Evidence map›Paper›PMID 40588597›Full record

ArticleNature human behaviour2025

Comparing the value of perceived human versus AI-generated empathy.

Matan Rubin, Joanna Z Li, Federico Zimmerman, Desmond C Ong, Amit Goldenberg, Anat Perry

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  14. A Framework for Automation in Psychotherapy.Current directions in psychological science · 2025
    Article
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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

6 authors.

Matan RubinPsychology Department, Hebrew University of Jerusalem, Jerusalem, Israel. Matan.rubin@mail.huji.ac.il.ORCID http://orcid.org/0009-0008-7869-4993
Joanna Z LiHarvard Business School, Harvard University, Boston, MA, USA.ORCID http://orcid.org/0009-0000-6010-0669
Federico ZimmermanHarvard Business School, Harvard University, Boston, MA, USA.
Desmond C OngPsychology Department, The University of Texas at Austin, Austin, TX, USA.
Amit Goldenberg *Harvard Business School, Harvard University, Boston, MA, USA.ORCID http://orcid.org/0000-0002-9417-5682
Anat Perry *Psychology Department, Hebrew University of Jerusalem, Jerusalem, Israel. Anat.perry@mail.huji.ac.il.ORCID http://orcid.org/0000-0003-2329-856X

Funding

Mind and Life Institute not applicable
6 · The paper itself

Abstract

Artificial intelligence (AI) and specifically large language models demonstrate remarkable social-emotional abilities, which may improve human-AI interactions and AI's emotional support capabilities. However, it remains unclear whether empathy, encompassing understanding, 'feeling with' and caring, is perceived differently when attributed to AI versus humans. We conducted nine studies (n = 6,282) where AI-generated empathic responses to participants' emotional situations were labelled as provided by either humans or AI. Human-attributed responses were rated as more empathic and supportive, and elicited more positive and fewer negative emotions, than AI-attributed ones. Moreover, participants' own uninstructed belief that AI had aided the human-attributed responses reduced perceived empathy and support. These effects were replicated across varying response lengths, delays, iterations and large language models and were primarily driven by responses emphasizing emotional sharing and care. Additionally, people consistently chose human interaction over AI when seeking emotional engagement. These findings advance our general understanding of empathy, and specifically human-AI empathic interactions.

Indexed as

Artificial IntelligenceEmpathyAdultEmotionsFemaleHumansMaleYoung Adult

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.