Welcome to Psychiatryai.com: Latest Evidence - RAISR4D

Estimated reading time for CME/CPD: 2 mins

Effects of a single-session facial emotion recognition training using the MTS paradigm in cognitively unimpaired older adults

Summarise with AI (MRCPsych/FRANZCP)

Trends Psychiatry Psychother. 2026 Jan 6. doi: 10.47626/2237-6089-2025-1082. Online ahead of print.

ABSTRACT

INTRODUCTION: Facial emotion recognition (FER) is the ability to interpret the feelings and emotions of others. Given the decline in FER ability observed in older adults, intervention studies to assess the effects of training this skill are essential. Therefore, this study aims to evaluate the effects of FER training in cognitively unimpaired older adults.

METHOD: A randomized, crossover clinical trial was conducted. Twenty-two individuals aged 60 years or older, without indications of depression or cognitive decline, were selected. Participants completed one session of FER training and one control training session through a Matching-to-Sample (MTS) procedure on a portable touchscreen computer, with a seven-day interval between sessions. The primary outcomes were total accuracy and accuracy by emotion in two FER tasks, using dynamic and static stimuli.

RESULTS: In the dynamic stimuli task, repeated-measures ANOVA revealed significant differences in overall emotion accuracy (F2,40 = 4.592; p = 0.016; η²p = 0.187). Bonferroni post hoc analysis indicated improvement following FER training compared to baseline (p = 0.004). Additionally, ANOVA showed improved recognition of happiness (F2,40 = 7.732; p = 0.001; η²p = 0.279) following FER training compared to control training. Regarding the static stimuli task, ANOVA revealed significant differences in scores only for disgust (F2,40 = 5.748; p = 0.006; η²p = 0.223), with improvement following FER training compared to baseline (p = 0.005).

CONCLUSION: FER training increased overall performance accuracy, particularly for happiness and disgust. Future studies involving multiple training sessions, larger sample sizes, and clinical populations are essential for the generalization of these findings.

PMID:41494082 | DOI:10.47626/2237-6089-2025-1082

Document this CPD

Share Evidence Blueprint

QR Code

Save to Google Notes

Search Google Scholar

Save as PDF

⭐ My Revision List

close chatgpt icon
ChatGPT

Enter your request.

← →
RAISR4D CME/CPD Evidence Nodes
Swipe to navigate RAISR4D CME/CPD evidence nodes.
CME/CPD