Statistical and computational models of the visual world paradigm: Growth curves and individual differences

By Daniel Mirman, J. Dixon, James S. Magnuson. Journal of Memory and Language, 2008

500 citations recorded by the source corpus.

Time course estimates from eye tracking during spoken language processing (the “visual world paradigm”, or VWP) have enabled progress on debates regarding fine-grained details of activation and competition over time. There are, however, three gaps in current analyses of VWP data: consideration of time in a statistically rigorous manner, quantification of individual differences, and distinguishing linguistic effects from non-linguistic effects. To address these gaps, we have developed an approach combining statistical and computational modeling. The statistical approach (growth curve analysis, a technique explicitly designed to assess change over time at group and individual levels) provides a rigorous means of analyzing time course data. We introduce the method and its application to VWP data. We also demonstrate the potential for assessing whether differences in group or individual data are best explained by linguistic processing or decisional aspects of VWP tasks through comparison of growth curve analyses and computational modeling, and discuss the potential benefits for studying typical and atypical language processing.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/35cd567b6178cfb89b423701498ef1245e632129

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Citations in the corpus

Perceiving and remembering events cross-linguistically: Evidence from dual-task paradigms (2010, background): “3 See Figure 6 of Mirman et al. (2008) for a discussion of how best to interpret linear and quadratic components of time.”
Author's personal copy Theories of spoken word recognition deficits in Aphasia: Evidence from eye-tracking and computational modeling (2011, unknown): “The cubic and quartic terms tend to capture minor details in the asymptotic tails of the fixation proportion curves and are not informative for typical VWP experiments (see Mirman et al. (2008) for details).”
Random effects structure for confirmatory hypothesis testing: Keep it maximal (2013, background): “Furthermore, none of the LMEM articles in the JML special issue (Baayen et al., 2008; Barr, 2008; Dixon, 2008; Jaeger, 2008; Mirman et al., 2008; Quené & van den Bergh, 2008) set a bad example of using random-intercept-only models.”
Analyzing dynamic phonetic data using generalized additive mixed modeling: A tutorial focusing on articulatory differences between L1 and L2 speakers of English (2018, unknown): “Consequently, if the patterns in the data are linear, or can be adequately represented by simple polynomials, it might be preferable to use growth curve analysis (Mirman, 2014; Mirman et al., 2008) or linear mixed-effects regression modeling via the R lme4 package (see also Winter & Wieling, 2016 fo”
The roles of tonal and segmental information in Mandarin spoken word recognition: An eyetracking study (2010, methodology): “…in interpreting higher-order terms given that they may also reflect other sources of variation such as early-going differences in time course; see Mirman et al. (2008), for further discussion. applying the same growth curve analysis procedure as above to a subset of the data containing only…”
Neural signatures of syntactic variation in speech planning (2021, unknown): “Eye fixations to agent characters were analyzed on the single-trial level with logistic mixedeffects growth curve regression [52,111,112].”