Patric R. Spence
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Patric R. Spence has a total of 101 co-authors. Here are the top co-authors by number of shared publications:
- Kenneth A. Lachlan
University of Connecticut
64 shared publications - Xialing Lin
University of Central Florida
28 shared publications - Chad Edwards
Western Michigan University
18 shared publications Autumn Edwards
School of Communication, Western Michigan University, Kalamazoo, U.S.A.
11 shared publicationsDavid Westerman
North Dakota State University
10 shared publications- Autumn Edwards
Western Michigan University
7 shared publications
Latest Publications
- 7 September 2026
- Published by Taylor & Francis in Western Journal of Communication
- p. 1-27
Identity Masking as a Heuristic Cue in Risk Communication: Effects on Risk Perception and Behavioral Intentions
News coverage often shapes how the public understands and responds to risks, particularly when journalists use reporting techniques that highlight, emphasize, or obscure aspects of a story. The sty...
- 7 September 2026
- Published by Taylor & Francis in Western Journal of Communication
- p. 1-27
Identity Masking as a Heuristic Cue in Risk Communication: Effects on Risk Perception and Behavioral Intentions
News coverage often shapes how the public understands and responds to risks, particularly when journalists use reporting techniques that highlight, emphasize, or obscure aspects of a story. The sty...
- 18 August 2026
- Published by Taylor & Francis in Communication Studies
- p. 1-17
Cognitive and Modality Constraints in Crisis Messages as Boundary Conditions for Convergence
During Hurricane Harvey in 2017 while flood waters covered streets across Houston one single phrase was recognized across every platform: “Turn Around, Don’t Drown.” The message, originally develop...
- 18 August 2026
- Published by Taylor & Francis in Communication Studies
- p. 1-17
Cognitive and Modality Constraints in Crisis Messages as Boundary Conditions for Convergence
During Hurricane Harvey in 2017 while flood waters covered streets across Houston one single phrase was recognized across every platform: “Turn Around, Don’t Drown.” The message, originally develop...
- 3 June 2026
- Published by Taylor & Francis in Communication Education
- p. 1-16
Just press “teach”? A longitudinal study of human expectations and responses to a robot instructor
- 3 June 2026
- Published by Taylor & Francis in Communication Education
- p. 1-16
Just press “teach”? A longitudinal study of human expectations and responses to a robot instructor
- 19 February 2026
- Published by Taylor & Francis in Southern Communication Journal
- Vol. 91 (3) , 175-192
Keeping Risk in Proportion? How Exemplars Amplify Risk Perceptions in Low-Risk Audiences
- 19 February 2026
- Published by Taylor & Francis in Southern Communication Journal
- Vol. 91 (3) , 175-192
Keeping Risk in Proportion? How Exemplars Amplify Risk Perceptions in Low-Risk Audiences
- 31 January 2026
- Published by Communication and Social Robotics Labs in Human-Machine Communication
- Vol. 12 (1) , 139-162
Normalizing AI: The Evanishing Effect and Rethinking the Machine Heuristic
This study examines how people evaluate factual claims attributed to human journalists versus artificial intelligence (AI) news bots, and how these evaluations are shaped by individual endorsement of the machine heuristic. An online experiment (N = 233) used a 2 (Agent: human vs. AI) × 2 (Information Veracity: accurate vs. inaccurate) design in which participants viewed a short tweet embedded in either a human or AI profile and then rated perceptions of source credibility, objectivity, bias, and accuracy. Endorsement of an AI-focused machine heuristic was significantly associated with higher source credibility, perceived objectivity, and perceived accuracy, and lower perceived bias, whereas agent type showed no significant effects in the full sample. In post hoc analyses, the difference by agent emerged only among participants high in the machine heuristic. These findings suggest boundary conditions of the machine heuristic: while it shapes perceptions, its operation appears increasingly agnostic to human versus AI source cues. We call this the evanishing effect, wherein source cues (human vs. AI) are recognized but do not systematically alter evaluations, so neither human nor machine agents are consistently preferred.
- 31 January 2026
- Published by Communication and Social Robotics Labs in Human-Machine Communication
- Vol. 12 (1) , 139-162
Normalizing AI: The Evanishing Effect and Rethinking the Machine Heuristic
This study examines how people evaluate factual claims attributed to human journalists versus artificial intelligence (AI) news bots, and how these evaluations are shaped by individual endorsement of the machine heuristic. An online experiment (N = 233) used a 2 (Agent: human vs. AI) × 2 (Information Veracity: accurate vs. inaccurate) design in which participants viewed a short tweet embedded in either a human or AI profile and then rated perceptions of source credibility, objectivity, bias, and accuracy. Endorsement of an AI-focused machine heuristic was significantly associated with higher source credibility, perceived objectivity, and perceived accuracy, and lower perceived bias, whereas agent type showed no significant effects in the full sample. In post hoc analyses, the difference by agent emerged only among participants high in the machine heuristic. These findings suggest boundary conditions of the machine heuristic: while it shapes perceptions, its operation appears increasingly agnostic to human versus AI source cues. We call this the evanishing effect, wherein source cues (human vs. AI) are recognized but do not systematically alter evaluations, so neither human nor machine agents are consistently preferred.
- 1 November 2025
- Published by Springer Nature
- p. 19-26
Life Is Lab: Our Ten Tips
- 6 October 2025
- Published by Taylor & Francis in Communication Studies
- Vol. 77 (6) , 951-974
Revisiting Identity Cues and Helpfulness Heuristics in Online Learning: Does Knowing Who Helps Matter?
- 6 October 2025
- Published by Taylor & Francis in Communication Studies
- Vol. 77 (6) , 951-974
Revisiting Identity Cues and Helpfulness Heuristics in Online Learning: Does Knowing Who Helps Matter?
- Open Access
- 1 October 2025
- Published by Communication and Social Robotics Labs in Human-Machine Communication
- Vol. 11, 7-12
Generative AI: Another Chapter of Human-Machine Communication
This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.
- 1 October 2025
- Published by Communication and Social Robotics Labs in Human-Machine Communication
- Vol. 11, 7-12
Open AccessGenerative AI: Another Chapter of Human-Machine Communication
This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.
- 30 September 2025
- Published by Taylor & Francis in Review of Communication
- Vol. 26 (2) , 130-143
How scientists study innovations on the background of a systematic review in an emerging field of research: the VBA Alexa
- 30 September 2025
- Published by Taylor & Francis in Review of Communication
- Vol. 26 (2) , 130-143
How scientists study innovations on the background of a systematic review in an emerging field of research: the VBA Alexa