How Artificial Neural Networks Affect Human Emotions
Author Information
Author(s): Agnieszka Marczak-Czajka, Timothy Redgrave, Mahsa Mitcheff, Michael Villano, Adam Czajka
Primary Institution: University of Notre Dame
Hypothesis
Can visual stimuli synthesized by artificial neural networks evoke specific emotional reactions in humans?
Conclusion
The study shows that synthesizing images using specific methods can enhance or reduce emotional reactions in viewers.
Supporting Evidence
- Synthesized images that maximized activations of some CNN layers led to significantly higher or lower arousal and valence levels compared to average subject reactions.
- Multiple linear regression analysis found that hue, feature congestion, and sub-band entropy were significant predictors of arousal.
- No statistically significant dependencies were found between image global visual features and the measured valence.
Takeaway
This study found that pictures made by computers can make people feel different emotions, like happy or sad, depending on how the pictures are made.
Methodology
Participants rated their emotional responses to images generated by a convolutional neural network based on self-assessment manikin figures.
Potential Biases
Self-reported data may introduce biases in emotional responses.
Limitations
The study relied on self-reported data and had a limited sample size, which may affect the generalizability of the findings.
Participant Demographics
150 participants (98 female) with a mean age of 26.16 years.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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