Barik, Kasturi; Daimi, Syed Naser; Jones, Rhiannon; Bhattacharya, Joydeep and Saha, Goutam. 2019. A machine learning approach to predict perceptual decisions: an insight into face pareidolia. Brain Informatics, 6(2), ISSN 2198-4018 [Article]

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Abstract or Description

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using differential hemispheric asymmetry features based on large-scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain activities could achieve a classification accuracy, discriminating face from no-face perception, of 75% across trials. The time–frequency features representing hemispheric asymmetry yielded the best classification performance, and prestimulus alpha oscillations were found to be mostly involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision making.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1186/s40708-019-0094-5
Additional Information:	The work was funded by the Ministry of Human Resource and Development, Government of India, under the scheme of Signals and Systems for Life Sciences (sanctioned vide No. F. No. 4-23/2014-TS.I, Dt. 14-02-2014).
Keywords:	Brain, EEG, Machine learning, Face processing, Mind reading, Pareidolia, Oscillations
Departments, Centres and Research Units:	Psychology
Dates:	Date Event 17 January 2019 Accepted 5 February 2019 Published
Item ID:	25745
Date Deposited:	06 Feb 2019 13:52
Last Modified:	29 Apr 2020 17:06
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/25745

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