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Related Concept Videos

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role of...
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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
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Related Experiment Video

Updated: Jul 8, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

AI-generated faces are becoming more trustworthy.

Alexis A McGuire1,2,3, Maty Bohacek4,5,6, Hany Farid7,8

  • 1Department of Psychology, Lancaster University, Lancaster, UK.

Journal of Vision
|July 7, 2026
PubMed
Summary

Newer AI models create less realistic but more trustworthy faces than older ones. Understanding generative AI (GAI) risks is crucial as its capabilities grow.

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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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Published on: December 24, 2015

Related Experiment Videos

Last Updated: Jul 8, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Generative artificial intelligence (GAI) offers advanced image generation capabilities.
  • Generative Adversarial Networks (GANs) produce highly realistic synthetic faces.
  • Limited research compares newer GAI models with GANs regarding realism and trustworthiness.

Purpose of the Study:

  • To compare the realism and trustworthiness of faces generated by diffusion models (DMs) against GANs and real faces.
  • To assess if DM-generated faces elicit trust and avoid the uncanny valley.
  • To understand the implications of increasingly sophisticated GAI for individuals and society.

Main Methods:

  • Experiment 1: 169 participants identified images as real or AI-generated (96 images).
  • Experiment 2: 87 participants rated the trustworthiness of 96 facial images.
  • Utilized diffusion models (DMs) and generative adversarial networks (GANs) for face synthesis.

Main Results:

  • Diffusion models (DMs) generated faces that were less photo-realistic than GANs.
  • Faces generated by DMs were rated as more trustworthy than both GAN-generated and real faces.
  • This suggests a potential shift in perception of AI-generated content.

Conclusions:

  • Diffusion models represent a significant advancement in GAI, producing trustworthy yet less realistic synthetic faces.
  • The increasing realism and accessibility of GAI necessitate understanding and mitigating potential societal harms.
  • Further research is needed to address the ethical and security implications of advanced GAI.