Object-based biased competition during covert spatial orienting

  • Miranda ScolariEmail author
  • Edward Awh


Biased-competition models assert that spatial attention facilitates visual perception by biasing competitive interactions in favor of relevant input. In line with this view, past work has shown that the benefits of covert spatial attention are greatest when targets must compete with interfering stimuli. Here we propose a boundary condition for the resolution of interference via exogenous attention: Attention resolves visual interference between targets and distractors, but only when they can be individuated into distinct representations. Thus, we propose that biased competition may be object-based. We replicated previous observations of larger attention effects when targets were flanked by irrelevant distractors (interference-present displays) than when targets were presented alone (interference-absent displays). Critically, we then showed that this amplification of cueing effects in the presence of interference is eliminated when strong crowding hampers individuation of the targets and distractors. Likewise, when targets were embedded within a noise mask that did not evoke the percept of an individuated distractor, the attention effects were equivalent across noise and lone-target displays. Thus, we conclude that exogenous spatial attention resolves interference in an object-based fashion that depends on the perception of individuated targets and distractors.


Space-based attention Object-based attention 


Supplementary material

13414_2018_1656_MOESM1_ESM.docx (30 kb)
ESM 1 (DOCX 30 kb)
13414_2018_1656_MOESM2_ESM.docx (154 kb)
ESM 2 (DOCX 154 kb)


