September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Weak interactions between surface and shape featured-based forms of attention during object perception
Author Affiliations
  • Nina Lee
    University of Toronto Scarborough
  • Matthias Niemeier
    University of Toronto ScarboroughCentre for Vision Research, York University
Journal of Vision September 2018, Vol.18, 322. doi:
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      Nina Lee, Matthias Niemeier; Weak interactions between surface and shape featured-based forms of attention during object perception. Journal of Vision 2018;18(10):322. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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Feature-based attention is well known to facilitate detection of stimuli or transients. Further, attention to features is known to aid in object perception; in particular, features that provide information about the surface properties of objects such as colour, and features that offer cues about the shape of the object. However, the influence of colour and shapes has never been tested together. The aim of the current study was to therefore test for interactions. To this end, participants detected the incomplete outlines of objects embedded in random arrays of lines and indicated their spatial location (left or right of centre). Attentional colour cues were conveyed through the frame and fixation point that predicted the colour of the upcoming object with 70% probability, and shape expectations were manipulated by sorting trials into blocks which repeated a single shape, or blocks which randomly displayed one of eight shapes each trial. As expected, we found colour cues and shape knowledge both to increase object perception accuracy. Surprisingly however, we observed no interaction between cues in that both together produced no super-additive benefit of attentional cueing. Our results suggest that object perception incorporates surface and shape feature cues through largely independent mechanisms. These results shed new light on the top-down processes of visual object recognition.

Meeting abstract presented at VSS 2018


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