Selected Publications

Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion (e.g., positive and negative affect intensity). We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings when combined with interpretable machine learning methods, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases.
In PLOS ONE., 2019.

The Iowa Gambling Task (IGT) is widely used to study decision‐making within healthy and psychiatric populations. Here, we propose the Outcome‐Representation Learning (ORL) model, a novel model that provides the best compromise between competing models. We test the performance of the ORL model on 393 subjects’ data collected across multiple research sites, and we show that the ORL reveals distinct patterns of decision‐making in substance‐using populations.
In Cog. Sci., 2018.

Machine learning is becoming more widely accepted across a broad range of scientific disciplines. The easyml (easy machine learning) package lowers the barrier to entry to machine learning, allowing users to apply best-practice regression and classification schemes to their own data.
In BioRxiv, 2017.

There is a growing interest in psychology to apply advanced computational models to decision making data collected from psychiatric populations to better understand maladaptive choice patterns. However, there are currently no easy-to-use tools for those who may not have the sophisticated mathematical/programming background to use such methods. Here, we present an R package which can fit an array of decision making models to a variety of different tasks with a single line of code.
In CPSY, 2017.

Recent Publications

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

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The Outcome‐Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task

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Easyml: Easily Build And Evaluate Machine Learning Models

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The Indirect Effect of Emotion Regulation on Minority Stress and Problematic Substance Use in Lesbian, Gay, and Bisexual Individuals

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Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

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Recent & Upcoming Talks

Recent Posts

Goals of Paramter Estimation When estimating paramters for a given model, we typically aim to make an inference on an individual’s underlying decision process. We may be inferring a variety of different factors, such as the rate at which someone updates their expectations, the way that someone subjectively values an outcome, or the amount of exploration versus exploitation that someone engages in. Once we estimnate an individual’s parameters, we can compare then to other people or even other groups of people.


Answer to post 1 In the previous post, I reviewed the Rescorla-Wagner updating (Delta) rule and its contemporary instantiation. At the end, I asked the following question: How should you change the learning rate so that the expected win rate is always the average of all past outcomes? We will go over the answer to this question before progressing to the use of the Delta rule in modeling human choice.


Short history In 1972, Robert Rescorla and Allan Wagner developed a formal theory of associative learning, the process through which multiple stimuli are associated with one-another. The most widely used example (Fig. 1) of associative learning comes straight from Psychology 101–Pavlov’s dog. Figure 1 The idea is simple, and it’s something that we experience quite often in everyday life. In the same way that Pavlov’s dog begins to drool after hearing a bell, certain cognitive and/or biological processes are triggered when we are exposed to stimuli that we have been exposed to in the past.




A cross-platform Python toolbox for analyzing facial expression data.


An R/Python toolbox for easily fitting a variety of machine learning models.


An R toolbox for fitting an array of decision making models with hierarchical Bayesian analysis.


Abnormal Psychology

I am currently teaching PSYCH 3331 at The Ohio State University. My course takes a dimensional and developmental perspective, and is divided mainly into sections for internalizing, externalizing, and psychotic forms of psychopathology. Further, I use principles of active learning to engage students during class. My course design was inspired largely by Ziv Bell, who developed a “flipped class” curriculum for his students to better understand and apply principles of abnormal psychology to everyday life.