010). Neurally constrained modeling of perceptual decision making. Psychological Review, 117, 1113–1143.CrossRefGoogle Scholar
  24. Qin, X. A., Koutstaal, W., & Engel, S. A. (2014). The hard-won benefits of familiarity in visual search: Naturally familiar brand logos are found faster. Attention, Perception, & Psychophysics, 76, 914–930.  https://doi.org/10.3758/APP.72.5.1267
  25. Palmer, J., Verghese, P., & Pavel, M. (2000). The psychophysics of visual search. Vision Research, 40, 1227–1268.CrossRefGoogle Scholar
  26. Rauschenberger, R., & Chu, H. (2006). The effects of stimulus rotation and familiarity in visual search. Perception & Psychophysics, 68(5), 770–775.CrossRefGoogle Scholar
  27. Reicher, G. M., Snyder, C. R. R., & Richards, J. T. (1976). Familiarity of background characters in visual scanning. Journal of Experimental Psychology: Human Perception & Performance, 2, 522–530.Google Scholar
  28. Rosenholtz, R. (2001). Search asymmetries? What search asymmetries? Perception & Psychophysics, 63, 476–489.CrossRefGoogle Scholar
  29. Rosenholtz, R., Huang, J., Raj, A., Balas, B. J., & Ilie, L. (2012). A summary statistic representation in peripheral vision explains visual search. Journal of Vision, 12(4), 1–17.  https://doi.org/10.1167/12.4.14 CrossRefGoogle Scholar
  30. Saiki, J. (2008). Stimulus-driven mechanisms underlying visual search asymmetry revealed by classification image analyses. Journal of Vision, 8(4), 30, 1–19.Google Scholar
  31. Saiki, J., Koike, T., Takahashi, K., & Inoue, T. (2005). Visual search asymmetry with uncertain targets. Journal of Experimental Psychology: Human Perception and Performance, 31(6), 1274–1287.PubMedGoogle Scholar
  32. Seidl, K. N., Peelen, M. V., & Kastner, S. (2012). Neural evidence for distracter suppression during visual search in real-world scenes. The Journal of Neuroscience, 32(34), 11812–11819.CrossRefGoogle Scholar
  33. Shasteen, J. R., Sasson, N. J., & Pinkham, A. E. (2014). Eye tracking the face in the crowd task: Why are angry faces found more quickly? PLOS ONE, 9(4), e93914.  https://doi.org/10.1371/journal.pone.0093914 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Shen, J., & Reingold, E. M. (2001). Visual search asymmetry: The influence of stimulus familiarity and low-level features. Perception & Psychophysics, 63, 464–475.CrossRefGoogle Scholar
  35. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84, 127–190.CrossRefGoogle Scholar
  36. Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97–136.  https://doi.org/10.1016/0010-0285(80)90005-5 CrossRefPubMedGoogle Scholar
  37. Treisman, A., & Gormican, S. (1988). Feature analysis in early vision: Evidence from search asymmetries. Psychological Review, 95, 15–48.CrossRefGoogle Scholar
  38. Treisman, A., & Souther, J. (1985). Search asymmetry: A diagnostic for preattentive processing of separable features. Journal of Experimental Psychology: General, 114, 285–310.CrossRefGoogle Scholar
  39. Visalli, A., & Vallesi, A. (2018). Monitoring processes in visual search enhanced by professional experience: The case of orange quality-control workers. Frontiers in Psychology, 9, 145.  https://doi.org/10.3389/fpsyg.2018.00145 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Wang, Q., Cavanagh, P., & Green, M. (1994). Familiarity and pop-out in visual search. Perception & Psychophysics, 56, 495–500.CrossRefGoogle Scholar
  41. Wang, L., Zhang, K., He, S., & Jiang, Y. (2010). Searching for life motion signals: Visual search asymmetry in local but not global biological-motion processing. Psychological Science, 21, 1083–1089.CrossRefGoogle Scholar
  42. Wolfe, J. M. (1998). What do 1,000,000 trials tell us about visual search? Psychological Science, 9, 33–39.CrossRefGoogle Scholar
  43. Wolfe, J. M. (2001). Asymmetries in visual search: An introduction. Perception & Psychophysics, 63, 381–389.CrossRefGoogle Scholar
  44. Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989). Guided search: An alternative to the feature integration model for visual search. Journal of Experimental Psychology: Human Perception & Performance, 15, 419–433.Google Scholar
  45. Wolfe, J. M., Oliva, A., Horowitz, T. S., Butcher, S. J., & Bompas, A. (2012). Segmentation of objects from backgrounds in visual search tasks. Vision Research, 42, 2985–3004.Google Scholar
  46. Woodman, G. F., Vogel, E. K., & Luck, S. J. (2001). Visual search remains efficient when visual working memory is full. Psychological Science, 12, 219–224.CrossRefGoogle Scholar
  47. Yang, H., Chen, X., & Zelinsky, G. J. (2009). A new look at novelty effects: Guiding search away from old distractors. Attention, Perception, & Psychophysics, 71, 554–564.CrossRefGoogle Scholar
  48. Zhaoping, L., & Frith, U. (2011). A clash of bottom-up and top-down processes in visual search: The reversed letter effect revisited. Journal of Experimental Psychology: Human Perception and Performance, 37(4), 997–1006.PubMedGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019
corrected publication 2019

Authors and Affiliations

  1. 1.Department of PsychologySt. Lawrence UniversityCantonUSA
  2. 2.University of MarylandCollege ParkUSA

Personalised recommendations