Explore 40 peer-reviewed papers in Behavioral Science (2024–2025). Academic research using Prolific for high-quality human data collection.
This page lists 40 peer-reviewed papers in the discipline of Behavioral Science in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: F Salvi, M Horta Ribeiro, R Gallotti, R West
Year: 2025
Published in: Nature Human Behaviour, 2025 - nature.com
Institution: EPFL, Fondazione Bruno Kessle, Princeton University
Research Area: Conversational Persuasion of LLM, Human-Computer Interaction (HCI), Behavioral Science, LLM
Discipline: Behavioral Science
GPT-4 can use personalized arguments to be more persuasive in debates, outperforming humans in 64.4% of AI-human comparisons when personalization is applied.
Methods: Preregistered controlled study involving multiround debates with random assignment to conditions focusing on AI-human comparisons, personalization, and opinion strength.
Key Findings: Effectiveness of persuasion by GPT-4, especially when using personalized arguments, compared to humans in debates.
Citations: 65
Sample Size: 900
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Authors: F Sun, N Li, K Wang, L Goette
Year: 2025
Published in: arXiv preprint arXiv:2505.02151, 2025 - arxiv.org
Institution: HKU Business School
Research Area: LLM Overconfidence and Human Bias Amplification, Bias, LLM
Discipline: Artificial Intelligence, Behavioral Science
Large language models (LLMs) exhibit overconfidence, amplifying human bias, especially in cases where their certainty declines, and their input doubles overconfidence in human decision making despite improving accuracy.
Methods: Algorithmically constructed reasoning problems with known ground truths were used to evaluate LLMs' confidence; comparisons were drawn with human performance using similar experimental protocols.
Key Findings: LLM confidence levels, correctness probabilities, comparison of bias between LLMs and humans, and effects of LLM input on human decision making.
Citations: 21
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Authors: SMC Loureiro, L Hollebeek, RA Rather
Year: 2025
Published in: Journal of Marketing ..., 2025 - Taylor & Francis
Institution: Universitário de Lisboa
Research Area: Marketing Communications, Social Media, Behavioral Science
Discipline: Marketing, Behavioral Science
Personalized advertising on social media enhances brand engagement and alleviates privacy concerns, with privacy concerns having no significant effect on consumer-brand engagement.
Methods: Grounded in social exchange theory, the study utilized a quantitative survey to assess relationships between personalized advertising, information control, privacy concerns, advertising avoidance, and brand engagement.
Key Findings: The interplay between personalized advertising, consumer brand engagement, privacy concerns, information control, and advertising avoidance.
Citations: 17
Sample Size: 429
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Authors: A Agarwal, SY Lee
Year: 2025
Published in: Information Systems ..., 2025 - pubsonline.informs.org
Institution: University of Texas
Research Area: Information Systems, Behavioral Economics, Social Media Marketing, Advertising
Discipline: Information Systems Research, Marketing, Behavioral Science
This academic article explores a specific topic within the field of Information Systems Research.
Citations: 14
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Authors: A Söderström, A Shatte
Year: 2025
Published in: Behavior Research ..., 2021 - Springer
Institution: University of Helsinki
Research Area: Intelligent Agents, Health Research Methodology, Behavioral Research
Discipline: Research Methodology, Behavioral Science
The study found that chatbot-assisted surveys modestly improve data quality, with most users finding the chatbots helpful and widely using them.
Methods: Randomized participants into chatbot-supported and unassisted survey conditions; assessed chatbot use, user satisfaction, and data quality via validated and deliberately confusing challenge items.
Key Findings: Effects of chatbot assistance on data quality, user satisfaction, and usage patterns in online questionnaires.
DOI: https://doi.org/10.3758/s13428-021-01574-w
Citations: 7
Sample Size: 300
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Authors: TS Behrend, RN Landers
Year: 2025
Published in: Journal of Business and Psychology, 2025 - Springer
Institution: University of Nebraska-Lincoln, University of Minnesota
Research Area: LLM in Behavioral Science Research, AI-Assisted Research Methodology
Discipline: Behavioral Science, Psychology, Artificial Intelligence
The paper proposes a framework with five use cases for integrating large language models into survey and experimental research, introduces the Qualtrics-AI Link (QUAIL) tool, and highlights technical and ethical considerations for using LLMs effectively and validly.
Methods: The paper outlines a decision-making framework for five potential uses of LLMs in survey and experimental design, introduces software (QUAIL) for integrating LLM knowledge into Qualtrics, and details technical steps such as prompt engineering, model testing, and validity monitoring.
Key Findings: Applications, implementation strategies, and ethical considerations of large language models in psychological research material development.
