RESEARCH TOPIC / BEHAVIORAL-SCIENCE
Attention, trust, persuasion, choice, habit, and durable behavior change.
GROUNDING MAP
Explore 17 research notes through a shared decision domain. Each article identifies its conclusion, sources, findings, and limitations.
LLMs can shift answers toward a user's stance or pushback, and ratings miss it. Investment reviews should not count AI agreement as independent verification, should re-verify AI objections, and should probe how models update at rollout and on change.
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Based on evidence that button emphasis, obstruction, defaults, and repeated requests alter choices, audit the procedures and visibility of acceptance, refusal, and withdrawal symmetrically.
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Do not mistake general research on psychological safety for AI-specific causal evidence; integrate dissent, error reporting, stop proposals, and retaliation checks into the execution procedures for AI implementation.
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Given habituation to repeated warnings, false alarms, and ignoring due to irrelevant notifications, design a warning budget by risk, display variations, and mandatory stops.
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Based on meta-analysis corrections, re-analysis of publication bias, and large-scale administrative nudge trials, we shift the decision framework to prioritize baseline rates, absolute differences, heterogeneity, and costs in advance.
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We distinguish between load reduction via external memory and the reduction of internal encoding and retrieval, measuring both the performance when AI is usable and the independent execution when it is not.
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From experiments showing that evaluations change when a work is labeled as AI-generated, we separate blind content evaluation from eligibility reviews of provenance, rights, and production process.
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Evidence that ranking methods yield inconsistent attitudinal effects despite altering exposure and usage necessitates the creation of a ranked evaluation separating information diversity, reliability, time utilization, and well-being.
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Distinguish between connection immediately after conversation, loneliness after repeated use, comparison with humans, and relationship replacement; do not extrapolate short-term effects to long-term well-being.
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Based on experiments showing that avoidance after observing algorithmic errors and acceptance with minor correction rights vary, we design operations that are neither mandatory use nor laissez-faire.
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Do not treat names, voices, appearances, and emotional expressions as uniform trust-enhancing measures; design them based on context, user emotions, understanding of capabilities, expectation violations, and handover to humans.
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We organize work design by separating allocation, monitoring, evaluation, and reward by function, and setting minimum standards for autonomy, connectivity pressure, anxiety, and the ability to appeal, in addition to efficiency.
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We present a correction protocol anchored to primary sources, integrating the potential of personalized correction dialogues, the LLM's own epistemic limits, the persistence of effects after warnings, and the decay of effectiveness.
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We design an operation that distinguishes the quality of AI-generated empathetic text from the recipient's perceived understanding, connection, and source preference, while safeguarding human editorial responsibility and recipient choice.
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Personalized conversational AI can correct misconceptions or steer users toward commercial interests without their awareness. We define operational conditions that distinguish legitimate behavioral support from manipulation and show how to measure autonomy.
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Whether AI explanations, confidence displays, judgment order, and user control can truly reconcile the adoption of correct advice with the rejection of incorrect advice is examined by organizing conditions from behavioral experiments.
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Conversation continuity and lasting real-world behavior are distinct. Drawing on meta-analyses and field RCTs of conversational coaches, this article outlines evaluation designs that avoid mistaking short-term responses for behavior change.
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