Abstract
Attention is tuned towards locations that frequently contain a visual search target (location probability learning; LPL). Peripheral vision, covering a larger field than the fovea, often receives information about the target. Yet what is the role of peripheral vision in attentional learning? Using gaze-contingent eye tracking, we examined the impact of simulated peripheral vision loss on location probability learning. Participants searched for a target T among distractor Ls. Unbeknownst to them, the T appeared disproportionately often in one quadrant. Participants searched with either intact vision or “tunnel vision,” restricting the visible search items to the central 6.7º (in diameter) of the current gaze. When trained with tunnel vision, participants in Experiment 1 acquired LPL, but only if they became explicitly aware of the target’s location probability. The unaware participants were not faster finding the target in high-probability than in low-probability locations. When trained with intact vision, participants in Experiment 2 successfully acquired LPL, regardless of whether they were aware of the target’s location probability. Thus, whereas explicit learning may proceed with central vision alone, implicit LPL is strengthened by peripheral vision. Consistent with Guided Search (Wolfe, 2021), peripheral vision supports a nonselective pathway to guide visual search.
Similar content being viewed by others
Data and code availability
These experiments were not pre-registered. De-identified data in an aggregated format, MATLAB scripts, and videos verifying script accuracy are available at the Open Science Framework (https://osf.io/zc4vu/).
References
Addleman, D. A., & Lee, V. G. (2022). Simulated central vision loss does not impair implicit location probability learning when participants search through simple displays. Attention, Perception, & Psychophysics, 84(6), 1901–1912. https://doi.org/10.3758/s13414-021-02416-9
Addleman, D. A., Legge, G. E., & Jiang, Y. V. (2021). Simulated central vision loss impairs implicit location probability learning. Cortex, 138, 241–252. https://doi.org/10.1016/j.cortex.2021.02.009
Albrecht, A. R., & Scholl, B. J. (2010). Perceptually Averaging in a Continuous Visual World: Extracting Statistical Summary Representations Over Time. Psychological Science, 21(4), 560–567. https://doi.org/10.1177/0956797610363543
Anstis, S. M. (1974). A chart demonstrating variations in acuity with retinal position. Vision Research, 14(7), 589–592. https://doi.org/10.1016/0042-6989(74)90049-2
Bedell, H. E., Tong, J., Woo, S. Y., House, J. R., & Nguyen, T. (2009). Orientation discrimination with macular changes associated with early amd. Optometry and Vision Science: Official Publication of the American Academy of Optometry, 86(5), 485–491. https://doi.org/10.1097/OPX.0b013e31819fa6e2
Brady, T. F., & Chun, M. M. (2007). Spatial constraints on learning in visual search: Modeling contextual cuing. Journal of Experimental Psychology: Human Perception and Performance, 33(4), 798. https://doi.org/10.1037/0096-1523.33.4.798
Brockmole, J. R., Castelhano, M. S., & Henderson, J. M. (2006). Contextual cueing in naturalistic scenes: Global and local contexts. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(4), 699–706. https://doi.org/10.1037/0278-7393.32.4.699
Brockmole, J. R., & Henderson, J. M. (2006). Using real-world scenes as contextual cues for search. Visual Cognition, 13(1), 99–108.
Bullimore, M. A., Bailey, I. L., & Wacker, R. T. (1991). Face recognition in age-related maculopathy. Investigative Ophthalmology & Visual Science, 32(7), 2020–2029.
