Evidence map›Paper›PMID 34867600›Full record

ArticleFrontiers in psychology2021

Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices.

Maria Heitmeier, Yu-Ying Chuang, R Harald Baayen

Abstract read
In one paragraph

Article in Frontiers in psychology, 2021. 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
–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

6 citing papers in PubMed.

  1. Most German Speakers Ignore the Cue That Best Predicts Plural Class.Open mind : discoveries in cognitive science · 2025
    Article
  2. Article
  3. Article
  4. Frequency effects in linear discriminative learning.Frontiers in human neuroscience · 2023
    Article
  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.

Maria HeitmeierDepartment of Linguistics, Eberhard-Karls Universität, Tübingen, Germany.
Yu-Ying ChuangDepartment of Linguistics, Eberhard-Karls Universität, Tübingen, Germany.
R Harald BaayenDepartment of Linguistics, Eberhard-Karls Universität, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study addresses a series of methodological questions that arise when modeling inflectional morphology with Linear Discriminative Learning. Taking the semi-productive German noun system as example, we illustrate how decisions made about the representation of form and meaning influence model performance. We clarify that for modeling frequency effects in learning, it is essential to make use of incremental learning rather than the end-state of learning. We also discuss how the model can be set up to approximate the learning of inflected words in context. In addition, we illustrate how in this approach the wug task can be modeled. The model provides an excellent memory for known words, but appropriately shows more limited performance for unseen data, in line with the semi-productivity of German noun inflection and generalization performance of native German speakers.

Indexed as

frequency of occurrenceGerman nounslinear discriminative learningmultivariate multiple regressionsemantic rolessemi-productivityWidrow-Hoff learningwug task

Identifiers

PMID34867600
PMCPMC8634146

What OpenQuestion holds

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Registered trials

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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.