Evidence map›Paper›PMID 42220926›Full record

ArticleBasic and clinical neuroscience2025

Development of an Automated Training Device for Macaque Monkeys.

Jalaledin Noroozi, Mahsa Ahmadi, Atlas Shahamati, Ehsan Rezayat, Mohammad-Reza A Dehaqani

Abstract read
In one paragraph

Article in Basic and clinical neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jalaledin NorooziDepartment of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.ORCID https://orcid.org/0000-0001-9547-8987
Mahsa AhmadiSchool of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.ORCID https://orcid.org/0009-0000-0454-8071
Atlas ShahamatiSchool of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.ORCID https://orcid.org/0009-0004-4984-1385
Ehsan RezayatDepartment of Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran.ORCID https://orcid.org/0000-0003-3808-1283
Mohammad-Reza A DehaqaniSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.ORCID https://orcid.org/0000-0003-4365-4365

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Training macaques for cognitive and behavioral tasks is often time-consuming, stressful, and requires extensive human involvement. Traditional chair-based methods may take weeks or months and can negatively affect animal welfare. To address these challenges, we developed an automated, cage-based training device. Methods: Two adult male rhesus macaques were trained using the device step-by-step on working memory, delayed match-to-sample, starting from simple screen touches and progressing to the full task. Results: The monkeys interacted well with the device, and the system reduced stress by eliminating the need for chair restraint. Training time was shortened, and both monkeys successfully learned the task within 30-33 days. Conclusion: This automated training device provides an efficient, cost-effective, and welfare-friendly alternative to conventional training methods for monkeys. It shortens training duration, improves task acquisition, and enhances enrichment, offering a promising tool for neuroscience research with non-human primates.

Indexed as

Macaque monkeyPrimate enrichmentWorking memory

Identifiers

PMID42220926
PMCPMC13220691

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.