  1. Abrams, R. A., & Law, M. B. (2000). Object-based visual attention with endogenous orienting. Attention, Perception, & Psychophysics, 62, 818–833. CrossRefGoogle Scholar
  2. Agaoglu, M. N., & Chung, S. T. (2016). Can (should) theories of crowding be unified? Journal of Vision, 16(15), 10. CrossRefGoogle Scholar
  3. Awh, E., Matsukura, M., & Serences, J. T. (2003). Top-down control over biased competition during covert spatial orienting. Journal of Experimental Psychology: Human Perception and Performance, 29, 52–63. Google Scholar
  4. Awh, E., Sgarlata, A. M., & Kliestik, J. (2005). Resolving visual interference during covert spatial orienting: Online attentional control through static records of prior visual experience. Journal of Experimental Psychology: General, 134, 192–206.CrossRefGoogle Scholar
  5. Baylis, G. C., & Driver, J. (1992). Visual parsing and response competition: The effect of grouping factors. Perception & Psychophysics, 51, 145–162. CrossRefGoogle Scholar
  6. Baylis, G. C., & Driver, J. (1993). Visual attention and objects: Evidence for hierarchical coding of location. Journal of Experimental Psychology: Human Perception and Performance, 19, 451–470. Google Scholar
  7. Beck, D. M., & Kastner, S. (2005). Stimulus context modulates competition in human extrastriate cortex. Nature Neuroscience, 8, 1110–1116. CrossRefGoogle Scholar
  8. Beck, D. M., & Kastner, S. (2009). Top-down and bottom-up mechanisms in biasing competition in the human brain. Vision Research, 49, 1154–1165. CrossRefGoogle Scholar
  9. Bles, M., Schwarzbach, J., De Weerd, P., Goebel, R., & Jansma, B. M. (2006). Receptive field size-dependent attention effects in simultaneously presented stimulus displays. NeuroImage, 30, 506–511. CrossRefGoogle Scholar
  10. Bouma, H. (1970). Interaction effects in parafoveal letter recognition. Nature, 226, 177–178.CrossRefGoogle Scholar
  11. Bouma, H. (1973). Visual interference in the parafoveal recognition of initial and final letters of words. Vision Research, 13, 767–782.CrossRefGoogle Scholar
  12. Carrasco, M. (2011). Visual attention: The past 25 years. Vision Research, 51, 1484–1525. CrossRefGoogle Scholar
  13. Cheal, M. L., & Gregory, M. (1997). Evidence of limited capacity and noise reduction with single-element displays in the location-cuing paradigm. Journal of Experimental Psychology: Human Perception and Performance, 23, 51–71.Google Scholar
  14. Chen, N., Bao, P., & Tjan, B. S. (2018). Contextual-dependent attention effect on crowded orientation signals in human visual cortex. Journal of Neuroscience, 38, 8433–8440.CrossRefGoogle Scholar
  15. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201–215.CrossRefGoogle Scholar
  16. Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222. CrossRefGoogle Scholar
  17. Dosher, B. A., & Lu, Z.-L. (2000). Noise exclusion in spatial attention. Psychological Science, 11, 139–146. CrossRefGoogle Scholar
  18. Duncan, J. (1984). Selective attention and the organization of visual information. Journal of Experimental Psychology: General, 113, 501–517. CrossRefGoogle Scholar
  19. Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96, 433–458. CrossRefGoogle Scholar
  20. Egly, R., Driver, J., & Rafal, R. D. (1994). Shifting visual attention between objects and locations: evidence from normal and parietal lesion subjects. Journal of Experimental Psychology: General, 123, 161–177. CrossRefGoogle Scholar
  21. Ester, E. F., Klee, D., & Awh, E. (2014). Visual crowding cannot be wholly explained by feature pooling. Journal of Experimental Psychology: Human Perception and Performance, 40, 1022–1033. Google Scholar
  22. Ester, E. F., Zilber, E., & Serences, J. T. (2015). Substitution and pooling in visual crowding induced by similar and dissimilar distractors. Journal of Vision, 15(1), 4. CrossRefGoogle Scholar
  23. Gheri, C., & Baldassi, S. (2008). Non-linear integration of crowded orientation signals. Vision Research, 48, 2352–2358.CrossRefGoogle Scholar
  24. Greenwood, J. A., Bex, P. J., & Dakin, S. C. (2009). Positional averaging explains crowding with letter-like stimuli. Proceedings of the National Academy of Sciences, 106, 13130–13135.CrossRefGoogle Scholar
  25. Greenwood, J. A., Bex, P. J., & Dakin, S. C. (2010). Crowding changes appearance. Current Biology, 20, 496–501. CrossRefGoogle Scholar
  26. Grindley, G. C., & Townsend, V. (1968). Voluntary attention in peripheral vision and its effects on acuity and differential thresholds. Quarterly Journal of Experimental Psychology, 20, 11–19.CrossRefGoogle Scholar
  27. Harrison, W. J., & Bex, P. J. (2015). A unifying model of orientation crowding in peripheral vision. Current Biology, 25, 3213–3219.CrossRefGoogle Scholar
  28. Hein, E., Rolke, B., & Ulrich, R. (2006). Visual attention and temporal discrimination: Differential effects of automatic and voluntary cueing. Visual Cognition, 13, 29–50.CrossRefGoogle Scholar
  29. Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2, 194–203. CrossRefGoogle Scholar
