Avatar

John D. Patterson

Postdoctoral Scholar

Pennsylvania State University

About

John Patterson is currently a postdoctoral scholar in the Department of Psychology at Pennsylvania State University. He earned his Ph.D. from Binghamton University in 2019. He is interested in how concepts are acquired and represented, how these representations are leveraged for other tasks (e.g., decision making, inference), and how insights in each of these areas can advise applications to education and machine learning. He uses behavioral, computational, and (soon) functional imaging methods to address these questions.

Interests

  • Category learning
  • Representation
  • Comparison
  • Analogy
  • Learning mode
  • Semi-Supervised learning
  • Computational modeling
  • Machine learning

Education

  • PhD in Cognitive Psychology, 2019

    Binghamton University

  • MS in Cognitive Psychology, 2016

    Binghamton University

  • BS in Psyc - Mind, Brain, & Behavior, 2012

    Colorado State University

Technical Skills

Python

R

Statistics,Mixed Models

Research Experience

 
 
 
 
 

Postdoctoral - Karuza Lab

The Pennsylvania State University

Sep 2019 – Present University Park, Pennsylvania
Investigating the role of noise in active learning and multimodal category learning
 
 
 
 
 

Graduate - Kurtz Lab

Binghamton University

Aug 2013 – Aug 2019 Binghamton, New York
 
 
 
 
 

Postbaccalaureate - Clegg Lab

Colorado State University

Aug 2012 – Jul 2013 Fort Collins, Colorado
 
 
 
 
 

Undergraduate - Seger Lab

Colorado State University

Aug 2010 – May 2012 Fort Collins, Colorado

Refereed Publications

Patterson & Karuza (2020) Schrödinger's category: Active learning in the face of label ambiguity

Fuzziness at the category boundary during an active learning task encourages high certainty sampling and lower quality representations.
PDF

Patterson & Kurtz (2020) Comparison-based learning of relational categories (you'll never guess)

The guess-and-correct cycle undermines the benefits of comparison opportunities.
PDF DOI

Patterson, Snoddy, & Kurtz (2019) Family Resemblance in unsupervised categorization: A dissociation between production and evaluation.

Though family resemblance (FR) is prevalent in natural kinds, people rarely produce FR solutions in unsupervised lab settings. Here we …
PDF

Patterson & Kurtz (2018) Semi-supervised learning: A role for similarity in generalization-based learning of relational categories

Unsupervised exposures that are superficially similar to exposures encountered under supervision faciliate conceptual development.
PDF

Patterson, Landy, & Kurtz (2017) Relational concept learning via guided interactive discovery

Active learning facilitates refinement of relational category representations.
PDF
See all publications

Popular Topics

Academic Active learning Analogy Applied Category construction Category learning Choking Classification learning Classification mode Comparison Deep Learning Demo Discovery learning Endorsement learning Evaluation Evolution Family Resemblance Fuzzy boundary Observational mode Performance pressure plot Production R Markdown regression Relational categories Science Education Semi-supervised Learning Similarity Structure Choice Task Transfer Unsupervised Categorization

Contact

  • Moore Building, University Park, PA, 16802, United States
  • 3rd Floor, Room 382A
  • Monday 10:00 to 13:00Wednesday 09:00 to 10:00
  • jdpttrsn
  • jdpatte12