ArticlePsychometrika2020
Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model.
Article in Psychometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Reduced on-line speech gesture integration during multimodal language processing in adults with moderate-severe traumatic brain injury: Evidence from eye-tracking.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM.Psychometrika · 2024Article
- Modeling Eye Movements During Decision Making: A Review.Psychometrika · 2023Review
- Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data.Psychometrika · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
This paper presents a dynamic tree-based item response (IRTree) model as a novel extension of the autoregressive generalized linear mixed effect model (dynamic GLMM). We illustrate the unique utility of the dynamic IRTree model in its capability of modeling differentiated processes indicated by intensive polytomous time-series eye-tracking data. The dynamic IRTree was inspired by but is distinct from the dynamic GLMM which was previously presented by Cho, Brown-Schmidt, and Lee (Psychometrika 83(3):751-771, 2018). Unlike the dynamic IRTree, the dynamic GLMM is suitable for modeling intensive binary time-series eye-tracking data to identify visual attention to a single interest area over all other possible fixation locations. The dynamic IRTree model is a general modeling framework which can be used to model change processes (trend and autocorrelation) and which allows for decomposing data into various sources of heterogeneity. The dynamic IRTree model was illustrated using an experimental study that employed the visual-world eye-tracking technique. The results of a simulation study showed that parameter recovery of the model was satisfactory and that ignoring trend and autoregressive effects resulted in biased estimates of experimental condition effects in the same conditions found in the empirical study.
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Registered trials
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.