#human-perception News & Analysis

5 articles tagged with #human-perception. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AINeutralarXiv – CS AI · May 287/10

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I Hear, Therefore I Trust: A Socio-Technical Investigation of Humans as Synthetic Speech Detectors

Researchers conducted a study with 47 participants to evaluate how humans detect synthetic speech, testing detection accuracy across authentic, fully synthetic, and partially synthetic utterances under various trust manipulation conditions. The findings reveal that humans perform poorly at detecting fully synthetic speech (below-chance levels) and that trust cues like instructional framing and provenance labeling do not significantly improve detection, though they influence detection behavior.

AIBearisharXiv – CS AI · May 277/10

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Eroding Trust in Real Speech: A Large-Scale Study of Human Audio Deepfake Perception

A comprehensive listening study of 1,768 participants reveals that while humans remain similarly accurate at detecting fake audio (71.2%), they have significantly eroded trust in authentic speech, with real sample detection dropping from 72.7% to 64.1% compared to 2021 baselines. Modern commercial and language model-generated deepfakes pose the greatest challenge to human perception, though ML detectors maintain >94.5% accuracy across all conditions.

AINeutralarXiv – CS AI · May 126/10

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Neuroscience-Inspired Analyses of Visual Interestingness in Multimodal Transformers

Researchers analyzed how Qwen3-VL-8B, a multimodal transformer, encodes visual interestingness—a measure derived from human engagement data—without explicit supervision. Using neuroscience-inspired methods, they found that the model's internal representations align with human-derived interestingness scores, suggesting transformers may capture principles of human attention and perception.

AINeutralarXiv – CS AI · Apr 76/10

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Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News

A research study using JudgeGPT platform found that humans cannot reliably distinguish between AI-generated and human-written news articles across 2,318 judgments from 1,054 participants. The study tested six different LLMs and concluded that user-side detection is not viable, suggesting the need for cryptographic content provenance systems.

AINeutralarXiv – CS AI · Mar 176/10

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Is Seeing Believing? Evaluating Human Sensitivity to Synthetic Video

Research reveals that humans can detect credibility issues in deepfake videos through visual and audio distortions. Three experiments show that both technical artifacts and distortions in synthetic media reduce perceived credibility, though understanding of human perception of deepfakes remains limited.