Behavioral Science: Research Area — Prolific Citations Library

Discover 22 peer-reviewed studies in Behavioral Science (2021–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 22 peer-reviewed papers in the research area of Behavioral Science in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 22)

On the conversational persuasiveness of GPT-4

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
Engaging with (vs. avoiding) personalized advertising on social media

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
Participant Interactions with Artificial Intelligence: Using Large Language Models to Generate Research Materials for Surveys and Experiments

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
An experimental online study on the impact of negative social media comments on anxiety and mood

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
Improving annotation quality: empirical insights into bias, human-AI collaboration, and workflow design

Authors: J Beck

Year: 2025

Published in: 2025 - edoc.ub.uni-muenchen.de

Institution: Ludwig-Maximilians-Universität München, University of Bayreuth

Research Area: Annotation Quality, Human-AI Collaboration, Behavioral Science, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI)

The study empirically evaluates annotation bias, proposes strategies to reduce its impact, and explores the use of large language models in automated and hybrid annotation workflows.

Methods: Empirical assessments and experimental evaluations involving annotation workflows and large language models.

Key Findings: Annotation bias, annotation quality, and the effectiveness of hybrid workflows integrating human input and AI models.
Large language model in creative work: The role of collaboration modality and user expertise

Authors: Z Chen, J Chan

Year: 2024

Published in: Management Science, 2024 - pubsonline.informs.org

Institution: University of Texas Dallas

Research Area: Human-AI Interaction, Creative Work, Behavioral Science

Discipline: Social Science

Using large language models (LLMs) as sounding boards improves ad content quality for nonexpert users, while using LLMs as ghostwriters can negatively impact expert users due to anchoring effects.

Methods: An experiment comparing ad copy creation with and without LLM assistance, focusing on two collaboration modalities: ghostwriting and sounding board approaches. Ad performance was measured via social media click rates, supported by textual analysis.

Key Findings: Effectiveness of LLM collaboration modalities (ghostwriting vs. sounding board) on ad quality and business outcomes for expert and nonexpert users.

DOI: https://doi.org/10.1287/mnsc.2023.03014

Citations: 180
Comparing attentional disengagement between Prolific and MTurk samples

Authors: DA Albert, D Smilek

Year: 2024

Published in: Scientific Reports, 2023 - nature.com

Institution: University of Waterloo, University of Waterloo

Research Area: Crowdsourcing Research Methods, Behavioral Science, Human-Computer Interaction (HCI)

Discipline: Psychological Science

Prolific participants exhibited lower levels of attentional disengagement compared to MTurk participants, with risk conditions and platform traits influencing task performance and disengagement.

Methods: Participants from Prolific and MTurk completed an attention task with varying error risk levels (high vs. low), and attentional disengagement was measured using task performance, self-reported mind wandering, and multitasking.

Key Findings: Attentional disengagement through task performance, mind wandering, and multitasking under different risk conditions across two recruitment platforms (Prolific and MTurk).

Citations: 150

Sample Size: 80
Why human-AI relationships need socioaffective alignment

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
Crowdsourcing Academic Online Research on Prolific

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
Crowdsourced Data Collection Opens New Avenues for the Behavioral Sciences to Impact Real-World Applications

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
Defaults: a double-edged sword in governing common resources

Authors: Eladio Montero-Porras Elias Fernández Domingos

Year: 2024

Published in: ArXiv

Institution: University of California, Université Catholique de Lille, Université Libre de Bruxelles, Vrije Universiteit Brussel

Research Area: Behavioral Science, Common Pool Resource Dilemma (CPRD)

Discipline: Computer Science, Game Theory (cs.GT), Computers, Society (cs.CY)
Influencing human-AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness

Authors: P Pataranutaporn, R Liu, E Finn, P Maes

Year: 2023

Published in: Nature Machine Intelligence, 2023 - nature.com

Institution: University of California Irvine

Research Area: Human-AI Interaction, Behavioral Science

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence

DOI: https://doi.org/10.1038/s42256-023-00720-7

Citations: 180
Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs

Authors: C Huber, A Dreber, J Huber, M Johannesson

Year: 2023

Published in: Proceedings of the ..., 2023 - pnas.org

Institution: Aalto University School of Business, Stockholm School of Economics, Stockholm University, University of Innsbruck

Research Area: Moral Behavior, Competition, Behavioral Science, Meta-analysis of Experimental Designs

Discipline: Behavioral Science

Citations: 59
Best practices and ethical considerations for crowd-sourced data in the behavioral sciences

Authors: R Kapitany, C Kavanagh

Year: 2023

Published in: 2023 - OSF

Research Area: Crowdsourcing ethics, best practices in behavioral science research

Discipline: Behavioral Science

Citations: 4
How is online self-reported weight compared with image-captured weight? A comparative study using data from an online longitudinal study of young adults

Authors: MC Whatnall, KZ Kolokotroni, TE Fozard

Year: 2023

Published in: The American Journal of ..., 2023 - Elsevier

Institution: The University of Sheffield, The University of Edinburgh

Research Area: Public Health, Behavioral Science, Digital Health

Discipline: Clinical Nutrition, Public Health, Behavioral Science

Citations: 4
Demographic, personality, and social cognition correlates of coronavirus guideline adherence in a US sample.

Authors: T Bogg, E Milad

Year: 2022

Published in: Health Psychology, 2020 - psycnet.apa.org

Institution: Wayne State University

Research Area: Health Psychology, Behavioral Science, Social Cognition related to public health adherence.

Discipline: Psychology

Citations: 153
Crowdsourcing in cognitive and systems neuroscience

Authors: BP Johnson, E Dayan, N Censor

Year: 2022

Published in: The ..., 2022 - journals.sagepub.com

Research Area: Cognitive Neuroscience, Systems Neuroscience, Crowdsourcing, Behavioral Neuroscience, Motor Control, Motor Learning

Discipline: Neuroscience, Psychology

DOI: https://doi.org/10.1177/10738584211017018

Citations: 25
MTurk and online panel research: The impact of COVID-19, bots, TikTok, and other contemporary developments

Authors: JK Goodman, S Wright

Year: 2022

Published in: 2022 - papers.ssrn.com

Institution: University of Kentucky, University of Wyoming

Research Area: Research Methods, Crowdsourcing, Behavioral Science

Discipline: Behavioral Science

Citations: 16
AI-induced indifference: Unfair AI reduces prosociality

Authors: RZ Zhang, EJ Kyung, C Longoni, L Cian, K Mrkva

Year: 2022

Published in: Cognition, 2025 - Elsevier

Institution: The Hong Kong University of Science and Technology, China Europe International Business School, University of South Florida, HEC Paris, University of Mannheim

Research Area: Social Psychology, Behavioral Science, Prosocial Behavior, AI Ethics.

Discipline: Social Psychology, Behavioral Science

DOI: https://doi.org/10.1016/j.cognition.2024.105937

Citations: 13
To believe or not to believe? Generative AI and the 'trust dilemma'in charitable and medical crowdfunding

Authors: C Arnold, LZ Xu, K Saffarizadeh

Year: 2021

Published in: Behaviour & Information ..., 2025 - Taylor & Francis

Institution: Northwestern Mutual Data Science Institute, Marquette University

Research Area: Generative AI, Crowdfunding, Trust in AI, Human-Computer Interaction (HCI), Behavioral Science

Discipline: Human-Computer Interaction (HCI), Behavioral Science