Final Prediction Error Criteria, Generalized

Krieger, Abba M.; Zhang, P.

doi:10.1002/9781118445112.stat03142

Cited by 1 publication

(1 citation statement)

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“…Conventional research methodologies, such as focus groups and surveys, are now complemented by a wide array of data analytics, neuroscientific investigations, machine learning algorithms, and predictive voter modeling. The intricate interplay between political leadership, party branding, and voter behavior is more pronounced than ever (Baines and Egan, 2001;Krieger and Zhang, 2014;Jungherr, 2016).…”

Section: Branding In Marketing and In Politicsmentioning

confidence: 99%

Unraveling neural pathways of political engagement: bridging neuromarketing and political science for understanding voter behavior and political leader perception

Çakar,

Filiz

2023

Front. Hum. Neurosci.

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IntroductionPolitical neuromarketing is an emerging interdisciplinary field integrating marketing, neuroscience, and psychology to decipher voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding.MethodsThis study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders.ResultsThis neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants’ perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters’ perceptions of political leaders based on the brain activity of the former.DiscussionThe findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science, and machine learning, in turn enabling predictive insights into voter preferences and behavior.

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Section: Branding In Marketing and In Politicsmentioning

confidence: 99%