Data-rich human communication
As a cognitive scientist and data scientist, I take a data-rich approach to understanding how people collaborate, bond, and fight. To do that, I weave together a variety of data sources from the lab and the real world for a converging tapestry of the many ways in which our language, movement, decisions, and emotions change during social contact. Understanding how context —including conversational goals, social connections, and physical spaces— shape our emerging behaviors is a primary goal of my research, embedded within rich traditions of dynamical and ecological perspectives on human behavior and cognition broadly.
I’m also interested in developing methods to quantify social interaction, promoting open science research and education, and creating opportunities for cognitive scientists and psychologists who are interested in big data, naturally occurring data, and data science.
I’m currently a postdoctoral scholar working with Tom Griffiths in the Institute of Cognitive and Brain Sciences at the University of California, Berkeley and a Moore-Sloan Data Science Fellow at the Berkeley Institute for Data Science.
In August 2018, I’ll be joining the University of Connecticut’s Department of Psychological Sciences as an Assistant Professor of Ecological Psychology within the Perception, Action, Cognition division.
Some recent work
Outreach: Videos and code from the Data on the Mind 2017 summer workshop are now available! Check out 11 tutorials dedicated to helping cognitive scientists explore questions about cognition and behavior with big and naturally occurring data. Find out more through the links below.
Call to action: Using big or naturally occurring data sets (BONDS) to test theories outside the lab by finding the traces of the behavioral and cognitive processes within the human-generated data (Paxton & Griffiths, 2017, Behavior Research Methods). Read the open-access article!
Publication: Using wearable technology to quantify the nonlinear context-sensitivity of interpersonal coordination to high- and low-level constraints during conversation (Paxton & Dale, 2017, Frontiers in Psychology). Read the open-access article, and check out our registered repository of data and code on OSF!
Methods development: Automatically and reproducibly quantify multi-level linguistic alignment in natural conversation using ALIGN—Analyzing Linguistic Interactions with Generalizable techNiques (Duran, Paxton, & Fusaroli, under review). Find our Python package on GitHub, or install it directly from PyPI. Read about it in our preprint on OSF’s PsyArXiv!
Fellowship: Moore-Sloan Data Science Fellow, Berkeley Institute for Data Science, AY 2016-2018.