September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Multisensory Detection: Using Vision and Haptics to detect hidden objects.
Author Affiliations
  • Julie Skevik
    Vision and Haptics Laboratory, School of Psychology and Clinical Language Sciences, University of Reading
  • Peter Scarfe
    Vision and Haptics Laboratory, School of Psychology and Clinical Language Sciences, University of Reading
Journal of Vision September 2018, Vol.18, 92. doi:10.1167/18.10.92
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      Julie Skevik, Peter Scarfe; Multisensory Detection: Using Vision and Haptics to detect hidden objects.. Journal of Vision 2018;18(10):92. doi: 10.1167/18.10.92.

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

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Abstract

Data from several studies show that when information is available from both vision and haptics simultaneously, people have a higher level of precision in discriminating properties such as size and shape, compared to either cue in isolation (Ernst and Banks 2002; Helbig and Ernst 2007). Most studies on the topic of visuo-haptic cue combination have focussed on discrimination, while few studies have investigated the effects on detection. In this experiment, we investigated whether these benefits would hold true in detection tasks, which have more relevance to applied situations such as the detection and delineation of cancerous tumours in medical imaging data (Abbey and Eckstein 2009). Participants had to compare two image patches presented on a computer monitor in a two-alternative forced choice task and detect which of the two patches contained a hidden 2D Gaussian profile. Information was available from touch, vision or both touch and vision simultaneously. Haptic feedback was provided by a desktop Phantom force feedback device and visual reliability parametrically manipulated by adding Gaussian white noise to each patch. Haptic stimuli maintained a constant level of reliability across conditions. Our findings are in line with those of previous discrimination studies. Observers are consistently better at detecting the hidden Gaussian signal across visual noise levels when both visual and haptic information were available, compared to either modality in isolation. We discuss our results in relation to models of optimal sensory cue combination, applied tasks such as tumour delineation and examine the haptic predictors of good performance.

Meeting abstract presented at VSS 2018

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