Emily Mech uses event-related potentials (ERPs), computational modeling, and behavioral methodology to examine how regularities in language and the world interact to affect the ways in which our linguistic and semantic knowledge is learned, structured, accessed, and communicated.
Download my CV.
Download my resumé.
PhD in Psychology (Cognitive), Expected Graduation - 2023
University of Illinois at Urbana-Champaign
MA in Psychology (Cognitive), 2018
University of California, Riverside
BA in Psychology, 2016
University of Wisconsin-Madison
BA in Communication Sciences and Disorders, 2016
University of Wisconsin-Madison
I use R to build data visualization and analytic pipelines for complex data collected from behavioral as well as neuroimaging (EEG) experiments. I regularly use packages such as tidyverse, lmer, and brms to plot data trends and implement statistical models.
I primarily use python to extract language statistics from corpora and manipulate data into different formats. Additionally, I have implemented computational models such decision trees, n-gram models, and SRNs. I’m currently developing a computational model of language comprehension in the cerebral hemispheres for my dissertation.
I primarily analyze my data with hierarchical linear models. I have implemented these models with both frequentist and bayesian estimation methods. I am also able to implement a variety of other univariate and multivariate models including but not limited to linear and logistic regression, ANOVA, EFA, CFA, and PCA.
I have conceptualized and implemented multiple research projects which have led to presentations at scientific conferences as well as published chapters and articles in scientific handbooks and journals.
I use the EEGlab and ERPlab packages in MATLAB to clean my EEG data. I have also used MATLAB to implement a psycholinguistics experiment.
I implemented an online self-paced reading experiment using jsPsych at the beginning of the COVID-19 pandemic.
Responsibilities include: