Evidence map›Paper›PMID 31485084›Full record

ArticleLanguage learning2018

Response Errors in Females' and Males' Sentence Lipreading Necessitate Structurally Different Models for Predicting Lipreading Accuracy.

Lynne E Bernstein

Abstract read
In one paragraph

Article in Language learning, 2018. 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
0.4field-weighted citation impact, top 32% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Visualization of Speech Perception AnalysisFrontiers in neurology · 2021
    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

1 author at 1 institution in 1 country.

Lynne E BernsteinDepartment of Speech, Language, and Hearing Science, George Washington University, 2121 I St NW, Washington, DC 20052.
George Washington University · US

Funding

Research_ProjectU54HD090257 · NICHD · CHILDREN'S RESEARCH INSTITUTE · PI VITTORIO GALLO, VITTORIO GALLO VITTORIO GALLO · 2016 to 2020
$5.9M
Visual Form-Based Spoken Word ProcessingR21DC012634 · NIDCD · GEORGE WASHINGTON UNIVERSITY · PI AUER, EDWARD T, BERNSTEIN, LYNNE ESTHER · 2012 to 2013
$427k
Multisensory Training for Unisensory Perceptual LearningR21DC014523 · NIDCD · GEORGE WASHINGTON UNIVERSITY · PI AUER, EDWARD T, BERNSTEIN, LYNNE ESTHER · 2014 to 2014
$250k
NICHD NIH HHS U54 HD090257NIDCD NIH HHS R21 DC012634NIDCD NIH HHS R21 DC014523
6 · The paper itself

Abstract

Lipreaders recognize words with phonetically impoverished stimuli, an ability that is generally poor in normal-hearing adults. Individual sentence lipreading trials from 341 young adults were modeled to predict words and phonemes correct in terms of measures of phoneme response dissimilarity (PRD), number of inserted incorrect response phonemes, lipreader gender, and a measure of speech perception in noise. Interactions with lipreaders' gender necessitated structurally different models of males' and females' lipreading. Overall, female lipreaders are more accurate, their ability to recognize words with impoverished or degraded input is consistent across visual and auditory modalities, and they amplify their correct responding through top-down insertion of text. Males' responses suggest that individuals with poorer auditory speech perception in noise amplify their responses by shifting towards including text in their response that is more perceptually discrepant from the stimulus. Attention to gender differences merits attention in future studies that use visual speech stimuli.

Identifiers

PMID31485084
PMCPMC6724546
OpenAlexW2790459563

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.