Chen, C., & Lee, V. G. (2023). Looking away to see: The acquisition of a search habit away from the saccade direction. Vision Research, 211, 108276. https://doi.org/10.1016/j.visres.2023.108276
Chen, C., Lee, V. G. (2023b). Stability of individual differences in implicitly guided attention. Quarterly Journal of Experimental Psychology, 17470218231196464. https://doi.org/10.1177/17470218231196463
Chong, S. C., & Treisman, A. (2003). Representation of statistical properties. Vision Research, 43(4), 393–404. https://doi.org/10.1016/S0042-6989(02)00596-5
Chun, M. M., Golomb, J. D., & Turk-Browne, N. B. (2011). A taxonomy of external and internal attention. Annual Review of Psychology, 62(1), 73–101. https://doi.org/10.1146/annurev.psych.093008.100427
Chun, M. M., & Jiang, Y. (1998). Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention. Cognitive Psychology, 36(1), 28–71. https://doi.org/10.1006/cogp.1998.0681
Chun, M. M., & Jiang, Y. (2003). Implicit, long-term spatial contextual memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(2), 224.
Cloutier, M., & DeLucia, P. R. (2022). Topical Review: Impact of Central Vision Loss on Navigation and Obstacle Avoidance while Walking. Optometry and Vision Science, 99(12), 890. https://doi.org/10.1097/OPX.0000000000001960
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18(1), 193–222. https://doi.org/10.1146/annurev.ne.18.030195.001205
Druker, M., Anderson, B. (2010). Spatial Probability Aids Visual Stimulus Discrimination. Frontiers in Human Neuroscience, 4. https://doi.org/10.3389/fnhum.2010.00063
Evans, K. K., Georgian-Smith, D., Tambouret, R., Birdwell, R. L., & Wolfe, J. M. (2013). The gist of the abnormal: Above-chance medical decision making in the blink of an eye. Psychonomic Bulletin & Review, 20(6), 1170–1175. https://doi.org/10.3758/s13423-013-0459-3
Evans, K. K., Haygood, T. M., Cooper, J., Culpan, A.-M., & Wolfe, J. M. (2016). A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast. Proceedings of the National Academy of Sciences, 113(37), 10292–10297. https://doi.org/10.1073/pnas.1606187113
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
Florey, J., Dakin, S. C., & Mareschal, I. (2017). Comparing averaging limits for social cues over space and time. Journal of Vision, 17(9), 17. https://doi.org/10.1167/17.9.17
Geng, J. J., & Behrmann, M. (2002). Probability Cuing of Target Location Facilitates Visual Search Implicitly in Normal Participants and Patients with Hemispatial Neglect. Psychological Science, 13(6), 520–525. https://doi.org/10.1111/1467-9280.00491
Geng, J. J., & Behrmann, M. (2005). Spatial probability as an attentional cue in visual search. Perception & Psychophysics, 67(7), 1252–1268. https://doi.org/10.3758/BF03193557
Geng, J. J., Soosman, S., Sun, Y., DiQuattro, N. E., Stankevitch, B., & Minzenberg, M. J. (2013). A Match Made by Modafinil: Probability Matching in Choice Decisions and Spatial Attention. Journal of Cognitive Neuroscience, 25(5), 657–669. https://doi.org/10.1162/jocn_a_00333
Geringswald, F., Baumgartner, F., Pollmann, S. (2012). Simulated loss of foveal vision eliminates visual search advantage in repeated displays. Frontiers in Human Neuroscience, 6. https://doi.org/10.3389/fnhum.2012.00134
Geringswald, F., & Pollmann, S. (2015). Central and peripheral vision loss differentially affects contextual cueing in visual search. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(5), 1485.
Gorea, A., Belkoura, S., & Solomon, J. A. (2014). Summary statistics for size over space and time. Journal of Vision, 14(9), 22. https://doi.org/10.1167/14.9.22
Haberman, J., Harp, T., & Whitney, D. (2009). Averaging facial expression over time. Journal of Vision, 9(11), 1. https://doi.org/10.1167/9.11.1
Haberman, J., & Whitney, D. (2010). The visual system discounts emotional deviants when extracting average expression. Attention, Perception, & Psychophysics, 72(7), 1825–1838. https://doi.org/10.3758/APP.72.7.1825
Jiang, Y. V. (2018). Habitual versus goal-driven attention. Cortex, 102, 107–120. https://doi.org/10.1016/j.cortex.2017.06.018
Jiang, Y. V., Swallow, K. M., Rosenbaum, G. M., & Herzig, C. (2013). Rapid acquisition but slow extinction of an attentional bias in space. Journal of Experimental Psychology: Human Perception and Performance, 39(1), 87–99. https://doi.org/10.1037/a0027611
Jiang, Y. V., Swallow, K. M., & Sun, L. (2014). Egocentric coding of space for incidentally learned attention: Effects of scene context and task instructions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(1), 233.
