Evidence map›Paper›PMID 31840588›Full record

ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2020

Modelling meaning composition from formalism to mechanism.

Andrea E Martin, Giosuè Baggio

Abstract read
In one paragraph

Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Compositional Processing Emerges in Neural Networks Solving Math Problems.CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference · 2021
    Article
  5. Theory Before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science.Perspectives on psychological science : a journal of the Association for Psychological Science · 2021
    Article
  6. Article
  7. Article
  8. 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

2 authors.

Andrea E MartinMax Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Giosuè BaggioLanguage Acquisition and Language Processing Lab, Department of Language and Literature, Norwegian University of Science and Technology, Trondheim, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human thought and language have extraordinary expressive power because meaningful parts can be assembled into more complex semantic structures. This partly underlies our ability to compose meanings into endlessly novel configurations, and sets us apart from other species and current computing devices. Crucially, human behaviour, including language use and linguistic data, indicates that composing parts into complex structures does not threaten the existence of constituent parts as independent units in the system: parts and wholes exist simultaneously yet independently from one another in the mind and brain. This independence is evident in human behaviour, but it seems at odds with what is known about the brain's exquisite sensitivity to statistical patterns: everyday language use is productive and expressive precisely because it can go beyond statistical regularities. Formal theories in philosophy and linguistics explain this fact by assuming that language and thought are

Indexed as

LanguageBrainCognitionEvoked PotentialsHumansLinguisticsNeurosciencesSemanticscognitioncompositionalitylanguagemechanistic modelsneurosciencesemantics

Identifiers

PMID31840588
PMCPMC6939358

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.