Evidence map›Paper›PMID 35879365›Full record

ArticleScientific reports2022

Strategies discovery in the active allothetic place avoidance task.

Avgoustinos Vouros, Tiago V Gehring, Bartosz Jura, Małgorzata J Węsierska, Daniel K Wójcik, Eleni Vasilaki

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 45% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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 at 5 institutions in 3 countries.

Avgoustinos Vouros *Department of Computer Science, The University of Sheffield, Sheffield, S1 4DP, UK.
Tiago V Gehring *Department of Computer Science, The University of Sheffield, Sheffield, S1 4DP, UK.
Bartosz JuraFaculty of Management and Social Communication, Jagiellonian University, 30-348, Cracow, Poland.
Małgorzata J WęsierskaNencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, 02-093, Poland.
Daniel K WójcikFaculty of Management and Social Communication, Jagiellonian University, 30-348, Cracow, Poland. d.wojcik@nencki.edu.pl.
Eleni VasilakiDepartment of Computer Science, The University of Sheffield, Sheffield, S1 4DP, UK. e.vasilaki@sheffield.ac.uk.
University of Sheffield · GBETH Zurich · CHInstytut Biologii Doświadczalnej im. Marcelego Nenckiego · PLJagiellonian University · PLPolish Academy of Sciences · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Active Allothetic Place Avoidance task is an alternative setup to Morris Water Maze that allows studying spatial memory in a dynamic world in the presence of conflicting information. In this task, a rat, freely moving on a rotating circular arena, has to avoid a sector defined within the room frame where shocks are presented. While for Morris Water Maze several studies have identified animal strategies which specifically affect performance, there were no such studies for the Active Allothetic Place Avoidance task. Using standard machine learning methods, we were able to reveal for the first time, to the best of our knowledge, explainable strategies that the animals employ in this task and demonstrate that they can provide a high-level interpretation for performance differences between an animal group treated with silver nanoparticles (AgNPs) and the control group.

Indexed as

Avoidance LearningMetal NanoparticlesAnimalsMaze LearningRatsRats, Long-EvansSilverSpatial MemorySilver

Identifiers

PMID35879365
PMCPMC9314408
OpenAlexW4287578808

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.