Jiang, Y. V., Won, B.-Y., & Swallow, K. M. (2014). First saccadic eye movement reveals persistent attentional guidance by implicit learning. Journal of Experimental Psychology: Human Perception and Performance, 40(3), 1161. https://doi.org/10.1037/a0035961
Jiang, M., Huang, S., Duan, J., & Zhao, Q. (2015). Salicon: Saliency in context. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1072-1080). https://www.cvfoundation.org/openaccess/content_cvpr_2015/html/Jiang_SALICON_Saliency_in_2015_CVPR_paper.html
Jiang, Y. V., Swallow, K. M., Won, B.-Y., Cistera, J. D., & Rosenbaum, G. M. (2015). Task specificity of attention training: The case of probability cuing. Attention, Perception, & Psychophysics, 77(1), 50–66. https://doi.org/10.3758/s13414-014-0747-7
Jiang, Y. V., Sha, L. Z., & Sisk, C. A. (2018). Experience-guided attention: Uniform and implicit. Attention, Perception, & Psychophysics, 80(7), 1647–1653. https://doi.org/10.3758/s13414-018-1585-9
Kleiner, M., Brainard, D., & Pelli, D. (2007). What's new in Psychtoolbox-3? Perception, 36 (ECVP Abstract Supplement). https://pure.mpg.de/rest/items/item_1790332/component/file_3136265/content
Legge, G. E., Rubin, G. S., Pelli, D. G., & Schleske, M. M. (1985). Psychophysics of reading—II. Low vision. Vision Research, 25(2), 253–265. https://doi.org/10.1016/0042-6989(85)90118-X
Lew, T. F., & Vul, E. (2015). Ensemble clustering in visual working memory biases location memories and reduces the Weber noise of relative positions. Journal of Vision, 15(4), 10. https://doi.org/10.1167/15.4.10
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual review of neuroscience, 24(1), 167–202. https://doi.org/10.1146/annurev.neuro.24.1.167
Montag, J. L. (2021). Chapter Six—Limited evidence for probability matching as a strategy in probability learning tasks. In K. D. Federmeier (Ed.), Psychology of Learning and Motivation (74th ed., pp. 233–273). Academic Press. https://doi.org/10.1016/bs.plm.2021.02.005
O’Regan, J. K., Rensink, R. A., & Clark, J. J. (1999). Change-blindness as a result of ‘mudsplashes’. Nature, 398(6722), 6722. https://doi.org/10.1038/17953
Qiu, F., & Leat, S. J. (2009). Functional deficits in early stage age-related maculopathy. Clinical and Experimental Optometry, 92(2), 90–98. https://doi.org/10.1111/j.1444-0938.2008.00343.x
Saldana, C., Claidière, N., Fagot, J., & Smith, K. (2022). Probability matching is not the default decision making strategy in human and non-human primates. Scientific Reports, 12(1), 1. https://doi.org/10.1038/s41598-022-16983-w
Sha, L. Z., Remington, R. W., & Jiang, Y. V. (2018). Statistical learning of anomalous regions in complex faux X-ray images does not transfer between detection and discrimination. Cognitive Research: Principles and Implications, 3(1), 48. https://doi.org/10.1186/s41235-018-0144-1
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “Brain-Training” Programs Work? Psychological Science in the Public Interest, 17(3), 103–186. https://doi.org/10.1177/1529100616661983
Sisk, C. A., Remington, R. W., & Jiang, Y. V. (2019). Mechanisms of contextual cueing: A tutorial review. Attention, Perception, & Psychophysics, 81(8), 2571–2589. https://doi.org/10.3758/s13414-019-01832-2
Treisman, A. (1996). The binding problem. Current Opinion in Neurobiology, 6(2), 171–178. https://doi.org/10.1016/S0959-4388(96)80070-5
