Word skipping: Effects of word length, predictability, spelling and reading skill

Readers’ eyes often skip over words as they read. Skipping rates are largely determined by word length; short words are skipped more than long words. However, the predictability of a word in context also impacts skipping rates. Rayner, Slattery, Drieghe and Liversedge reported an effect of predictability on word skipping for even long words (10-13 characters) that extend beyond the word identification span. Recent research suggests that better readers and spellers have an enhanced perceptual span. We explored that whether reading and spelling skill interact with word length and predictability to impact word skipping rates in a large sample ( N = 92) of average and poor adult readers. Participants read the items from Rayner et al., while their eye movements were recorded. Spelling skill (zSpell) was assessed using the dictation and recognition tasks developed by Sally Andrews and colleagues. Reading skill (zRead) was assessed from reading speed (words per minute) and comprehension accuracy of three 120 word passages each with 10 comprehension questions. We fit linear mixed models to the target gaze duration data and generalized linear mixed models to the target word skipping data. Target word gaze durations were significantly predicted by zRead, while the skipping likelihoods were significantly predicted by zSpell. Additionally, for gaze durations, zRead significantly interacted with word predictability as better readers relied less on context to support word processing. These effects are discussed in relation to the lexical quality hypothesis and eye movement models of reading.

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Word skipping during the lexical acquisition process

Quarterly Journal of Experimental Psychology ◽

10.1177/1747021820967292 ◽

2020 ◽

pp. 174702182096729

Author(s):

Michael A Eskenazi ◽

Paige Kemp ◽

Jocelyn R Folk

Keyword(s):

Eye Movements ◽

Word Length ◽

Mixed Effects ◽

Skill Assessment ◽

Mixed Effects Models ◽

Lexical Acquisition ◽

Spelling Skill ◽

Word Skipping ◽

Semantic Acquisition ◽

Novel Words

During reading, most words are identified in the fovea through a direct fixation; however, readers also identify some words in the parafovea without directly fixating them. This word skipping process is influenced by many lexical and visual factors including word length, launch position, frequency, and predictability. Although these factors are well understood, there is some disagreement about the process that leads to word skipping and the degree to which skipped words are processed. The purpose of this study was to investigate the word skipping process when readers are exposed to novel words in an incidental lexical acquisition paradigm. Participants read 18 three-letter novel words (i.e., pru, cho) in three different informative contexts each while their eye movements were monitored. They then completed a surprise test of their orthographic and semantic acquisition and a spelling skill assessment. Mixed-effects models indicated that participants learned spellings and meanings of words at the same rate regardless of the number of times that they were skipped. However, word skipping rates increased across the three exposures and reading times decreased. Results indicate that readers appear to process skipped words to the same degree as fixated words. However, this may be due to a more cautious skipping process used during lexical acquisition of unfamiliar words compared to processing of already known words.

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Heat, Hills and the High Season: A Model-Based Comparative Analysis of Spatio-Temporal Factors Affecting Shared Bicycle Use in Three Southern European Islands

Sustainability ◽

10.3390/su13063274 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3274

Author(s):

Suzanne Maas ◽

Paraskevas Nikolaou ◽

Maria Attard ◽

Loukas Dimitriou

Keyword(s):

