Evidence map›Paper›PMID 26858510›Full record

ArticleJournal of memory and language2016

Observational Word Learning: Beyond Propose-But-Verify and Associative Bean Counting.

Tanja Roembke, Bob McMurray

Abstract read
In one paragraph

Article in Journal of memory and language, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Trial
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  8. Review
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  13. Cross-situational statistical learning in younger and older adults.Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition · 2021
    Article
  14. Article
  15. Article
  16. Article
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  18. Word learning mechanisms.Wiley interdisciplinary reviews. Cognitive science · 2017
    Review
  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

2 authors.

Tanja RoembkeDept. of Psychological and Brain Sciences University of Iowa.
Bob McMurrayDept. of Psychological and Brain Sciences Dept. of Communication Sciences and Disorders Dept. of Linguistics University of Iowa.

Funding

Speech Production &LanguageP50DC000242 · NIDCD · UNIVERSITY OF IOWA · PI Bruce Jay Gantz · 1989 to 2026
$58.8M
The Development of Real Time Spoken and Written Word Recognition: Cognitive Bases of Language and Educational OutcomesR01DC008089 · NIDCD · UNIVERSITY OF IOWA · PI MCMURRAY, BOB · 2007 to 2023
$7.3M
NIDCD NIH HHS P50 DC000242NIDCD NIH HHS R01 DC008089
6 · The paper itself

Abstract

Learning new words is difficult. In any naming situation, there are multiple possible interpretations of a novel word. Recent approaches suggest that learners may solve this problem by tracking co-occurrence statistics between words and referents across multiple naming situations (e.g. Yu & Smith, 2007), overcoming the ambiguity in any one situation. Yet, there remains debate around the underlying mechanisms. We conducted two experiments in which learners acquired eight word-object mappings using cross-situational statistics while eye-movements were tracked. These addressed four unresolved questions regarding the learning mechanism. First, eye-movements during learning showed evidence that listeners maintain multiple hypotheses for a given word and bring them all to bear in the moment of naming. Second, trial-by-trial analyses of accuracy suggested that listeners accumulate continuous statistics about word/object mappings, over and above prior hypotheses they have about a word. Third, consistent, probabilistic context can impede learning, as false associations between words and highly co-occurring referents are formed. Finally, a number of factors not previously considered in prior analysis impact observational word learning: knowledge of the foils, spatial consistency of the target object, and the number of trials between presentations of the same word. This evidence suggests that observational word learning may derive from a combination of gradual statistical or associative learning mechanisms and more rapid real-time processes such as competition, mutual exclusivity and even inference or hypothesis testing.

Indexed as

associative learningcross-situational learningeye movementsObservational learningstatistical learningword learning

Identifiers

PMID26858510
PMCPMC4742346

What OpenQuestion holds

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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.