Vadillo, M. A., Linssen, D., Orgaz, C., Parsons, S., & Shanks, D. R. (2020). Unconscious or underpowered? Probabilistic cuing of visual attention. Journal of Experimental Psychology: General, 149(1), 160–181. https://doi.org/10.1037/xge0000632
Virsu, V., Rovamo, J. (1979). Visual resolution, contrast sensitivity, and the cortical magnification factor. Experimental Brain Research, 37(3). https://doi.org/10.1007/BF00236818
Wang, B., & Theeuwes, J. (2018). Statistical regularities modulate attentional capture. Journal of Experimental Psychology: Human Perception and Performance, 44(1), 13–17. https://doi.org/10.1037/xhp0000472
Wang, Y.-Z., Wilson, E., Locke, K. G., & Edwards, A. O. (2002). Shape Discrimination in Age-Related Macular Degeneration. Investigative Ophthalmology & Visual Science, 43(6), 2055–2062.
Whitney, D., & Yamanashi Leib, A. (2018). Ensemble perception. Annual Review of Psychology, 69(1), 105–129. https://doi.org/10.1146/annurev-psych-010416-044232
Wolfe, B. A., Kosovicheva, A. A., Leib, A. Y., Wood, K., & Whitney, D. (2015). Foveal input is not required for perception of crowd facial expression. Journal of Vision, 15(4), 11. https://doi.org/10.1167/15.4.11
Wolfe, J. M. (2021). Guided search 6.0: An updated model of visual search. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-020-01859-9
Wolfe, J. M., Võ, M.L.-H., Evans, K. K., & Greene, M. R. (2011). Visual search in scenes involves selective and nonselective pathways. Trends in Cognitive Sciences, 15(2), 77–84. https://doi.org/10.1016/j.tics.2010.12.001
Yamanashi Leib, A., Fischer, J., Liu, Y., Qiu, S., Robertson, L., & Whitney, D. (2014). Ensemble crowd perception: A viewpoint-invariant mechanism to represent average crowd identity. Journal of Vision, 14(8), 26. https://doi.org/10.1167/14.8.26
Zang, X., Jia, L., Müller, H. J., & Shi, Z. (2015). Invariant spatial context is learned but not retrieved in gaze-contingent tunnel-view search. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(3), 807.
Acknowledgements
This study was supported by a McKnight award to VGL. We thank Roger Remington, Yi Ni Toh, Emma Holtz, and Gavin Oliver for comments and suggestions, and Sanjali Roy and Mikayla Albertson for assistance with data collection.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethics approval and consent to participate
The study received Institutional Review Board approval from the University of Minnesota [STUDY00007254]. All participants provided informed consent in person.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Significance statement
What role does peripheral vision play in guiding spatial attention? Here, we showed that location probability learning (LPL) – the acquisition of an attentional preference for locations that frequently contained a visual search target – relied partially on peripheral vision. When searching with intact vision, participants acquired LPL regardless of whether they became aware of the target’s biased locations. In contrast, when searching with a simulated peripheral vision loss, only participants with explicit awareness about the target’s location probability acquired LPL. The findings have implications for understanding attentional learning in patients with peripheral vision loss.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, C., Lee, V.G. Contribution of peripheral vision to attentional learning. Atten Percept Psychophys 86, 95–108 (2024). https://doi.org/10.3758/s13414-023-02808-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3758/s13414-023-02808-z