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Updated: Jul 20, 2025
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Errors as a Means of Reducing Impulsive Food Choice
Published on: June 5, 2016
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Food Choices after Cognitive Load: An Affective Computing Approach
Arpita Mallikarjuna Kappattanavar1, Pascal Hecker1, Sidratul Moontaha1
1Digital Health-Connected Healthcare, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.
Sensors (Basel, Switzerland)
|July 29, 2023
Summary
High cognitive load increases calorie consumption, especially in individuals with negative emotions. This research paves the way for an eating behavior assistant system to manage food choices during demanding mental tasks.
Area of Science:
- Psychology
- Nutritional Science
- Human-Computer Interaction
Background:
- Previous research used subjective methods to link negative emotions and cognitive load to eating behavior.
- Objective assessment of cognitive load often overlooks its impact on dietary choices.
Purpose of the Study:
- To explore the development of an integrated eating behavior assistant system.
- To objectively detect cognitive load and analyze its influence on food selection.
Main Methods:
- Two experimental sessions were conducted using custom software to induce cognitive load.
- Collected physiological, food consumption, and affective state data over 30 hours.
- Utilized a Support Vector Machine model with grid search and leave-one-subject-out cross-validation for cognitive load detection.
Main Results:
- A Support Vector Machine model achieved 85.12% accuracy in detecting cognitive load using eight features.
- Statistical analysis revealed significant increases in specific food consumption among subjects with higher negative affect after high cognitive load tasks.
- 75% of subjects with higher negative affect showed altered food consumption patterns.
Conclusions:
- Cognitive load and affective states significantly influence food choices.
- Findings support the development of an eating behavior assistant system to manage dietary intake during cognitive demand.
- Future work should focus on system enhancement and real-world application testing.
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