DOI: https://doi.org/10.1007/s10869-025-10035-6
Citations: 6
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Authors: TK Koh
Year: 2025
Published in: Organization Science, 2025 - pubsonline.informs.org
Institution: University of North Carolina Chapel Hill
Research Area: Crowdsourcing Contests, Feedback Use, Priming Intervention, Organizational Science
Discipline: Behavioral Sciences
The paper examines how solvers in crowdsourcing contests prioritize feedback from seekers over peers, even when equally constructive, and proposes an intervention to improve feedback usage for better outcomes.
Methods: The study involved a field survey and three online experiments to test the theorized source effect and the proposed feedback evaluation intervention.
Key Findings: Solvers' feedback usage patterns, the source effect of feedback (seeker vs. peer), and the influence of feedback constructiveness on idea quality and solvers’ winning prospects.
Citations: 5
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Authors: C Chen, Z Cui
Year: 2025
Published in: Journal of Medical Internet Research, 2025 - jmir.org
Institution: Medical College of Wisconsin
Research Area: Trust in AI, AI-assisted diagnosis, Health communication, Healthcare human-AI interaction
Discipline: Digital Health, Human-Computer Interaction (HCI), Behavioral Science
Patients trust and are more likely to seek help from doctors explicitly avoiding AI-assisted diagnosis rather than those using extensive or moderate AI, highlighting a strong aversion to AI in healthcare settings.
Methods: A randomized, web-based 4-group survey experiment was conducted with controls for sociodemographic factors and analysis using regression, mediation, and moderation techniques.
Key Findings: Trust in and intention to seek medical help from health care professionals using AI-assisted diagnosis versus those avoiding AI, and the influence of demographic, social, and experiential factors.
DOI: https://doi.org/10.2196/66083
Citations: 4
Sample Size: 1762
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Authors: Y Ai, A von Mühlenen
Year: 2025
Published in: Scientific Reports, 2025 - nature.com
Institution: University of Warwick
Research Area: Social media, Mental Health, Behavioral Science
Discipline: Behavioral Science
Negative social media comments significantly increase anxiety and decrease mood, with younger adults showing heightened sensitivity compared to older adults.
Methods: Participants shared blog posts on a simulated internet forum and were exposed to negative, neutral, or positive comments; mood and anxiety levels were measured using validated scales.
Key Findings: Impact of negative, neutral, and positive social media comments on anxiety and mood across adult participants.
Citations: 3
Sample Size: 128
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Authors: D OConnell, A Bautista
Year: 2025
Published in: ... Student Journal of ..., 2025 - journals.library.columbia.edu
Institution: University of Houston, Webster University
Research Area: Crowdsourcing Research Methodology, Human-Computer Interaction (HCI)
Discipline: Computational Social Science, Behavioral Research
Prolific outperforms MTurk in participant data quality and affordability for online survey-based research.
Methods: Data from participants recruited via MTurk and Prolific were analyzed for cost, attention measures, participation duration, and internal consistency.
Key Findings: Comparison of data quality and cost-effectiveness between MTurk and Prolific for online survey recruitment.
Citations: 1
Sample Size: 699
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Authors: L S. Treiman, CJ Ho, W Kool
Year: 2025
Published in: Proceedings of the 2025 ACM Conference ..., 2025 - dl.acm.org
Institution: Washington University in St. Louis, National Cheng Kung University
Research Area: Human-AI Interaction, Cognitive Science, Behavioral Research in AI Training
Discipline: Human-Computer Interaction (HCI), Behavioral Science
Participants tend to rely on intuition (fast thinking) rather than deliberation (slow thinking) when training AI agents in the ultimatum game, impacting human-AI collaboration system design.
Methods: Participants trained an AI agent in the ultimatum game to analyze whether their training decisions aligned more with intuitive or deliberative cognitive processes.
Key Findings: The cognitive processes (fast vs. slow thinking) underlying human decision-making during AI training.
DOI: https://dl.acm.org/doi/abs/10.1145/3715275.3732177
Citations: 1
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Authors: B Katz, N Abdelgawad, D Friedberg, P Roberts, S Misra
Year: 2025
Published in: Innovation in Aging, 2025•pmc.ncbi.nlm.nih.gov
Institution: Virginia Tech
Research Area: Human–AI Interaction (HCI), Technology Perception
Discipline: Behavioral Science
Age significantly influences perceptions of generative AI tools, with older individuals perceiving more benefits and fewer risks compared to younger individuals; thinking dispositions also play a role.
Methods: A nationally representative survey of US adults conducted via the Prolific platform using various AI-relevant scales, including attitudes, risks, benefits, frequency of use, expertise, and literacy assessments.
Key Findings: Demographic factors, industry types, thinking dispositions, and attitudes toward generative AI tools, including risk and utility perceptions.
