Analyzing ‘visual world’ eyetracking data using multilevel logistic regression

By Dale J. Barr. 2008

665 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/de9350607d3c8ea4c5a2bcdf154625843e3c3f58

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In Developmental and Educational Psychology

In Experimental and Cognitive Psychology

In Human-Computer Interaction

Citations in the corpus

The role of fillers in listener attributions for speaker disfluency (2010, background): “However, such analyses can be misleading about the time course of effects, because they confound effects that emerge during the time window with ‘anticipation’ effects that may have emerged earlier and that persist over the time window (Barr, 2008a, 2008b).”
Random effects structure for confirmatory hypothesis testing: Keep it maximal (2013, neutral): “Furthermore, none of the LMEM articles in the JML special issue (Baayen et al., 2008; Barr, 2008; Dixon, 2008; Jaeger, 2008; Mirman, Dixon, & Magnuson, 2008; Quené & van den Bergh, 2008) set a”
Clearing the garden-path: improving sentence processing through cognitive control training (2014, methodology): “All accuracy data were first transformed using an empirical (e)logit function to correct potential problems related to heterogeneity of variance (see equation 5 in the study of Barr, 2008).”
A cognitive load delays predictive eye movements similarly during L1 and L2 comprehension (2017, methodology): “We then transformed fixation probability in every time bin into log odds, using the empirical logit function (Barr, 2008).”
Investigating the time-course of phonological prediction in native and non-native speakers of English: A visual world eye-tracking study (2018, methodology): “The model included random intercepts by participants and by items (Barr, 2008).”
The role of contrastive intonation contours in the retrieval of contextual alternatives (2010, methodology): “This analysis has been chosen over the more traditional separate subject and items analyses as it is less dependent on normality and sphericity assumptions, can cope well with missing data, and allows for the combined analysis of categorical and continuous predictors (e.g., Baayen, 2008; Barr, 2008;”
Growth Curve Analysis and Visualization Using R (2014, methodology): “fixation time course data, one treated participants as random effects (Barr, 2008) and the other treated them as fixed effects (Mirman, Dixon, & Magnuson, 2008).”
Perceiving and remembering events cross-linguistically: Evidence from dual-task paradigms (2010, methodology): “We tested the reliability of the patterns in Figure 4 by using multi-level mixed modeling over time, following Barr (2008). Our approach diverges from Barr‟s in that we used crossed random effects modeling of Subject and Items on non-averaged data rather than modeling Subject and Item means separate”
Cross-linguistic differences in case marking shape neural power dynamics and gaze behavior during sentence planning. (2022, methodology): “For each trial, consecutive fixations within areas of interest were subsumed into gazes (Griffin & Davison, 2011) and then aggregated into 100 ms time bins to reduce temporal autocorrelation (Barr, 2008; Cho, Brown-Schmidt, & Lee, 2018).”