Evidence map›Paper›PMID 27932852›Full record

ArticleEvolutionary biology2016

Evolutionary Connectionism: Algorithmic Principles Underlying the Evolution of Biological Organisation in Evo-Devo, Evo-Eco and Evolutionary Transitions.

Richard A Watson, Rob Mills, C L Buckley, Kostas Kouvaris, Adam Jackson, Simon T Powers, Chris Cox, Simon Tudge, Adam Davies, Loizos Kounios and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Evolutionary biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
23.6field-weighted citation impact, top 1% 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

30 citing papers in PubMed, 83 citations in OpenAlex.

  1. Heuristically Adaptive Diffusion-Model Evolutionary Strategy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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  3. Implications From the Analogous Relationship Between Evolutionary and Learning Processes.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Article
  12. Cells as the first data scientists.Journal of the Royal Society, Interface · 2023
    Article
  13. Article
  14. Article
  15. Article
  16. Tempos and modes of collectivity in the history of life.Theory in biosciences = Theorie in den Biowissenschaften · 2021
    Review
  17. Article
  18. Article
  19. Article
  20. 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

11 authors at 4 institutions in 3 countries.

Richard A WatsonAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK ; Institute for Life Sciences, University of Southampton, Southampton, UK.
Rob MillsBiosystems & Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Lisbon, Portugal.
C L BuckleySchool of Engineering and Informatics, University of Sussex, Falmer, UK.
Kostas KouvarisAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK.
Adam JacksonAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK.
Simon T PowersUniversity of Lausanne, Lausanne, Switzerland.
Chris CoxAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK.
Simon TudgeAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK.
Adam DaviesAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK.
Loizos KouniosAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK.
Daniel PowerAgents, Interactions and Complexity, ECS, University of Southampton, Southampton, UK.
University of Southampton · GBUniversity of Lausanne · CHUniversity of Lisbon · PTUniversity of Sussex · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term "evolutionary connectionism" to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions.

Indexed as

Evolutionary developmental biologyEvolutionary ecologyLearning theoryMajor transitions in evolution

Identifiers

PMID27932852
PMCPMC5119841
OpenAlexW2156183867

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.