The Study of Human Preference Based on Integrated Analysis of N1 and LPP Components

TL;DR

This study investigates neural correlates of human preferences using ERP components N1 and LPP, revealing how early visual attention and emotional engagement relate to preference levels through EEG analysis.

Contribution

It introduces a novel integrated model combining N1 and LPP ERP components to assess preference strength and direction, advancing EEG-based preference analysis methods.

Findings

N1 amplitude increases for preferred items in frontal regions

LPP amplitudes are larger for both preferred and non-preferred items

The relationship model effectively interprets EEG data in emotional preference contexts

Abstract

Human preference research is a significant domain in psychology and psychophysiology, with broad applications in psychiatric evaluation and daily life quality enhancement. This study explores the neural mechanisms of human preference judgments through the analysis of event-related potentials (ERPs), specifically focusing on the early N1 component and the late positive potential (LPP). Using a mixed-image dataset covering items such as hats, fruits, snacks, scarves, drinks, and pets, we elicited a range of emotional responses from participants while recording their brain activity via EEG. Our work innovatively combines the N1 and LPP components to reveal distinct patterns across different preference levels. The N1 component, particularly in frontal regions, showed increased amplitude for preferred items, indicating heightened early visual attention. Similarly, the LPP component exhibited larger amplitudes for both preferred and non-preferred items, reflecting deeper emotional engagement and cognitive evaluation. In addition, we introduced a relationship model that integrates these ERP components to assess the intensity and direction of preferences, providing a novel method for interpreting EEG data in the context of emotional responses. These findings offer valuable insights into the cognitive and emotional processes underlying human preferences and present new possibilities for brain-computer interface applications, personalized marketing, and product design.