Evidence map›Paper›PMID 31663267›Full record

ReviewTopics in cognitive science2020

Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning.

Willem Zuidema, Robert M French, Raquel G Alhama, Kevin Ellis, Timothy J O'Donnell, Tim Sainburg, Timothy Q Gentner

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Topics in cognitive science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
16.3field-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

4 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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

7 authors at 5 institutions in 4 countries.

Willem ZuidemaInstitute for Logic, Language and Computation, University of Amsterdam.
Robert M FrenchLEAD-CNRS, University of Burgundy.
Raquel G AlhamaMax Planck Institute for Psycholinguistics.
Kevin EllisDepartment of Brain and Cognitive Sciences, MIT.
Timothy J O'DonnellDepartment of Linguistics, McGill University.
Tim SainburgDepartment of Psychology, University of California San Diego.
Timothy Q GentnerDepartment of Psychology & Division of Biological Sciences, University of California San Diego.
University of California, San Diego · USLaboratoire d'étude de l'apprentissage et du développement · FRMax Planck Institute for Psycholinguistics · NLMcGill University · CAUniversity of Amsterdam · NL

Funding

Temporal Pattern Perception Mechanisms for Acoustic CommunicationR01DC018055 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI TIMOTHY Q GENTNER · 2019 to 2026
$2.6M
Neural mechanisms of auditory temporal pattern perceptionR56DC016408 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GENTNER, TIMOTHY Q · 2017 to 2017
$364k
ESRC of the United KingdomFrench National Research Agency (ANR) ORA-10-056Netherlands Organization for Scientific Research 024.001.006NIDCD NIH HHS R01 DC018055NIDCD NIH HHS R56 DC016408NSF GRFP 2017216247
6 · The paper itself

Abstract

There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques-even simple ones that are straightforward to use-can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.

Indexed as

Cognitive ScienceModels, TheoreticalNeural Networks, ComputerPsycholinguisticsHumansArtificial grammar learningArtificial language learningBayesian modelingComputational modelingFormal grammarsNeural networks

Identifiers

PMID31663267
PMCPMC7496886
OpenAlexW2980055757

What OpenQuestion holds

Textmetadata
LicenceTDM
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.