Evidence map›Paper›PMID 35387204›Full record

ArticleAutonomous agents and multi-agent systems2022

Designing Empathic Virtual Agents: Manipulating Animation, Voice, Rendering, and Empathy to Create Persuasive Agents.

Dhaval Parmar, Stefan Olafsson, Dina Utami, Prasanth Murali, Timothy Bickmore

Abstract read
In one paragraph

Article in Autonomous agents and multi-agent systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dhaval ParmarNortheastern University, Boston, MA, USA.
Stefan OlafssonNortheastern University, Boston, MA, USA.
Dina UtamiNortheastern University, Boston, MA, USA.
Prasanth MuraliNortheastern University, Boston, MA, USA.
Timothy BickmoreNortheastern University, Boston, MA, USA.

Funding

Conversational Agents to Improve Quality of Life in Palliative CareR01NR016131 · NINR · BOSTON MEDICAL CENTER · PI BICKMORE, TIMOTHY W., PAASCHE-ORLOW, MICHAEL · 2016 to 2020
$3.4M
Conversational Agents to Improve HPV Vaccine Acceptance in Primary CareR01CA273208 · NCI · TUFTS MEDICAL CENTER · PI BICKMORE, TIMOTHY W., PAASCHE-ORLOW, MICHAEL · 2022 to 2024
$2.0M
NCI NIH HHS R01 CA273208NINR NIH HHS R01 NR016131
6 · The paper itself

Abstract

Designers of virtual agents have a combinatorically large space of choices for the look and behavior of their characters. We conducted two between-subjects studies to explore the systematic manipulation of animation quality, speech quality, rendering style, and simulated empathy, and its impact on perceptions of virtual agents in terms of naturalness, engagement, trust, credibility, and persuasion within a health counseling domain. In the first study, animation was varied between manually created, procedural, or no animations; voice quality was varied between recorded audio and synthetic speech; and rendering style was varied between realistic and toon-shaded. In the second study, simulated empathy of the agent was varied between no empathy, verbal-only empathic responses, and full empathy involving verbal, facial, and immediacy feedback. Results show that natural animations and recorded voice are more appropriate for the agent's general acceptance, trust, credibility, and appropriateness for the task. However, for a brief health counseling task, animation might actually be distracting from the persuasive message, with the highest levels of persuasion found when the amount of agent animation is minimized. Further, consistent and high levels of empathy improve agent perception but may interfere with forming a trusting bond with the agent.

Indexed as

Agent perceptionAnimation fidelityRendering styleSimulated empathyVirtual agentsVoice quality

Identifiers

PMID35387204
PMCPMC8979496

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.