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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Marketing Science

Background:

  • Consumer behavior is influenced by marketing stimuli at a peri-perceptual level.
  • Understanding these early processing stages is crucial for marketing effectiveness.
  • Existing methods may not fully capture the complexity of familiarity detection.

Purpose of the Study:

  • To introduce a novel deep learning method using spiking neural networks (SNNs) for analyzing electroencephalogram (EEG) data.
  • To investigate the peri-perceptual processing of familiarity in response to marketing stimuli.
  • To explore the differential brain responses to familiar and unfamiliar logos.

Main Methods:

  • Applied a deep learning approach utilizing SNNs to analyze EEG data.
  • Collected EEG data from 20 participants exposed to familiar and unfamiliar logos.
  • Utilized SNNs to identify spatio-temporal patterns in brain activity.

Main Results:

  • The SNN model successfully differentiated brain responses to familiar and unfamiliar logos.
  • A distinct time-locked activation pattern was observed around 200 milliseconds post-stimulus.
  • Familiar logos elicited greater neural connectivity and more dynamic spatio-temporal patterns compared to unfamiliar ones.

Conclusions:

  • Spiking neural network models show promise as tools for studying peri-perceptual mechanisms.
  • The findings highlight differential neural processing of familiarity in marketing contexts.
  • This SNN approach can be extended to investigate other perceptual processes in neuroscience.