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Emotional Cognitive Expression in Lacquer Colors Based on Prior Knowledge.

Ping Wei1

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This study introduces a framework for understanding emotions in lacquer painting. It uses emotional cognitive evaluation to analyze how colors convey feelings, improving affective expression efficiency by 1.3%.

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Area of Science:

  • Art History
  • Cognitive Science
  • Computer Science

Background:

  • Lacquer painting research is growing, with increased interest in modern techniques.
  • Challenges exist in quantifying the emotional impact of lacquer painting colors.
  • Limited research specifically links lacquer art creation to emotional expression.

Purpose of the Study:

  • To present a decision-making framework for emotional cognitive learning in lacquer painting.
  • To address the difficulty in describing and evaluating the emotions conveyed by lacquer art.
  • To highlight the importance of affective computing in analyzing art.

Main Methods:

  • Developing a framework based on emotional cognitive evaluation theory.
  • Implementing an observation module to collect emotional data from lacquer paintings.
  • Utilizing an emotional evaluation system combining data with prior knowledge.

Main Results:

  • A framework for analyzing emotional states in lacquer art was established.
  • The emotional evaluation system effectively processed preprocessed information and prior knowledge.
  • A 1.3% increase in the efficiency of affective expression was achieved.

Conclusions:

  • Emotional cognitive expression is crucial in lacquer painting.
  • Affective computing is essential for machine learning and decision control in art analysis.
  • The proposed framework offers a novel approach to understanding art and emotion.