Neural correlates of the non-optimal price: an MEG/EEG study

Frontiers in Human Neuroscience. 2025. Vol. 19. Article 1470662.

Gorin A., Kuznetsova E., Kislov A., Levchenko E., Klucharev V., Moiseeva V., Yurchenko A., Luzhin A., Galkina N., Shestakova A.

Introduction: Setting the right price is crucial for e􀀀ectively positioning products
in the market. Conversely, setting a “non-optimal price”—one that is perceived
as much lower or higher than the product’s true market value—can negatively
influence consumer decisions and business results.
Methods: We conducted two electroencephalography (EEG) studies and
one magnetoencephalography (MEG) study to investigate brain mechanisms
underlying the perception of prices during a price judgment task. In each trial,
participants were exposed to a mobile phone image (iPhone, Nokia, or Xiaomi)
followed by a price, and instructed to judge whether the price was high or low
based on a target word (“cheap” or “expensive”).
Results: In both EEG experiments, we found a strong N400-like response to the
incongruent target words following prices that substantially di􀀀ered fromthe real
market value of themobile phone. The MEG experiment extended these findings
by localizing the brain source of the price-related, M400-like response, the
magnetic counterpart to the N400 component, in the ventromedial prefrontal
cortex (vmPFC) and anterior cingulate cortex (ACC) implicated in value-based
and reward-based learning, respectively. Our results demonstrate that both the
brain sources and the timing of the price-related M400 response di􀀀ered from
those of the standard M400 evoked by semantically incongruent words.
Discussion: Overall, our results suggest that the N400-like response can serve
as a neural marker of price-product incongruity, with potential applications in
consumer research.

Keywords: EEG, neuromarketing, EEG and MEG

Publication based on the results of: Multidisciplinary study of behavior and decision-making in health population and patients using behavioral, economic, neurocognitive, neuroeconomic, neurocomputational and neural network approaches (2025)