Erin H. Bugbee

Erin H. Bugbee

Cognitive Decision Science PhD Student at Carnegie Mellon

Carnegie Mellon University

Biography

I am currently a PhD student in the Department of Social & Decision Sciences at Carnegie Mellon University.

Visit my Academic website.
Download my CV.

Interests
  • Sequential Decision Making
  • Learning in Humans and Machines
  • Behavioral Game Theory
Education
  • PhD Cognitive Decision Science, 2025

    Carnegie Mellon University

  • ScB Statistics with Honors, 2020

    Brown University

  • AB Behavioral Decision Sciences, 2020

    Brown University

Skills

Statistics
R
Python

Experience

 
 
 
 
 
Graduate Researcher
Sep 2020 – Present Pittsburgh, PA
PI: Cleotilde Gonzalez
 
 
 
 
 
Undergraduate Researcher
Sep 2019 – Sep 2020 Providence, RI
PI: Steven Sloman. Studied trust in machines in the workplace through behavioral experimentation.
 
 
 
 
 
President
Sep 2019 – May 2020 Providence, RI
 
 
 
 
 
Sales Analytics & Insights Intern
May 2019 – Aug 2019 Orlando, FL
Parks, Experiences, and Consumer Products
 
 
 
 
 
Undergraduate Researcher
Jan 2019 – May 2020 Providence, RI
PI: Matthew Nassar. Used reinforcement learning models to understand how place field remapping might be used to improve learning in dynamic environments through simulations of the multi-armed bandit task.
 
 
 
 
 
Explore Intern
May 2018 – Aug 2018 Redmond, WA
Microsoft Support Engineering Group, Cloud & AI Platform
 
 
 
 
 
Undergraduate Researcher
Jun 2017 – Aug 2017 Providence, RI
Applied techniques from topological data analysis to music information retrieval.

Recent Publications

(2020). SuPP and MaPP: Adaptable Structure-Based Representations for MIR Tasks. In ISMIR.

PDF Cite Code Poster

(2018). SE and SnL Diagrams: Flexible Data Structures for MIR. In ISMIR.

PDF Cite

Teaching

Carnegie Mellon University

  • Thinking in Person vs. Thinking Online, Prof. Danny Oppenheimer (Fall 2020)

Brown University

  • NEUR 1660: Neural Computations in Learning and Decision Making (Spring 2020)
  • CSCI 0100: Data Fluency for All, Head Teaching Assistant (Fall 2019)
  • CLPS 0220: Making Decisions (Spring 2019)
  • CSCI 1951A: Data Science (Spring 2019)
  • PHP 1501: Essentials of Data Analysis (Fall 2018)
  • APMA 1655: Statistical Inference I (Fall 2018)
  • CSCI 0100: Data Fluency for All (Fall 2017)

Projects

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Cognitive Models of Sequential Choice in the Optimal Stopping Task
In the optimal stopping problem, a decision maker aims to select the option that maximizes reward in a sequence, under the condition that they must select it at the time of presentation. Past literature suggests that people use a series of thresholds to make decisions (Lee, 2006), and researchers have developed a hierarchical Bayesian model, Bias-From-Optimal (BFO), to characterize these thresholds (Guan et al., 2015, 2020). BFO relies on optimal thresholds and the idea that people’s thresholds are characterized by how far they are from optimal and how this bias increases or decreases throughout the sequence. In this work, we challenge the assumption that people use thresholds to make decisions. We develop a cognitive model based on Instance-Based Learning Theory (Gonzalez et al., 2003) to demonstrate an inductive process by which individual thresholds are derived, without assuming that people use thresholds or relying on optimal thresholds. The IBL model makes decisions by considering the current value and the distance of its position from the end of the sequence, and learns through feedback from past decisions. Using this model, we simulate the choices of 56 individuals and compare these simulations with empirical data provided by Guan et al. (2020). Our results demonstrate that the IBL model replicates human behavior and generates the BFO model’s thresholds, without assuming any thresholds. Overall, our approach improves upon previous methods by producing cognitively plausible choices, resembling those of humans. The IBL model can therefore be used to predict human risk tendencies in sequential choice tasks.
Cognitive Models of Sequential Choice in the Optimal Stopping Task