Evidence map›Paper›PMID 32873200›Full record

ArticleProceedings. Biological sciences2020

Honeybees solve a multi-comparison ranking task by probability matching.

HaDi MaBouDi, James A R Marshall, Andrew B Barron

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings. Biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.8field-weighted citation impact, top 10% 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

6 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. A Markovian dynamics forProceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  4. Review
  5. Article
  6. 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

3 authors at 1 institution in 2 countries.

HaDi MaBouDiDepartment of Computer Science, University of Sheffield, Sheffield, UK.
James A R MarshallDepartment of Computer Science, University of Sheffield, Sheffield, UK.
Andrew B BarronDepartment of Computer Science, University of Sheffield, Sheffield, UK.
University of Sheffield · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Honeybees forage on diverse flowers which vary in the amount and type of rewards they offer, and bees are challenged with maximizing the resources they gather for their colony. That bees are effective foragers is clear, but how bees solve this type of complex multi-choice task is unknown. Here, we set bees a five-comparison choice task in which five colours differed in their probability of offering reward and punishment. The colours were ranked such that high ranked colours were more likely to offer reward, and the ranking was unambiguous. Bees' choices in unrewarded tests matched their individual experiences of reward and punishment of each colour, indicating bees solved this test not by comparing or ranking colours but by basing their colour choices on their history of reinforcement for each colour. Computational modelling suggests a structure like the honeybee mushroom body with reinforcement-related plasticity at both input and output can be sufficient for this cognitive strategy. We discuss how probability matching enables effective choices to be made without a need to compare any stimuli directly, and the use and limitations of this simple cognitive strategy for foraging animals.

Indexed as

AnimalsBeesBehavior, AnimalChoice BehaviorColorColor PerceptionComputer SimulationFlowerscolour learningecological rationalitymulti-armed bandit taskmushroom bodyprobability matchingreinforcement learning

Identifiers

PMID32873200
PMCPMC7542781
OpenAlexW3082748035

What OpenQuestion holds

Textmetadata
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.