Abstract
Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveals the complexity of peri-perceptual processes of familiarity. The method is applied to data from 20 participants viewing familiar and unfamiliar logos. The results support the potential of SNN models as novel tools in the exploration of peri-perceptual mechanisms that respond differentially to familiar and unfamiliar stimuli. Specifically, the activation pattern of the time-locked response identified by the proposed SNN model at approximately 200 milliseconds post-stimulus suggests greater connectivity and more widespread dynamic spatio-temporal patterns for familiar than unfamiliar logos. The proposed SNN approach can be applied to study other peri-perceptual or perceptual brain processes in cognitive and computational neuroscience.
Highlights
Whilst functional magnetic resonance imaging studies show recognised brands activate inferior frontal gyrus, anterior insula and anterior cingulate gyrus bilaterally[16], a greater understanding of the temporal dynamics of neurocognitive processes that underpin buying behaviour might be obtained using electroencephalographic (EEG) data
We demonstrate for the first time that such spiking neural network (SNN) models can learn deep spatio-temporal patterns of EEG/event-related potential (ERP) data, reflecting peri-perceptual processes during a neuromarketing experiment in which familiar and unfamiliar logos are presented
This paper proposes a new methodology and a SNN model for training on EEG data to capture differences in dynamic brain activation patterns corresponding to peri-perceptual processes in response to familiar and unfamiliar stimuli, exemplified here as marketing logos
Summary
Whilst functional magnetic resonance imaging (fMRI) studies show recognised brands activate inferior frontal gyrus, anterior insula and anterior cingulate gyrus bilaterally[16], a greater understanding of the temporal dynamics of neurocognitive processes that underpin buying behaviour might be obtained using electroencephalographic (EEG) data. Neuromarketing studies utilising ERPs to investigate the post-perceptual components of ERP, such as the P300, in relation to familiarity[21], show higher amplitudes towards familiar than unfamiliar brands, which has been interpreted as reflecting strength in categorisation and attitude towards the brand. Few studies have investigated how earlier information processing stages are affected by stimuli and the related dynamic spatio-temporal patterns of brain activities[22,23]. Observing and understanding the specific details of how these processes occur dynamically over time (especially at a subconscious level) are not investigated in depth in current neuroscience research, and little work in computational neuroscience has been performed on this topic[24]. The current study proposes a novel computational modelling framework that is used here to develop a model of consumer behaviour that represents how early marketing materials are perceived at an unconscious level of information processing. Various SNN architectures have been developed far, along with their applications for modelling and knowledge discovery across domain areas using various high-dimensional spatio-temporal datasets, including brain data[36,37]
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