Large language models display the identifiable victim effect at roughly twice the human baseline, strongly amplified by instruction tuning and chain-of-thought prompting but inverted by reasoning-specialized models.
Social Influence 11(3), 199–215 (Jul 2016)
3 Pith papers cite this work. Polarity classification is still indexing.
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Polite chatbot feedback lowers psychological reactance and boosts behavioral intentions but lacks engagement, whereas verbal leakage heightens surprise and engagement at the expense of increased reactance.
Proposes a behavioral model of positive friction to characterize beneficial obstacles in AI user experiences and developer processes, diagnose needs, and suggest design solutions.
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Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models
Large language models display the identifiable victim effect at roughly twice the human baseline, strongly amplified by instruction tuning and chain-of-thought prompting but inverted by reasoning-specialized models.
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Polite But Boring? Trade-offs Between Engagement and Psychological Reactance to Chatbot Feedback Styles
Polite chatbot feedback lowers psychological reactance and boosts behavioral intentions but lacks engagement, whereas verbal leakage heightens surprise and engagement at the expense of increased reactance.
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Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction
Proposes a behavioral model of positive friction to characterize beneficial obstacles in AI user experiences and developer processes, diagnose needs, and suggest design solutions.