  30. Kastner, S., De Weerd, P., Desimone, R., & Ungerleider, L. G. (1998). Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. Science, 282, 108–111. CrossRefGoogle Scholar
  31. Kimchi, R., & Pirkner, Y. (2015). Multiple level crowding: Crowding at the object parts level and at the object configural level. Perception, 44, 1275–1292.CrossRefGoogle Scholar
  32. Kooi, F. L., Toet, A., Tripathy, S. P., & Levi, D. M. (1994). The effect of similarity and duration on spatial interaction in peripheral vision. Spatial Vision, 8, 255–279.CrossRefGoogle Scholar
  33. Kramer, A. F., & Jacobson, A. (1991). Perceptual organization and focused attention: The role of objects and proximity in visual processing. Perception & Psychophysics, 50, 267–284. CrossRefGoogle Scholar
  34. Lu, Z.-L., & Dosher, B. A. (1998). External noise distinguishes attention mechanisms. Vision Research, 38, 1183–1198. CrossRefGoogle Scholar
  35. Moore, C. M., Yantis, S., & Vaughan, B. (1998). Object-based visual selection: Evidence from perceptual completion. Psychological Science, 9, 104–110. CrossRefGoogle Scholar
  36. Moran, J., & Desimone, R. (1985). Selective attention gates visual processing in the extrastriate cortex. Frontiers in Cognitive Neuroscience, 229, 342–345.Google Scholar
  37. Neisser, U., & Becklen, R. (1975). Selective looking: Attending to visually specified events. Cognitive Psychology, 7, 480–494. CrossRefGoogle Scholar
  38. Parkes, L., Lund, J., Angelucci, A., Solomon, J. A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4, 739–744. CrossRefGoogle Scholar
  39. Pelli, D. G., Palomares, M., & Majaj, N. J. (2004). Crowding is unlike ordinary masking: Distinguishing feature integration from detection. Journal of Vision, 4(12), 12. CrossRefGoogle Scholar
  40. Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25. CrossRefGoogle Scholar
  41. Posner, M. I., Snyder, C. R., & Davidson, B. J. (1980). Attention and the detection of signals. Journal of Experimental Psychology: General, 109, 160–174. CrossRefGoogle Scholar
  42. Reynolds, J. H., & Chelazzi, L. (2004). Attentional modulation of visual processing. Annual Review of Neuroscience, 27, 611–647. CrossRefGoogle Scholar
  43. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237. CrossRefGoogle Scholar
  44. Scholl, B. J., Pylyshyn, Z. W., & Feldman, J. (2001). What is a visual object? Evidence from target merging in multiple object tracking. Cognition, 80, 159–177. CrossRefGoogle Scholar
  45. Scolari, M., Ester, E. F., & Serences, J. T. (2014). Feature- and object-based attentional modulation in the human visual system. In K. Nobre & S. Kastner (Eds.), The Oxford handbook of attention (pp. 573–600). Oxford: Oxford University Press.Google Scholar
  46. Scolari, M., Kohnen, A., Barton, B., & Awh, E. (2007). Spatial attention, preview, and popout: Which factors influence critical spacing in crowded displays? Journal of Vision, 7(2), 7. CrossRefGoogle Scholar
  47. Shiu, L.-P., & Pashler, H. (1994). Negligible effect of spatial precuing on identification of single digits. Journal of Experimental Psychology: Human Perception and Performance, 20, 1037–1054. Google Scholar
  48. Simons, D. J., & Levin, D. T. (1998). Failure to detect changes to people during a real-world interaction. Psychonomic Bulletin & Review, 5, 644–649.CrossRefGoogle Scholar
  49. Stevens, A. A., Maron, L., Nigg, J. T., Cheung, D., Ester, E. F., & Awh, E. (2012). Increased sensitivity to perceptual interference in adults with attention deficit hyperactivity disorder. Journal of the International Neuropsychological Society, 18, 511–520.CrossRefGoogle Scholar
  50. Strasburger, H. (2005). Unfocussed spatial attention underlies the crowding effect in indirect form vision. Journal of Vision, 5(11), 8. CrossRefGoogle Scholar
  51. Treisman, A. (1991). Search, similarity, and integration of features between and within dimensions. Journal of Experimental Psychology: Human Perception and Performance, 17, 652–676. Google Scholar
  52. Tripathy, S. P., & Cavanagh, P. (2002). The extent of crowding in peripheral vision does not scale with target size. Vision Research, 42, 2357–2369. CrossRefGoogle Scholar
  53. Vecera, S. P. (2000). Toward a biased competition account of object-based segregation and attention. Brain and Mind, 1, 353–384.CrossRefGoogle Scholar
  54. Vecera, S. P., & Farah, M. J. (1997). Is visual image segmentation a bottom-up or an interactive process? Perception & Psychophysics, 59, 1280–1296. CrossRefGoogle Scholar
  55. Williamson, K., Scolari, M., Jeong, S., Kim, M.-S., & Awh, E. (2009). Experience-dependent changes in the topography of visual crowding. Journal of Vision, 9(11), 15. CrossRefGoogle Scholar
  56. Wolford, G. (1975). Perturbation model for letter identification. Psychological Review, 82, 184–199. CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of Psychological SciencesTexas Tech UniversityLubbockUSA
  2. 2.Department of PsychologyUniversity of ChicagoChicagoUSA
  3. 3.Institute for Mind and BiologyUniversity of ChicagoChicagoUSA

Personalised recommendations