Evidence map›Paper›PMID 30346410›Full record

ArticleJournal of visualized experiments : JoVE2018

Visual Classical Conditioning in Wood Ants.

A Sofia D Fernandes, C L Buckley, J E Niven

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Colour vision in ants (Formicidae, Hymenoptera).Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Review
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

3 authors.

A Sofia D FernandesDepartment of Informatics, University of Sussex; School of Life Sciences, University of Sussex; Centre for Computational Neuroscience and Robotics, University of Sussex; A.S.David-Fernandes@sussex.ac.uk.
C L BuckleyDepartment of Informatics, University of Sussex; Centre for Computational Neuroscience and Robotics, University of Sussex.
J E NivenSchool of Life Sciences, University of Sussex; Centre for Computational Neuroscience and Robotics, University of Sussex.

Funding

Biotechnology and Biological Sciences Research Council BB/R005036/1
6 · The paper itself

Abstract

Several species of insects have become model systems for studying learning and memory formation. Although many studies focus on freely moving animals, studies implementing classical conditioning paradigms with harnessed insects have been important for investigating the exact cues that individuals learn and the neural mechanisms underlying learning and memory formation. Here we present a protocol for evoking visual associative learning in wood ants through classical conditioning. In this paradigm, ants are harnessed and presented with a visual cue (a blue cardboard), the conditional stimulus (CS), paired with an appetitive sugar reward, the unconditional stimulus (US). Ants perform a Maxilla-Labium Extension Reflex (MaLER), the unconditional response (UR), which can be used as a readout for learning. Training consists of 10 trials, separated by a 5-minute intertrial interval (ITI). Ants are also tested for memory retention 10 minutes or 1 hour after training. This protocol has the potential to allow researchers to analyze, in a precise and controlled manner, the details of visual memory formation and the neural basis of learning and memory formation in wood ants.

Indexed as

AnimalsAntsConditioning, ClassicalCuesMemoryModels, BiologicalReflexRewardTime FactorsVisual Perception

Identifiers

PMID30346410
PMCPMC6235424

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.