Human Ai Interaction And Perception: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Human Ai Interaction And Perception (2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Human Ai Interaction And Perception in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Beyond Face Value: Visual and Auditory Signals in Human and Machine Trust Judgments

Authors: N Tyulina, Y Yu, TA Emmanouil, SI Levitan

Year: 2025

Published in: Proceedings of the 7th ACM ..., 2025 - dl.acm.org

Institution: University of Cambridge, University of Bath, University of Edinburgh, New York University

Research Area: Human-AI Interaction, Trust and Perception, Nonverbal Communication

Discipline: Applied Linguistics

Trust judgments are primarily influenced by auditory cues in both humans and multimodal models, though subtle differences in modality weighting exist between them.

Methods: Behavioral experiment with trust ratings of bimodal stimuli across four trust congruence conditions, combined with a multimodal model trained using HuBERT and ResNet-50 with late fusion, analyzed using Permutation Feature Importance (PFI).

Key Findings: The construction of trust from visual and auditory signals in both humans and multimodal models, focusing on modality dominance and feature weighting.

Sample Size: 150
Improving Experimental Methods to Capture Real-World Human-AI Perceptions and Interactions

Authors: N Haduong

Year: 2025

Published in: 2025 - search.proquest.com

Institution: University of Washington

Research Area: Human-AI Interaction and Perception

Discipline: Human-Computer Interaction (HCI)

The research focuses on developing methodologies to bridge the gap between controlled laboratory studies and real-world human-AI perceptions and interactions, promoting task immersion and intrinsic motivation to model realistic behaviors.

Methods: Used task immersion techniques, domain-specific recruitment, error taxonomy development, and CPS-TaskForge environment generator for systematic study of collaborative problem solving and AI-assisted decision-making.

Key Findings: Human perceptions of AI in collaborative problem solving, understanding risks in AI-assisted decision making, and user behavior under performance pressure with AI advice.