Correlation Analysis ◽

Mixed Models ◽

Linear Mixed Models ◽

Positive Association ◽

Sustainable Mobility ◽

Weather Factors ◽

Gran Canaria ◽

Bivariate Correlation ◽

Temporal Factors

Bicycle sharing systems (BSSs) have been implemented in cities worldwide in an attempt to promote cycling. Despite exhibiting characteristics considered to be barriers to cycling, such as hot summers, hilliness and car-oriented infrastructure, Southern European island cities and tourist destinations Limassol (Cyprus), Las Palmas de Gran Canaria (Canary Islands, Spain) and the Valletta conurbation (Malta) are all experiencing the implementation of BSSs and policies to promote cycling. In this study, a year of trip data and secondary datasets are used to analyze dock-based BSS usage in the three case-study cities. How land use, socio-economic, network and temporal factors influence BSS use at station locations, both as an origin and as a destination, was examined using bivariate correlation analysis and through the development of linear mixed models for each case study. Bivariate correlations showed significant positive associations with the number of cafes and restaurants, vicinity to the beach or promenade and the percentage of foreign population at the BSS station locations in all cities. A positive relation with cycling infrastructure was evident in Limassol and Las Palmas de Gran Canaria, but not in Malta, as no cycling infrastructure is present in the island’s conurbation, where the BSS is primarily operational. Elevation had a negative association with BSS use in all three cities. In Limassol and Malta, where seasonality in weather patterns is strongest, a negative effect of rainfall and a positive effect of higher temperature were observed. Although there was a positive association between BSS use and the number of visiting tourists in Limassol and Malta, this is predominantly explained through the multi-collinearity with weather factors rather than by intensive use of the BSS by tourists. The linear mixed models showed more fine-grained results and explained differences in BSS use at stations, including differences for station use as an origin and as a destination. The insights from the correlation analysis and linear mixed models can be used to inform policies promoting cycling and BSS use and support sustainable mobility policies in the case-study cities and cities with similar characteristics.

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Confidence, prediction, and tolerance in linear mixed models

Statistics in Medicine ◽

10.1002/sim.8386 ◽

2019 ◽

Vol 38 (30) ◽

pp. 5603-5622 ◽

Cited By ~ 3

Author(s):

Bernard G. Francq ◽

Dan Lin ◽

Walter Hoyer

Keyword(s):

Mixed Models ◽

Linear Mixed Models

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Power for balanced linear mixed models with complex missing data processes

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2021.1909732 ◽

2021 ◽

pp. 1-19

Author(s):

Kevin P. Josey ◽

Brandy M. Ringham ◽

Anna E. Barón ◽

Margaret Schenkman ◽

Katherine A. Sauder ◽

...

Keyword(s):

Missing Data ◽

Mixed Models ◽

Linear Mixed Models

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l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

Statistical Methods in Medical Research ◽

10.1177/09622802211017583 ◽

2021 ◽

pp. 096228022110175

Author(s):

Jan P Burgard ◽

Joscha Krause ◽

Ralf Münnich ◽

Domingo Morales

Keyword(s):

Parameter Estimation ◽

Medical Treatment ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Obesity Prevalence ◽

Model Parameter ◽

Model Parameter Estimation ◽

Public Health Reporting ◽

Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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EXPRESS: Improving reading rate prediction with word length information: Evidence from Dutch

Quarterly Journal of Experimental Psychology ◽

10.1177/17470218211017100 ◽

2021 ◽

pp. 174702182110171

Author(s):

Marc Brysbaert ◽

Longjiao Sui ◽

Wouter Duyck ◽

Nicolas Dirix

Keyword(s):

Word Length ◽

Word Processing ◽

Reading Rate ◽

English Word ◽

Length Effect ◽

Non Fiction ◽

Word Length Effect ◽

Best Fitting ◽

Dutch Language ◽

Processing Effort

Previous research in English has suggested that reading rate predictions can be improved considerably by taking average word length into account. In the present study, we investigated whether the same regularity holds for Dutch. The Dutch language is very similar to English, but words are on average half a letter longer: 5.1 letters per word (in non-fiction) instead of 4.6. We collected reading rates of 62 participants reading 12 texts with varying word lengths, and examined which change in the English equation accounts for the Dutch findings. We observed that predictions were close to the best fitting curve as soon as the average English word length was replaced by the average Dutch word length. The equation predicts that Dutch texts with an average word length of 5.1 letters will be read at a rate of 238 word per minute (wpm). Texts with an average word length of 4.5 letter will be read at 270 wpm, and texts with an average word length of 6.0 letters will be read at a rate of 202 wpm. The findings are in line with the assumption that the longer words in Dutch do not slow down silent reading relative to English and that the word length effect observed in each language is due to word processing effort and not to low-level, visual factors.

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