pyarbus: a Python library for eye tracking data analysis

Ivanov, Paul

doi:10.1167/13.15.54

Cited by 18 publications

(22 citation statements)

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“…Only gaze points that fell into AOIs entered analyses. Data cleaning and analysis were carried out in the R package eyetrackingR (Dink & Ferguson, 2015 ). Trials in which the eye tracker lost the eyes for more than 50% of the trial duration were excluded from analyses.…”

Section: Methodsmentioning

confidence: 99%

The impact of perceived emotions on toddlers' word learning

Twomey

Westermann

2022

Child Development

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Others' emotional expressions affect individuals' attention allocation in social interactions, which are integral to the process of word learning. However, the impact of perceived emotions on word learning is not well understood. Two eye‐tracking experiments investigated 78 British toddlers' (37 girls) of 29‐ to 31‐month‐old retention of novel label‐object and emotion‐object associations after hearing labels presented in neutral, positive, and negative affect in a referent selection task. Overall, toddlers learned novel label‐object associations regardless of the affect associated with objects but showed an attentional bias toward negative objects especially when emotional cues were presented (d = 0.95), suggesting that identifying the referent to a label is a competitive process between retrieval of the learned label‐object association and the emotional valence of distractors.

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Section: Methodsmentioning

confidence: 99%

The impact of perceived emotions on toddlers' word learning

Twomey

Westermann

2022

Child Development

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“…We used the packages lme4 (Bates et al, 2015) and eyetrackingR (Dink and Fergusson, 2015) from the free software R (R Core Team, 2020) for data analysis. For each sample of the eye-tracking data, it was computed whether the participant looked at the target picture or the distractor picture, which depended on the word that was played at that moment.…”

Section: Analysismentioning

confidence: 99%

Implicit cross-situational word learning in children with and without developmental language disorder and its relation to lexical-semantic knowledge

Broedelet

Boersma²,

Rispens³

2023

Front. Commun.

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IntroductionResearch indicates that statistical learning plays a role in word learning by enabling the learner to track the co-occurrences between words and their visual referents, a process that is named cross-situational word learning. Word learning is problematic for children with developmental language disorder (DLD), and a deficit in statistical learning has been suggested to contribute to the language difficulties in these children. Therefore, we investigate whether children with DLD have more difficulty than TD children with learning novel word–referent pairs based on cross-situational statistics in an implicit task, and whether this ability is related to their lexical-semantic skills. Moreover, we look at the role of variability of the learning environment.MethodsIn our implicit cross-situational word learning task, each trial in the exposure phase was in itself ambiguous: two pictures of unknown objects were shown at the same time and two novel words were played consecutively, without indicating which word referred to which object. However, as every word occurred with its correct referent consistently, the children could learn the word–referent pairs across trials. The children were not explicitly instructed to learn the names of new objects. As an on-line measure of learning, eye-movements were recorded during the exposure phase. After exposure, word–referent knowledge was also tested using multiple choice questions. Several measures of lexical-semantic knowledge were administered to the children with DLD, as well as tasks measuring non-verbal intelligence and phonological processing. Contextual variability (the number of different distractors with which a particular word–referent pair occurs across trials) was manipulated between subjects by constructing two types of exposure conditions: low contextual diversity vs. high contextual diversity.ResultsBoth groups of children performed significantly above chance level on the test phase, but the TD children significantly outperformed the children with DLD. We found no significant effect of contextual diversity. The eye-tracking data revealed some evidence of on-line learning, but no differences between groups. Finally, the regression analyses did not reveal any significant predictors of off-line or on-line cross-situational word learning ability.DiscussionOur results indicate that although children with DLD are able to learn word-referent pairs in an implicit task, they have more difficulty than TD children. Possibly they need more input to achieve the same level.

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“…The raw eye movement data were pre-processed in R, using the eyetrackingR package (Dink and Ferguson, 2015). Initially, the raw data were processed into the format required for eyetrackingR using R. Thereafter, for each data point, reporting was provided for the PoF to the AOIs as opposed to the non-AOIs.…”

Section: The Time Course Analysis Of the Eye Movement Datamentioning

confidence: 99%

“…As a result, applying the trackloss_analysis() function in eyetrackingR, we calculated that 31.13% (SD = 0.09) of data points are the off-screen for the TD group and 30.35% (SD = 0.09) of those are for the ADHD group. Next, we down-sampled the data from 500 Hz to 50 Hz at the end of the pre-processing steps and transformed the PoF on the targets (i.e., AOIs), and this time used an empirical logit (Elog) transformation (Barr 2008) calculated through the eyetrackingR package (Dink and Ferguson, 2015).…”

Section: The Time Course Analysis Of the Eye Movement Datamentioning

confidence: 99%

Dynamic eye-tracking evaluation of responding joint attention abilities and face scanning patterns in children with attention deficit hyperactivity disorder

et al. 2023

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There has been growing evidence that autistic traits are more represented in children with attention-deficit hyperactivity disorder (ADHD). The purpose of this study was to investigate autistic traits associated with responding joint attention (RJA) abilities and face scanning patterns using eye-tracking in children with ADHD, and to compare with typically developing peers (TDs). All child participants viewed a series of videos related to male and female children under congruent and incongruent conditions during eye-tracking evaluation. The object and face regions of the models within the videos were determined as areas of interest (AOIs). Children with ADHD had significantly elevated ratings of autistic traits than TDs. Time course analysis of the proportion of fixations (PoF) on object region determined that children with ADHD tended to show more interest in the objects and had higher PoF on face interest area, including eyes and mouth compared to TD children in the videos when the male/female model shifts his/her gaze to the corner. Higher SRS scores were associated with higher PoF on the Face AOI in both groups. Given these findings, social skill interventions directly targeting the core deficits of RJA and problems in facial scanning appears to be beneficial in children with ADHD.

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pyarbus: a Python library for eye tracking data analysis

Cited by 18 publications

References 0 publications

The impact of perceived emotions on toddlers' word learning

The impact of perceived emotions on toddlers' word learning

Implicit cross-situational word learning in children with and without developmental language disorder and its relation to lexical-semantic knowledge

Dynamic eye-tracking evaluation of responding joint attention abilities and face scanning patterns in children with attention deficit hyperactivity disorder

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