Evidence map›Paper›PMID 39814803›Full record

ArticleScientific reports2025

Challenges and insights of transferring animal maze studies principles to human spatial learning research.

Piruza Manukyan, Elizaveta Romanova, Alexander Latanov, Pavel Shlepnev, Anastasia Sharapkova, Naida Garabova, Vladimir Kasatkin, Vera Tolchennikova

Abstract read
In one paragraph

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

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0citing papers in PubMed
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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

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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

8 authors.

Piruza ManukyanResearch Institute for Brain Development and Peak Performance, RUDN University, Moscow, Russia.
Elizaveta RomanovaResearch Institute for Brain Development and Peak Performance, RUDN University, Moscow, Russia. romanova_enk@mail.ru.
Alexander LatanovResearch Institute for Brain Development and Peak Performance, RUDN University, Moscow, Russia.
Pavel ShlepnevResearch Institute for Brain Development and Peak Performance, RUDN University, Moscow, Russia.
Anastasia SharapkovaResearch Institute for Brain Development and Peak Performance, RUDN University, Moscow, Russia.
Naida GarabovaMedical Institute, RUDN University, Moscow, Russia.
Vladimir KasatkinCognitive Development and Neurorehabilitation Foundation, Moscow, Russia.
Vera TolchennikovaResearch Institute for Brain Development and Peak Performance, RUDN University, Moscow, Russia.

Funding

RUDN University Scientific Projects Grant System 212200-2-000
6 · The paper itself

Abstract

Maze tasks, originally developed in animal research, have become a popular method for studying human cognition, particularly with the advent of virtual reality. However, these experiments frequently rely on simplified environments and tasks, which may not accurately reflect the complexity of real-world situations. Our pilot study aims to transfer a multi-alternative maze with a complex task structure, previously demonstrated to be useful in studying animal cognition, to studying human spatial cognition. The challenges to be resolved at this stage included developing a virtual maze and selecting an appropriate instruction that will elicit processes similar to those observed in animal models. A virtual maze was developed, and two types of instructions were provided to the participants: (1) to collect coins; (2) to interact with the maze in order to draw its structure after the game. The results indicate that a more structured instruction with a clear attainable goal ("collect") prompted more in-depth exploration and engagement with the key elements of the maze, eliciting processes similar to those of animals. While the maze demonstrates promise as a tool for comparative studies, it also has the potential to uncover different aspects of human cognition.

Indexed as

Maze LearningSpatial LearningAdultAnimalsCognitionFemaleHumansMalePilot ProjectsVirtual RealityYoung AdultAnimal researchSpatial cognitionSpatial navigationVirtual maze

Identifiers

PMID39814803
PMCPMC11736021

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.