Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80454
Title: Effect of subliminal lexical priming on the subjective perception of images : a machine learning approach
Authors: Mohan, Dhanya Menoth
Kumar, Parmod
Mahmood, Faisal
Wong, Kian Foong
Agrawal, Abhishek
Mohamed Elgendi
Shukla, Rohit
Ang, Natania
Ching, April
Dauwels, Justin
Chan, Alice Hiu Dan
Keywords: Subliminal Priming
Perception of Images
DRNTU::Humanities::Linguistics
Issue Date: 2016
Source: Mohan, D. M., Kumar, P., Mahmood, F., Wong, K. F., Agrawal, A., Mohamed Elgendi., . . . Chan, A. H. D. (2016). Effect of subliminal lexical priming on the subjective perception of images : a machine learning approach. PLOS ONE, 11(2), e0148332-. doi:10.1371/journal.pone.0148332
Series/Report no.: PLOS ONE
Abstract: The purpose of the study is to examine the effect of subliminal priming in terms of the perception of images influenced by words with positive, negative, and neutral emotional content, through electroencephalograms (EEGs). Participants were instructed to rate how much they like the stimuli images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words that exhibit positive, negative, and neutral connotations with respect to the images. Simultaneously, the EEGs were recorded. Statistical tests such as repeated measures ANOVAs and two-tailed paired-samples t-tests were performed to measure significant differences in the likability ratings among the three prime affect types; the results showed a strong shift in the likeness judgment for the images in the positively primed condition compared to the other two. The acquired EEGs were examined to assess the difference in brain activity associated with the three different conditions. The consistent results obtained confirmed the overall priming effect on participants’ explicit ratings. In addition, machine learning algorithms such as support vector machines (SVMs), and AdaBoost classifiers were applied to infer the prime affect type from the ERPs. The highest classification rates of 95.0% and 70.0% obtained respectively for average-trial binary classifier and average-trial multi-class further emphasize that the ERPs encode information about the different kinds of primes.
URI: https://hdl.handle.net/10356/80454
http://hdl.handle.net/10220/46528
DOI: 10.1371/journal.pone.0148332
Rights: © 2016 Mohan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SoH Journal Articles

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