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BEGIN:VEVENT
DTSTAMP:20260811T201915Z
DTSTART;VALUE=DATE-TIME:20251014T120000
DTEND;VALUE=DATE-TIME:20251014T130000
SUMMARY:Department Psychology Seminars: Dr Belen Lopez-Perez University o
 f Manchester
TZID:Europe/London
UID:20251014-8ac672c4990a69aa01990eb3c4af3e5e@warwick.ac.uk
CREATED:20250922T080133Z
DESCRIPTION:Host: Dr Michaela Gummerum Title: A Digital Shoulder to Cry O
 n: Understanding Why Large Language Models Can Surpass Humans as Extrins
 ic Interpersonal Emotion Regulators Abstract: Does artificial intelligen
 ce (AI) provide emotional support as effective as humans? In this talk\,
  I present research examining when and why large language models (LLMs) 
 are preferred as extrinsic emotion regulators (EER) in non-clinical cont
 exts. Across three studies\, we systematically compared the regulatory e
 ffects and perceived effectiveness of LLM- versus human-generated comfor
 ting messages. The first part of Study 1 (N = 279) used thematic analysi
 s to show that LLM-generated responses largely mirrored human EER strate
 gies\, with some discrepancies between different LLMs. The second part o
 f Study 1 (N = 309) demonstrated that LLMs generally outperformed human 
 regulators with no differences observed for the sadness scenarios and th
 e emotional response of fear (outcome). Study 2 (N = 196) showed again s
 ome LLM advantage in some scenarios and outcomes but emotional validatio
 n (i.e.\, acknowledgement of the target’s emotional response) did not ex
 plain the higher emotional improvement achieved by LLMs. Study 3 (N = 18
 8) identified actionable support (i.e.\, specific and implementable regu
 latory tactics) as a key factor explaining LLM’s regulatory advantage in
  the previous studies. These findings suggest that while LLMs can effect
 ively sometimes surpass human regulators in EER\, such advantage seems t
 o be limited to providing actionable tactics within comforting messages.
  However\, our results also suggest overall advantages seem to be contex
 t and outcome dependent. I will discuss the implications for theories of
  interpersonal emotion regulation and AI-human interaction\, highlightin
 g the critical role of actionable regulatory tactics for successful inte
 rpersonal emotion regulation.
LOCATION:H0.43
CATEGORIES:External speaker,Internal speaker,RAS (Research Active Staff),
 EDandI,PGR Careers,UG Student
LAST-MODIFIED:20250922T080133Z
ORGANIZER;CN=Hannah Austin:
END:VEVENT
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