Citations: 1
Sample Size: 500
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Authors: A Klingbeil, C Grützner, P Schreck
Year: 2024
Published in: Computers in Human Behavior, 2024 - Elsevier
Institution: University of Hohenheim, University of Hohenheim, University of Hohenheim
Research Area: Trust in AI, Overreliance on AI, Human-AI Interaction
Discipline: Human-Computer Interaction (HCI), Artificial Intelligence, Behavioral Science
The study found that individuals tend to overrely on AI-generated advice in uncertain situations, often to the detriment of their own decisions and third parties, despite contradicting contextual information or their own judgment.
Methods: A domain-independent, incentivized, interactive behavioral experiment was conducted to analyze user behavior in decision-making scenarios involving AI advice.
Key Findings: Extent and impact of user reliance on AI advice, including its effects on decision efficiency and outcomes for themselves and others.
DOI: https://doi.org/10.1016/j.chb.2024.108352
Citations: 247
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Authors: HR Kirk, I Gabriel, C Summerfield, B Vidgen
Year: 2024
Published in: Humanities and Social ..., 2025 - nature.com
Institution: Oxford Internet Institute, University of Oxford
Research Area: Socioaffective Alignment in Human-AI Relationships, AI Ethics, Behavioral Science
Discipline: Artificial Intelligence, Behavioral Science
The paper emphasizes the need for socioaffective alignment in human-AI relationships to ensure AI systems support human psychological needs rather than exploit them, as interactions with AI transition from transactional to sustained engagement.
Methods: Conceptual analysis of socioaffective dynamics in human-AI interactions, framed through psychological theories and principles.
Key Findings: Exploration of how AI systems impact socioaffective relationships, psychological needs, autonomy, companionship, and human well-being.
DOI: https://doi.org/10.1057/s41599-025-04532-5
Citations: 59
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Authors: A von Schenk, V Klockmann
Year: 2024
Published in: ... on Psychological Science, 2025 - journals.sagepub.com
Institution: Max Planck Institute
Research Area: Social Preferences, Behavioral Economics, Human-Machine Interaction
Discipline: Behavioral Science
Humans exhibit stronger social preferences toward machines when they know machine payoffs benefit a human recipient, and weak preferences when payoff information is absent, suggesting belief formation is self-serving.
Methods: Conducted an online experiment with participants and follow-up surveys to compare the impact of different implementations of machine payoffs and information transparency on social preferences.
Key Findings: Social preferences and reciprocity behaviors toward machines with varying payoff structures and transparency about the beneficiaries.
DOI: https://doi.org/10.1177/17456916231194949
Citations: 40
Sample Size: 1198
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Authors: LS Treiman, CJ Ho, W Kool
Year: 2024
Published in: Proceedings of the National Academy of ..., 2024 - pnas.org
Institution: Massachusetts Institute of Technology, Yale University, Washington University in St. Louis
Research Area: AI Ethics, Behavioral Economics, Decision-Making in AI Systems
Discipline: Artificial Intelligence, Behavioral Science
People alter their behavior when they know their actions will train AI, leading to unintentional habits and biased training data for AI systems.
Methods: Five studies were conducted using the ultimatum game; participants were tasked with deciding on monetary splits proposed by either humans or AI, with some informed their decisions would train the AI.
Key Findings: Behavioral changes in participants when training AI, persistence of these changes over time, and implications for AI training bias.
DOI: https://doi.org/10.1073/pnas.2408731121
Citations: 13
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Authors: Eyal Peer
Year: 2024
Published in: CAMBRIDGE
Institution: Hebrew University, University of Cambridge
Research Area: Crowdsourcing, Research Methodology in Behavioral and Social Sciences
Discipline: Social, Behavioral Sciences
Citations: 7
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Authors: Z Li, M Yin
Year: 2024
Published in: Advances in Neural Information Processing ..., 2024 - proceedings.neurips.cc
Institution: Purdue University
Research Area: Human Behavior Modeling, Explainable AI, Decision Making in AI systems.
Discipline: Artificial Intelligence, Behavioral Science
DOI: https://doi.org/10.52202/079017-0163
Citations: 7
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Authors: S Schmer-Galunder, R Wheelock, Z Jalan
Year: 2024
Published in: Proceedings of the ..., 2024 - ojs.aaai.org
Institution: Google DeepMind, Google, Accenture, Amazon
Research Area: AI Ethics and Prosocial Data Annotation
Discipline: Artificial Intelligence, Ethics, Behavioral Science
DOI: https://doi.org/10.1609/aies.v7i1.31726
Citations: 3
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Authors: DJ Kravitz, SR Mitroff
Year: 2024
Published in: Policy Insights from ..., 2024 - journals.sagepub.com
Institution: University of North Carolina at Chapel Hill, Duke University
Research Area: Crowdsourcing, Behavioral Sciences, Policy Applications
Discipline: Behavioral Science
Citations: 2