About
About
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Drew J. McLaughlin is a Postdoctoral Researcher at the Basque Center on Cognition, Brain and Language. She completed her Ph.D. in the Department of Psychological & Brain Sciences at Washington University in St. Louis.
The majority of her research examines the cognitive basis of accent processing. For her dissertation, she investigated the implicit effect of social information on the perception of first and second language ("foreign") accented speech.
At the Basque Center on Cognition, Brain and Language (BCBL), Drew is currently investigating the cognitive mechanisms that support rapid accommodation of accent variation in multilingual listeners. To this aim, she is integrating evidence from pupil dynamics, gaze behavior, and electroencephalography (EEG).
The majority of her research examines the cognitive basis of accent processing. For her dissertation, she investigated the implicit effect of social information on the perception of first and second language ("foreign") accented speech.
At the Basque Center on Cognition, Brain and Language (BCBL), Drew is currently investigating the cognitive mechanisms that support rapid accommodation of accent variation in multilingual listeners. To this aim, she is integrating evidence from pupil dynamics, gaze behavior, and electroencephalography (EEG).
Advancing Pupillometry Research Methods
Advancing Pupillometry Research Methods
Drew is an expert in cognitive pupillometry, the measure of the task-evoked pupil response for experimental research. She has extensive experience using pupillometry for empirical research and has also conducted methodological investigations to determine best practices for pupillometry (see Figure below). Drew maintains a research line committed to the advancement of pupillometry (and, more broadly, quantitative) research methods.
Drew is an expert in cognitive pupillometry, the measure of the task-evoked pupil response for experimental research. She has extensive experience using pupillometry for empirical research and has also conducted methodological investigations to determine best practices for pupillometry (see Figure below). Drew maintains a research line committed to the advancement of pupillometry (and, more broadly, quantitative) research methods.
Walkthrough and materials for creating EyeLink-compatible experiments with PsychoPy and preparing EyeLink EDF/ASC files for analyses are available in the Pupillometry tab. Analysis scripts from prior work can be found on Open Science Framework.
Walkthrough and materials for creating EyeLink-compatible experiments with PsychoPy and preparing EyeLink EDF/ASC files for analyses are available in the Pupillometry tab. Analysis scripts from prior work can be found on Open Science Framework.
Figure. Visualization of the importance of modeling trial (i.e., time across the experiment) is shown in McLaughlin et al. (2023). Here, raw data (gray dots) and predicted GCA fit lines are summarized by quartile (i.e., collapsing trials 1–20, 21–40, 41–60, and 61–80). Quartiles of trials are for visualization only and were not used for modeling. Size of the pupil is shown on the y-axis, and time within the trial (where zero is the start of the stimulus) is shown on the x-axis.
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