Machine Learning: Research Area — Prolific Citations Library

Discover 9 peer-reviewed studies in Machine Learning (2018–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 9 peer-reviewed papers in the research area of Machine Learning in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (9 of 9)

Capturing the complexity of human strategic decision-making with machine learning

Authors: JQ Zhu, JC Peterson, B Enke, TL Griffiths

Year: 2025

Published in: Nature Human Behaviour, 2025 - nature.com

Institution: Princeton University, Boston University, Harvard University

Research Area: Strategic decision-making, Machine learning, Computational Cognitive Science

Discipline: Artificial Intelligence

This study used deep neural networks to analyze human strategic decision-making, predicting choices more accurately than existing theories and uncovering the context-dependent nature of reasoning and decision-making in complex games.

Methods: Deep neural networks trained on data from procedurally generated matrix games with over 2,400 variations; models were modified for interpretability.

Key Findings: Human choices and reasoning in initial play of two-player matrix games, focusing on strategic decision-making and response to game complexity.

DOI: https://doi.org/10.1038/s41562-025-02230-5

Citations: 16

Sample Size: 90000
Multimodal machine learning for video based single question mental health assessment

Authors: B Grimm, P Yilmam, B Talbot, L Larsen

Year: 2025

Published in: npj Digital Medicine, 2025 - nature.com

Institution: Videra Health

Research Area: Computational Mental Health Assessment, Multimodal Machine Learning

Discipline: Computational Health, Digital Medicine

A multimodal machine learning model using text (MPNet) and voice (HuBERT) analysis predicts depression, anxiety, and trauma from a single video-based question with strong performance and demographic consistency while significantly reducing assessment time.

Methods: Multimodal analysis combining MPNet for textual data and HuBERT for prosodic voice features trained on video-based responses.

Key Findings: Efficient prediction of self-reported scores for depression (PHQ-9), anxiety (GAD-7), and trauma (PCL-5) from brief video responses.

Sample Size: 2420
Large Language Models can impersonate politicians and other public figures

Authors: S Herbold, A Trautsch, Z Kikteva, A Kaufman

Year: 2024

Published in: arXiv preprint arXiv ..., 2024 - arxiv.org

Institution: University of Passau

Research Area: Computation and Language, Artificial Intelligence, Machine Learning

Discipline: Artificial Intelligence, Political Science, Natural Language Processing

Citations: 7
Interactive Groupwise Comparison for Faster Reinforcement Learning from Human Feedback

Authors: J Kompatscher

Year: 2024

Published in: 2024 - aaltodoc.aalto.fi

Research Area: Reinforcement Learning from Human Feedback (RLHF), Human-Computer Interaction (HCI), Machine Learning (ML)

Discipline: Computer Science

DOI: https://urn.fi/URN:NBN:fi:aalto-202501271897

Citations: 1
Feedback loops in machine learning: a study on the interplay of continuous updating and human discrimination

Authors: K Bauer, R Heigl, O Hinz, M Kosfeld

Year: 2023

Published in: Journal of the Association for ..., 2024 - aisel.aisnet.org

Institution: University of Mannheim

Research Area: Feedback Loops in Machine Learning, Human Discrimination, Ethics in ML-supported Decision Making

Discipline: Computational Social Science

DOI: 10.17705/1jais.00853

Citations: 17
It is an online platform and not the real world, I don't care much: Investigating Twitter Profile Credibility With an Online Machine Learning-Based Tool

Authors: J Li, V Paananen, SA Suryanarayana

Year: 2023

Published in: Proceedings of the ..., 2023 - dl.acm.org

Institution: University of Oulu, University of Helsinki

Research Area: Twitter Credibility, Machine Learning Tools, Online Behavior, Human-Computer Interaction (HCI)

Discipline: Computational Social Science

DOI: https://doi.org/10.1145/3576840.3578308

Citations: 8
Humans forgo reward to instill fairness into AI

Authors: LS Treiman, CJ Ho, W Kool

Year: 2023

Published in: Proceedings of the AAAI Conference on ..., 2023 - ojs.aaai.org

Institution: Massachusetts Institute of Technology, Yale University

Research Area: AI Ethics, Human-AI Interaction, Fairness in Machine Learning Training

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

DOI: https://doi.org/10.1609/hcomp.v11i1.27556

Citations: 6
A framework for rigorous evaluation of human performance in human and machine learning comparison studies

Authors: HP Cowley, M Natter, K Gray-Roncal, RE Rhodes

Year: 2022

Published in: Scientific Reports, 2022 - nature.com

Institution: Johns Hopkins University Applied Physics Laboratory, University of Oxford, Johns Hopkins University, Johns Hopkins University Applied Physics Laboratory, University of Oxford, Johns Hopkins University

Research Area: Human-AI Interaction, Machine Learning Evaluation, AI Evaluation

Discipline: Human-Computer Interaction (HCI)

DOI: https://doi.org/10.1038/s41598-022-08078-3

Citations: 34
Making better use of the crowd: How crowdsourcing can advance machine learning research

Authors: JW Vaughan

Year: 2018

Published in: Journal of Machine Learning Research, 2018 - jmlr.org

Institution: Microsoft Research

Research Area: Crowdsourcing for Machine Learning Research, including data generation, model evaluation, hybrid intelligence systems, behavioral experiments.

Discipline: Machine Learning

Citations: 264