Voluntary control of illusory contour formation

  • William J. HarrisonEmail author
  • Reuben Rideaux


The extent to which visual inference is shaped by attentional goals is unclear. Voluntary attention may simply modulate the priority with which information is accessed by the higher cognitive functions involved in perceptual decision making. Alternatively, voluntary attention may influence fundamental visual processes, such as those involved in segmenting an incoming retinal signal into a structured scene of coherent objects, thereby determining perceptual organization. Here we tested whether the segmentation and integration of visual form can be determined by an observer’s goals, by exploiting a novel variant of the classical Kanizsa figure. We generated predictions about the influence of attention with a machine classifier and tested these predictions with a psychophysical response classification technique. Despite seeing the same image on each trial, observers’ perception of illusory spatial structure depended on their attentional goals. These attention-contingent illusory contours directly conflicted with other, equally plausible visual forms implied by the geometry of the stimulus, revealing that attentional selection can determine the perceived layout of a fragmented scene. Attentional goals, therefore, not only select precomputed features or regions of space for prioritized processing, but under certain conditions also greatly influence perceptual organization, and thus visual appearance.


Object-based attention Cognitive and attentional control Grouping Segmentation 


Author note

We are indebted to Peter Bex, who developed the novel Kanizsa figure with us and provided helpful feedback on our study design and results. We also thank Tom Wallis for feedback on an earlier draft that led to the mixture modeling and for overall improvements in the manuscript. This research was supported by funding to W.J.H. from King’s College Cambridge and the National Health and Medical Research Council of Australia (APP1091257), and by funding to R.R. (ECF-2017-573) from the Leverhulme Trust. Both authors designed the experiment and collected the data. W.J.H. analyzed the experimental data, R.R. performed the SVM analyses, and both authors performed the model comparisons. Both authors contributed equally to the writing of the manuscript. We declare we have no competing interests.

Supplementary material

13414_2019_1678_MOESM1_ESM.pdf (174 kb)
Supplementary Fig. 1 Support vector machine (SVM) images. From left to right, the first two columns show examples of wide and narrow exemplar images used to train the SVM in the inducers, triangle, and star protocols. The column on the right shows examples of the test image for each protocol. (PDF 174 kb)
13414_2019_1678_MOESM2_ESM.pdf (195 kb)
Supplementary Fig. 2 Raw classification images and psychophysical performance. (a) Classification images without averaging of edges via rotation. Note that the individual images show varying degrees of a complete triangle. One explanation for this is that observers perceived a partial shape—for example, a single or pair of unconnected lines between the cued inducers. Given the strength of the Kanizsa illusion in producing the percept of a triangle, rather than a partial shape, a more likely explanation is that observers perceived a triangle, but only used part of this triangle to perform the task. (b) Threshold performance across blocks shown separately for each observer. Thresholds were the midpoint of a cumulative Gaussian fit to accuracy data for each session. (PDF 195 kb)
13414_2019_1678_MOESM3_ESM.pdf (171 kb)
Supplementary Fig. 3 Individual classification images revealing a potential influence of non-cued structure. These images were created by summing each classification image with a flipped version of itself. Note that the emergent structure aligns to the geometry of the star implied by our Kanizsa figure (Fig. 1a). (PDF 170 kb)


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Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of CambridgeCambridgeUK
  2. 2.Queensland Brain InstituteUniversity of QueenslandSt LuciaAustralia

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