Broadly, I am interested in how people understand the emotions and other mental states of those around them (Affective and Social Cognition). I study this by building computational cognitive models of reasoning. That is, I investigate how people intuitively reason about those around them, and try to codify such reasoning using computational models (usually, via probabilistic approaches). Computational cognitive modeling (i) allows researchers to specify and test precise, quantitative hypotheses about cognition and affect, and (ii) opens the doors to many applications, such as enabling computers to "reason" about emotions and mental states in a human-like manner.
In my work, I take an interdisciplinary approach, theoretically grounded in cognitive science and affective science, along with tools from computer science (probabilistic modeling and probabilistic programming; machine learning; natural language processing; social network analyses) and behavioral economics (economic games and analyses).
In addition to cognitive science research, I also explore many applications of the modeling work that I do. I am interested in two main areas of applications: applications to technology (e.g., affective computing and affective robotics), and applications to clinical contexts (e.g., improving models of affective and social dysfunction).
I also apply modeling to study empathy, Theory of Mind, and prosocial behavior.
Below, I've listed a sample of several projects that I am currently working on.
When we see someone miss a bus, receive a present, or walk with a skip in their step, we have no difficulty inferring their emotions, their thoughts, and even what they might do next. The ability to effortlessly perform such reasoning, Affective Cognition (reasoning about affect), is crucial to our everyday lives.
Underpinning affective cognition is a set of rich knowledge about emotions, encapsulated in laypeople's intuitive theories of emotion. That is, in people's minds, what are emotions? What causes emotions? What do emotions cause people to do?
I take a probabilistic approach by modeling people's intuitive theories as generative, causal models (i.e., using Bayesian Networks and other similar models). Under this framework, laypeople's intuitive reasoning about emotions can be described by (Bayesian) inference within these causal models. (See here for a more detailed writeup.)
I use a variety of different approaches to address these questions (and much more!) in a variety of different contexts:
Ong, Zaki, & Goodman, Cognition, 2015
Ong, Goodman, & Zaki, Emotion, 2017
Ong, Doctoral Dissertation, 2017
When people reason about the emotions of those around them, are they actually accurate? Or perhaps, are there systematic biases in the way that people interpret different types of information when inferring the emotions of those around them? What are the computational, psychological, and neural bases underlying accurate emotion judgments?
In this set of projects, we are interested in people's accuracy in evaluating the emotions of those around them. My collaborators and I investigate empathic accuracy using challenging, naturalistic contexts. To this end, I use tools from computer science to extract features from videos (e.g., acoustic features, linguistic features) and examine how people use these features in making their judgments. This involves acoustic processing, natural language processing, as well as time-series probabilistic modeling.
In addition to computational modeling, we also study empathic accuracy (i) with neuroscientific approaches (fMRI, EEG), (ii) across demographic groups and across cultures, and (iii) in populations with mood disorders.
Devlin, Zaki, Ong, & Gruber, Cognitive Therapy and Research, 2016
As they say, "No man is an island". People naturally turn to others in their social network for companionship and social support, in both good times and bad. When important life events happen, who do people turn to, and when is the provided social support effective?
I use social network analyses to look at how people navigate their social networks, and how emotions, empathy, and social support propagate within both in-person and online social networks. I also combine network analysis with other approaches (like analyzing natural language text) to examine how social interactions contribute to social support within the network.
Morelli, Ong, Makati, Jackson, & Zaki, PNAS, 2017
The emotions that we feel towards those around us influence our behavior towards them, and I'm interested in (social, emotional, empathic) factors that motivate people to be nice to others.
Ong, Zaki, & Gruber, J. Abnorm. Psych., 2017
Nook, Ong, Morelli, Mitchell, & Zaki, PSPB, 2016
Ong, undergraduate honors thesis, 2011
In the past, prior to my starting graduate school, in roughly reverse chronological order, I have done research in behavioral economics, vision, condensed matter physics, optics, and material science. I've written up some of my previous, completed projects in optics and physics, below.
I developed a method of analyzing anisotropy in speckled images, by analyzing the power spectral distribution in a polar-coordinate representation.
[with Sanjeev Solanki, Xinan Liang, Xuewu Xu]
We investigated how applying an oscillatory shear flow affects the Brownian motion of collodial dimer particles (based on the geometry of the dimer particle).
[with Brian Leahy, Xiang Cheng, Itai Cohen]
We examined how the structure of a collodial crystal (having symmetric dimers in a crystal of spheres) affects the dislocation dynamics of the crystal.
[with Sharon Gerbode, Itai Cohen, and others]