Elizabeth M Clerkin, PhD

Elizabeth M Clerkin, PhD

Postdoctoral Researcher

Indiana University

About Me

I am quantitative researcher with 7 years of experience designing and executing complex research projects. I have a PhD in Psychology, and I am excitedly pursuing a research career in industry. I am relocating the DC Metro Area in July 2022!

Here are some reasons I could be a great addition to your team:

  • I am a specialist in creating rich data visualizations that communicate a clear story and actionable insights.
  • I am a wiz at analyzing large unstructured datasets as well as experimental design.
  • I am skilled in data collection from human subjects, including mobile eye-tracking which I have done with participants of all ages.
  • I am very comfortable with public speaking, and I have given talks and posters at 9 international research conferences.
  • I have also published articles in top academic journals such as the Proceedings of the National Academy of Sciences and Trends in Cognitive Science.
  • I have a strong desire to always keep learning, and my PhD shows that I can devote myself to becoming an expert on a topic.
  • I have always led with empathy for my research participants and taken a participant-centered approach. I’m ready to apply this same mindset to solving problems in other contexts!

In my free time, I love to bake and bullet journal. I am also passionate about dance — I have been dancing since I was 3!

Interests

  • Data Visualization
  • Person-centered Data Collection
  • Educational Technology

Education

  • PhD in Psychology, September 2021

    Indiana University

  • BA with Honors in Psychology, May 2015

    Indiana University

Skills

R

Data manipulation (e.g., dplyr and other packages in the tidyverse), data visualization (e.g., ggplot2), and statistical analysis.

Statistics

E.g., glm, anova, logistic regression, clustering, model fitting

Python

Algorithm implementation and data visualization (e.g., using Matplotlib).

Eye-tracking

Pupil Labs mobile eye-tracking system — eye-gaze data collection, calibration, and analysis (with infants and adults).

Communication

E.g., oral presentations, poster presentations, outreach events, scientific writing.

Matlab

Behavioral experiment programming using PsychToolbox, data manipulation, and data visualization.

Experience

 
 
 
 
 

Postdoctoral Researcher

Indiana University — Cognitive Development Lab

Sep 2021 – Present
  • Consulted on a series of research projects funded by the National Institutes of Health (National Eye Institute) to examine the development of early visual object recognition. Steered research methodology and analytic approaches to build upon insights from my doctoral research program.
  • Designed survey to assess image quality, scripted in HTML for display on Amazon Mechanical Turk, and distributed to 1000+ individuals.
  • Authored an article published in the Proceedings of the National Academy of Sciences, which is one of the foremost scientific journals and has an impact factor of 11.205.
 
 
 
 
 

Doctoral Researcher

Indiana University — Cognitive Development Lab

Aug 2015 – Sep 2021
  • Conducted 150+ participant testing sessions, using experimental methods (e.g., computerized visual search/matching tasks) and observational methods (e.g., observations of interpersonal interactions and eye-gaze tracking). Supervised junior researchers during 150+ experimental testing sessions.
  • Implemented exploratory data analysis techniques on a dataset consisting of ~200 hours of video collected from head-mounted cameras worn by participants during daily life.
  • Authored impactful research articles published in top academic journals which have been cited by researchers more than 250 times since 2017 and covered in publications such as The Atlantic.
  • Presented findings to a range of audiences including small groups of colleagues and crowds of 100+ attendees at 9 international research conferences.
  • Fostered collaborations with colleagues across disciplines such as machine learning and computer vision to meet mutual research goals as part of a $3 million dollar research initiative.
  • Cultivated and mentored a diverse team of 15 undergraduate junior researchers including international students and students from groups historically underrepresented in research.

More Publications

Quickly discover relevant content by filtering publications.

Real-world statistics at two timescales and a mechanism for infant learning of object names

Infants learn mappings between heard names and seen things before their first birthday and before they produce spoken language. Two challenges to explaining this early learning are the immaturity of infant memory systems and the infrequency of any individual object name in the heard language input. We quantified the frequency of visual referents, heard names, and the cooccurrences of referents and names in infant everyday experiences. We discovered statistical patterns at two timescales that align with a cortical mechanism of associative memory formation that supports the rapid formation of durable associative memories from very few experienced cooccurrences.
Project DOI
Real-world statistics at two timescales and a mechanism for infant learning of object names

How everyday visual experience prepares the way for learning object names

A key question in early word learning is how infants learn their first object names despite a natural environment thought to provide messy data for linking object names to their referents. Using head cameras worn by 7 to 11-month-old infants in the home, we document the statistics of visual objects, spoken object names, and their co-occurrence in everyday meal time events. We show that the extremely right skewed frequency distribution of visual objects underlies word-referent cooccurrence statistics that set up a clear signal in the noise upon which infants could capitalize to learn their first object names.
PDF Project
How everyday visual experience prepares the way for learning object names

The Developing Infant Creates a Curriculum for Statistical Learning

New efforts are using head-cameras and eye-trackers worn by infants to capture everyday visual environments from the infant learner’s point of view. From this vantage point, the training sets for statistical learning develop as the infant’s sensory-motor abilities develop, yielding a series of ordered data sets for visual learning that differ in content and structure between time points but are highly selective at each time point. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains.
Linda B Smith, Swapnaa Jayaraman, Elizabeth M Clerkin, PhD, Chen Yu
PDF Project DOI
The Developing Infant Creates a Curriculum for Statistical Learning

Real-world visual statistics and infants' first-learned object names

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.
Elizabeth M Clerkin, PhD, Elizabeth Hart, James M Rehg, Chen Yu, Linda B Smith
PDF Project DOI
Real-world visual statistics and infants' first-learned object names

How everyday visual experience prepares the way for learning object names

Infants learn their first object names by linking heard names to scenes. A core theoretical problem is how infants select the right referent from cluttered and ambiguous scenes. Here we show how the distributional properties of objects in young infants’ visual experiences may help solve this core problem in early word learning. Infant perspective scenes of mealtimes were collected using head cameras worn by 9-month-old infants (147 mealtimes from 8 infants). The frequency distribution of objects was extremely skewed with the most frequent visual objects corresponding to the normatively first learned object names in English.
Elizabeth M Clerkin, PhD, Elizabeth Hart, James M Rehg, Chen Yu, Linda B Smith
PDF Project DOI
How everyday visual experience prepares the way for learning object names

Projects

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All Natural Statistics

the Homeview Project

The Homeview project is a large corpus of infant perspective scenes (using head cameras) and audio in the home as infants 1 to 24 months of age go about their daily life. The corpus, with over 500 hours of head camera video promises new insights into the natural statistics of visual experiences

Featured Talk

The everyday statistics of objects and their names

The frequency properties of visual objects and object names in daily life are fundamentally different, and they do not set up a rich co-occurrence structure. However, both modalities select for early-learned object names. The rarity of co-occurrences of early-learned object-name pairs in the mealtime context suggests that infants may be learning from minimal co-occurrence data as long as half of the pair (the object or its name) is highly frequent.
Jul 24, 2019 — Jul 27, 2019 Palais des Congrès de Montréal
Project Slides Follow
The everyday statistics of objects and their names

Contact

  • 1101 East 10th St, Bloomington, IN 47405
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