dc.contributor.authorMohan, Dhanya Menoth
dc.contributor.authorKumar, Parmod
dc.contributor.authorMahmood, Faisal
dc.contributor.authorWong, Kian Foong
dc.contributor.authorAgrawal, Abhishek
dc.contributor.authorMohamed Elgendi
dc.contributor.authorShukla, Rohit
dc.contributor.authorAng, Natania
dc.contributor.authorChing, April
dc.contributor.authorDauwels, Justin
dc.contributor.authorChan, Alice Hiu Dan
dc.contributor.editorBen Hamed, Suliann*
dc.date.accessioned2018-11-02T03:17:08Z
dc.date.available2018-11-02T03:17:08Z
dc.date.issued2016
dc.identifier.citationMohan, 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.0148332en_US
dc.identifier.urihttp://hdl.handle.net/10220/46528
dc.description.abstractThe 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.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.format.extent22 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesPLOS ONEen_US
dc.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.en_US
dc.subjectSubliminal Primingen_US
dc.subjectPerception of Imagesen_US
dc.subjectDRNTU::Humanities::Linguisticsen_US
dc.titleEffect of subliminal lexical priming on the subjective perception of images : a machine learning approachen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolSchool of Humanitiesen_US
dc.contributor.schoolSchool of Social Sciencesen_US
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pone.0148332
dc.description.versionPublished versionen_US


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