September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Decoding face pareidolia in the human brain with fMRI
Author Affiliations
  • Susan Wardle
    Department of Cognitive Science, Macquarie University, Sydney, Australia
    ARC Centre for Excellence in Cognition and its Disorders, Macquarie University
  • Kiley Seymour
    Department of Cognitive Science, Macquarie University, Sydney, Australia
    ARC Centre for Excellence in Cognition and its Disorders, Macquarie University
  • Jessica Taubert
    Laboratory of Brain And Cognition, National Institute of Mental Health
Journal of Vision August 2017, Vol.17, 294. doi:10.1167/17.10.294
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      Susan Wardle, Kiley Seymour, Jessica Taubert; Decoding face pareidolia in the human brain with fMRI. Journal of Vision 2017;17(10):294. doi: 10.1167/17.10.294.

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

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Abstract

A common human experience is face pareidolia, whereby illusory faces are perceived in inanimate objects. A unique aspect of pareidolia is that the objects are typically perceived simultaneously as both an illusory face and an inanimate object. Ventral visual areas such as the lateral occipital complex (LOC) and fusiform face area (FFA) in human occipital-temporal cortex are category-selective and respond to either objects or faces respectively. Consequently, it is unclear how these category-selective regions process stimuli with a dual face/object identity. Here we use fMRI to probe how visual stimuli with a persistent dual identity are processed by face and object-selective areas. We used a diverse image set containing natural examples of pareidolia in a wide variety of everyday objects. Critically, we created a yoked image set that was matched for object content and visual features but did not contain any illusory faces. We used a yoked block design to measure patterns of BOLD activation in response to objects where pareidolia was present or absent. Standard functional localizers were used to define category-selective areas. Using standard leave-one-run out classification, a linear support vector machine (SVM) could decode pareidolia objects versus non-face objects from both early visual cortex (V1), and higher-level category-selective areas (LOC and FFA). Importantly, in both LOC and FFA the classifier could successfully decode the presence or absence of pareidolia faces in new image sets that were not used for training the classifier, demonstrating generalization. In contrast, the presence of pareidolia could not be decoded in V1 when different image sets were used for training versus testing the classifier. This suggests that both FFA and LOC respond to the presence of illusory faces in inanimate objects. Interestingly, cross-classification of object identity was not successful in either FFA or LOC, suggesting face pareidolia is strongly represented in these areas.

Meeting abstract presented at VSS 2017

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