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Positive affect significantly influences cognitive processes, including evaluation, memory, creativity, and social judgments. Compared to negative affect, positive emotional states promote more favorable interpretations of stimuli, cognitive flexibility, and heuristic processing. These effects highlight emotions' powerful role in shaping how individuals perceive, remember, and interact with the world.Influence on Evaluation and AttributionWhen individuals experience positive affect, they are...
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Cognition plays a pivotal role in shaping emotional experiences, as demonstrated by Schachter and Singer’s two-factor theory of emotion. According to this model, emotion arises from a combination of physiological arousal and cognitive interpretation. The body’s physiological response to stimuli is ambiguous and only gains emotional significance through cognitive labeling. For instance, an increased heart rate and adrenaline surge while standing near an attractive person may be...
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research.

Dennis Küster1,2, Eva G Krumhuber3, Lars Steinert1

  • 1Cognitive Systems Lab, Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany.

Frontiers in Neuroscience
|May 16, 2020
PubMed
Summary
This summary is machine-generated.

Automatic emotion assessment from video is key for predicting consumer choices. This study calls for validating facial expression classifiers and focusing on spontaneous emotions in real-world settings for better marketing insights.

Keywords:
automatic human affect analysis (AHAA)consumer researchdynamic responsesfacial expressionmachine learningspontaneous expressions

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Area of Science:

  • Consumer Psychology
  • Affective Computing
  • Human-Computer Interaction

Background:

  • Contact-free video analysis for emotional responses targets the growing market of predicting consumer choices.
  • Objective data on consumer attention and engagement can inform marketing decisions.
  • Current automatic affect recognition faces challenges like lack of cross-system validation and focus on posed vs. spontaneous expressions.

Purpose of the Study:

  • To address limitations in automatic affect recognition for consumer research.
  • To advocate for rigorous cross-system validation of facial expression classifiers.
  • To promote the study of spontaneous emotional expressions in naturalistic settings.

Main Methods:

  • Cross-system validation of commercial and free facial expression classifiers.
  • Leveraging fine-grained emotional response dynamics.
  • Analyzing facial behavior in socio-emotional contexts within naturalistic choice settings.

Main Results:

  • Facial expression classifiers require rigorous validation across different systems.
  • Emphasis should shift towards understanding spontaneous emotional expressions.
  • Facial activity analysis is more valuable as a starting point for broader analyses.

Conclusions:

  • Applied consumer research should prioritize studying facial behavior in naturalistic, socio-emotional contexts.
  • The Affective Human-computer Interaction Analysis (AHAA) framework can be applied effectively.
  • Future research needs to overcome obstacles in decontextualized laboratory studies and focus on nuanced facial activity analysis.