Explore 4 peer-reviewed studies by Y Kim in Probabilistic reasoning and Bayesian cognition (2024–2026). Discover research powered by Prolific's participant panel.
This page lists 4 peer-reviewed papers authored or co-authored by Y Kim in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
-
Authors: L Qiu, F Sha, K Allen, Y Kim, T Linzen, S van Steenkiste
Year: 2026
Published in: Nature …, 2026 - nature.com
Institution: Meta, Google DeepMind, Massachusetts Institute of Technology, Google Research, Google
Research Area: Probabilistic reasoning, Bayesian cognition, Neural language models, Reasoning, AI Evaluations
Discipline: Machine learning, Artificial intelligence
This paper sits at the intersection of machine learning and computational cognitive science, showing that large language models can acquire generalized probabilistic reasoning by being trained to imitate Bayesian belief updating rather than relying on prompting or heuristics.
Citations: 8
-
Authors: M Groh, A Sankaranarayanan, N Singh, DY Kim
Year: 2025
Published in: Nature ..., 2024 - nature.com
Institution: Northwestern University, Massachusetts Institute of Technology
Research Area: Deepfakes, Media Forensics, Human Perception of AI-Generated Content, Political Communication
Discipline: Computational Social Science
Humans are better at detecting deepfake political speeches using audio-visual cues than relying on text alone; state-of-the-art text-to-speech audio makes deepfakes harder to discern.
Methods: Five pre-registered randomized experiments with varied base rates of misinformation, audio sources, question framings, and media modalities were conducted.
Key Findings: Human accuracy in discerning real political speeches from deepfakes across media formats and contextual variables.
DOI: https://doi.org/10.1038/s41467-024-51998-z
Citations: 63
Sample Size: 2215
-
Authors: SSY Kim, JW Vaughan, QV Liao, T Lombrozo
Year: 2025
Published in: Proceedings of the ..., 2025 - dl.acm.org
Institution: Wake Forest University, University of Illinois at Urbana-Champaign, Princeton University, University of California Berkeley
Research Area: Appropriate Reliance on LLMs, Explainable AI, Human-AI Interaction, Cognitive Psychology
Discipline: Cognitive Psychology, Artificial Intelligence, Human-Computer Interaction (HCI)
The study examines factors that influence users' reliance on LLM responses, finding explanations increase reliance, while sources and inconsistent explanations reduce reliance on incorrect responses.
Methods: Think-aloud study followed by a pre-registered, controlled experiment to assess the impact of explanations, sources, and inconsistencies in LLM responses on user reliance.
Key Findings: Users' reliance on LLM responses, accuracy, and the influence of explanations, inconsistencies, and sources on these measures.
DOI: https://doi.org/10.1145/3706598.3714020
Citations: 38
Sample Size: 308
-
Authors: G Lee, JY Huh, HY Kim
Year: 2024
Published in: International Textile and Apparel ..., 2024 - iastatedigitalpress.com
Institution: University of Minnesota, Texas Tech University
Research Area: Marketing, Consumer Behavior
Discipline: Textile, Apparel Studies, Marketing, Social Science
DOI: https://doi.org/10.31274/itaa.17436
Citations: 2