Evidence map›Paper›PMID 35122322›Full record

ArticleCognitive science2022

When the "Tabula" is Anything but "Rasa:" What Determines Performance in the Auditory Statistical Learning Task?

Amit Elazar, Raquel G Alhama, Louisa Bogaerts, Noam Siegelman, Cristina Baus, Ram Frost

Open access · hybridAbstract read
In one paragraph

Article in Cognitive science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.0field-weighted citation impact, top 5% 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

9 citing papers in PubMed, 28 citations in OpenAlex.

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  7. Statistical learning at a virtual cocktail party.Psychonomic bulletin & review · 2024
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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

6 authors at 4 institutions in 4 countries.

Amit ElazarDepartment of Psychology, The Hebrew University of Jerusalem.
Raquel G AlhamaDepartment of Cognitive Science & Artificial Intelligence, Tilburg University.
Louisa BogaertsDepartment of Experimental Psychology, Ghent University.
Noam SiegelmanHaskins Laboratories, New Haven, CT.
Cristina BausDepartment of Cognition, Development and Educational Psychology, University of Barcelona.
Ram FrostDepartment of Psychology, The Hebrew University of Jerusalem.
Haskins Laboratories · USGhent University · BEHebrew University of Jerusalem · ILPompeu Fabra University · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

How does prior linguistic knowledge modulate learning in verbal auditory statistical learning (SL) tasks? Here, we address this question by assessing to what extent the frequency of syllabic co-occurrences in the learners' native language determines SL performance. We computed the frequency of co-occurrences of syllables in spoken Spanish through a transliterated corpus, and used this measure to construct two artificial familiarization streams. One stream was constructed by embedding pseudowords with high co-occurrence frequency in Spanish ("Spanish-like" condition), the other by embedding pseudowords with low co-occurrence frequency ("Spanish-unlike" condition). Native Spanish-speaking participants listened to one of the two streams, and were tested in an old/new identification task to examine their ability to discriminate the embedded pseudowords from foils. Our results show that performance in the verbal auditory SL (ASL) task was significantly influenced by the frequency of syllabic co-occurrences in Spanish: When the embedded pseudowords were more "Spanish-like," participants were better able to identify them as part of the stream. These findings demonstrate that learners' task performance in verbal ASL tasks changes as a function of the artificial language's similarity to their native language, and highlight how linguistic prior knowledge biases the learning of regularities.

Indexed as

LearningSpeech PerceptionAuditory PerceptionHumansLanguageLinguisticsVerbal LearningPrior knowledgeSpeech segmentationStatistical learningSyllable frequency

Identifiers

PMID35122322
PMCPMC9285054
OpenAlexW4210788110

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.