Evidence map›Paper›PMID 40097898›Full record

ArticlePsychonomic bulletin & review2025

High variability orthographic training: Learning words in a logographic script through training with multiple typefaces.

Eric Pelzl

Abstract read
In one paragraph

Article in Psychonomic bulletin & review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Eric PelzlThe Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. eric.pelzl@polyu.edu.hk.ORCID http://orcid.org/0000-0001-8335-8814

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We tested whether naturally occurring visual variability-specifically, typefaces-would help people generalize word learning to typefaces they had never seen before. In Chinese, thousands of unique written characters must be learned item by item, and differentiated from similar-looking characters. Participants (n = 190) with no previous Chinese experience learned 24 Chinese characters in one of two training groups: the Single-Typeface group trained using only one of three Chinese typefaces; the Variable-Typeface group trained using all three. Everyone completed two training and testing phases. During Definition Training, they saw each character six times and learned to associate it with an English definition (-water). After training, participants were tested on their accuracy in providing definitions for the characters. During Form Training, participants chose the characters they had previously learned from a display that included a trained character and a visually similar distractor ( vs ). After training, they were tested on their speed/accuracy in choosing the learned characters. At testing in both phases, half of the words were presented in a familiar typeface; half in a novel typeface. Results showed significant interactions between training and testing conditions in both phases, with a significant effect of training in the Form Testing phase: Single-Typeface training resulted in faster responses for familiar typefaces, but much slower responses for novel typefaces; in comparison, Variable-Typeface training resulted in better generalization to novel typefaces. These results suggest that typeface variability can influence how effectively people generalize knowledge during the initial stages of learning a logographic script.

Indexed as

LearningPattern Recognition, VisualReadingAdultFemaleHumansMaleYoung AdultDesirable difficultyHigh variability trainingOrthographyVocabulary learning

Identifiers

PMID40097898
PMCPMC12426071

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.