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Decision-making without a brain: how an amoeboid organism solves the two-armed bandit

Overview of attention for article published in Journal of The Royal Society Interface, June 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#23 of 2,162)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
6 news outlets
blogs
2 blogs
twitter
259 tweeters
facebook
9 Facebook pages
googleplus
6 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
141 Mendeley
citeulike
1 CiteULike
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Title
Decision-making without a brain: how an amoeboid organism solves the two-armed bandit
Published in
Journal of The Royal Society Interface, June 2016
DOI 10.1098/rsif.2016.0030
Pubmed ID
Authors

Chris R. Reid, Hannelore MacDonald, Richard P. Mann, James A. R. Marshall, Tanya Latty, Simon Garnier

Abstract

Several recent studies hint at shared patterns in decision-making between taxonomically distant organisms, yet few studies demonstrate and dissect mechanisms of decision-making in simpler organisms. We examine decision-making in the unicellular slime mould Physarum polycephalum using a classical decision problem adapted from human and animal decision-making studies: the two-armed bandit problem. This problem has previously only been used to study organisms with brains, yet here we demonstrate that a brainless unicellular organism compares the relative qualities of multiple options, integrates over repeated samplings to perform well in random environments, and combines information on reward frequency and magnitude in order to make correct and adaptive decisions. We extend our inquiry by using Bayesian model selection to determine the most likely algorithm used by the cell when making decisions. We deduce that this algorithm centres around a tendency to exploit environments in proportion to their reward experienced through past sampling. The algorithm is intermediate in computational complexity between simple, reactionary heuristics and calculation-intensive optimal performance algorithms, yet it has very good relative performance. Our study provides insight into ancestral mechanisms of decision-making and suggests that fundamental principles of decision-making, information processing and even cognition are shared among diverse biological systems.

Twitter Demographics

The data shown below were collected from the profiles of 259 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Belgium 2 1%
Brazil 1 <1%
United Kingdom 1 <1%
France 1 <1%
Taiwan 1 <1%
Luxembourg 1 <1%
Russia 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 128 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 30%
Researcher 21 15%
Student > Bachelor 19 13%
Student > Master 14 10%
Unspecified 10 7%
Other 35 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 28%
Computer Science 20 14%
Unspecified 15 11%
Physics and Astronomy 13 9%
Psychology 13 9%
Other 40 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 242. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 August 2019.
All research outputs
#51,933
of 13,528,041 outputs
Outputs from Journal of The Royal Society Interface
#23
of 2,162 outputs
Outputs of similar age
#2,180
of 264,413 outputs
Outputs of similar age from Journal of The Royal Society Interface
#2
of 55 outputs
Altmetric has tracked 13,528,041 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,162 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.9. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,413 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.