I am broadly interested in how people understand the emotions and other mental states of those around them (Affective and Social Cognition). My primary approach is to studying such reasoning is by building computational cognitive models. 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, and using tools from computer science (probabilistic modeling; machine learning; natural language processing; social network analysis).

Understanding how humans reason about emotions and mental states (top) lets us build computational models and artificial intelligence that can similarly reason about emotions and mental states (right arrow). Furthermore, having better computational models (bottom) will allow us to ask more precise questions about the nature of human cognition (left arrow). This results in a virtuous cycle where scientific progress in psychology fuels progress in artificial intelligence which in turn fuels more progress in psychology.

Core Research Directions

I build computational models of how people reason about emotions.
[Figure adapted from Ong et al., 2015]

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.

My research seeks to understand the cognitive mechanisms that underlie such complex reasoning, as well as how such reasoning is learnt over development.

Current projects include:
  • Models of cognitive appraisals;
  • Integrating emotions into models of Theory of Mind (e.g., goal-directed emotional expression);
  • Developmental models of affective cognition.
Sample publications:
  • Ong, D. C., Zaki, J., & Goodman, N. D. (2019). Computational models of emotion inference in Theory of Mind: A review and roadmap. Topics in Cognitive Science. 11(2), 338-357.

  • Asaba, M.*, Ong, D. C.*, & Gweon, H. (2019). Integrating expectations and outcomes: Preschoolers' developing ability to reason about others' emotions. Developmental Psychology, 55(8), 1680-1693.

  • Ong, D. C., Zaki, J., & Goodman, N. D. (2015). Affective Cognition: Exploring lay theories of emotion. Cognition, 143, 141-162.

We study how people understand the emotions of others at a fine-grained, second-by-second level using naturalistic videos.
[Figure from Devlin et al., 2016]

Are people accurate in their understanding of the emotions of those around them? What are the computational, psychological, and neural bases that support accurate emotion judgments?

In addition to using computational modeling and machine learning, 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.

I am especially interested in multimodal emotion understanding from naturalistic stimuli, and how such understanding is contextualized.

Current projects include:
  • Emotional cue integration across multiple modalities
  • Incorporating context into emotion understanding
Sample publications:
  • Ong, D. C., Wu, Z., Zhi-Xuan, T., Reddan, M., Kahhale, I., Mattek, A., & Zaki, J. (2021). Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset. IEEE Transactions on Affective Computing, 12(3), 579-594.

  • Genzer, S.*, Ong, D. C.*, Zaki, J., & Perry, A. (2022). Mu rhythm suppression over sensorimotor regions is associated with greater empathic accuracy. Social Cognitive and Affective Neuroscience, 17(9), 788–801.

We use various approaches to improve machine learning models for emotion recognition.
[Figure from a talk given in Nov 2021]

How can we train machine learning models to recognize human emotions accurately and in context? Can we use such technology to improve mental health and emotional well-being, in domains such as in education?

Although my research involves multiple modalities (images, speech, etc.), many of my projects specifically deal with understanding emotions from natural language.

I am especially interested in ethical affective computing: how should we build and deploy these systems in an ethical manner.

Current projects include:
  • Building better (contextualized; multimodal; fine-grained; time-series) machine learning models for emotion recognition
  • Model interpretability
  • Integrating theory into deep learning models (e.g., using probabilistic programming)
  • Various projects related to ethical affective computing
Sample publications:
  • Ong, D. C., Soh, H., Zaki, J., & Goodman, N. D. (2021). Applying Probabilistic Programming to Affective Computing. IEEE Transactions on Affective Computing, 12(2), 306-317.
    [Best of IEEE Transactions on Affective Computing 2021 Paper Collection 🏆]

  • Ong, D. C. (2021). An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence. In Proceedings of the 9th International Conference on Affective Computing and Intelligent Interaction (ACII 2021).
    [Best Paper Award 🏆]

We are studying various ways to use large language models for psychologically-precise outcomes.
[Figure from an earlier version of Demszky et al. (2023)]

We are interested in using large language models in psychology for a variety of different outcomes (e.g., well-being, motivation).

Current projects include:
  • Developing frameworks to study emotional re-appraisal in large language models
  • Developing frameworks to study empathy in large language models
  • With colleagues like David Yeager, we are studying how to use large language models to provide mindset-supportive language
Sample publications:
  • Demszky*, D., Yang*, D., Yeager*, D. S., Bryan, C. J., Clapper, M., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., Jones, M., Krettek-Cobb, D., Lai, L., JonesMitchell, N., Ong, D. C., Dweck^, C. S., Gross^, J. J., & Pennebaker^, J. W. (2023). Using Large Language Models in Psychology. Nature Reviews Psychology.

  • Zhan, H., Ong, D. C., & Li, J.J. (2023). Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023.

We use social psychological principles to increase people's motivation to empathize
[Figure from intervention described in
Weisz et al., 2020]

We have been using social psychological interventions to increase empathy (by increasing motivation to empathize with others).
In addition to interventions to increase empathy, we are also examining interventions for emotion regulation.

Sample publications:
  • Weisz, E., Chen, P., Ong, D. C., Carlson, R. W., Clark, M. D., & Zaki, J. (2022). A Brief Intervention to Motivate Empathy among Middle School Students. Journal of Experimental Psychology: General, 151(12), 3144–3153.

  • Weisz, E., Ong, D. C., Carlson, R. W., & Zaki, J. (2021). Building Empathy: A Brief Intervention to Promote Social Connection. Emotion, 21(5), 990–999

Collaborators in this line of work include: Patricia Chen, Erika Weisz, Jamil Zaki

The Strategic Resource Use Intervention increases students' exam performance in two randomized controlled trials.
[Figure from Chen et al., 2017]

In collaboration with Patricia Chen and the Motivation and Self-Regulation lab, we have been studying self-regulated learning in students.

Sample publications:
  • Chen, P., Teo, D. W. H., Foo, D. X. Y., Derry, H. A., Hayward, B. T., Schulz, K. W., Hayward, C., McKay, T. A., & Ong, D. C. (2022). Real-World Effectiveness of a Social-Psychological Intervention Translated from Controlled Trials to Classrooms. npj Science of Learning, 7 (20).

  • Chen, P.*, Ong, D. C.*, Ng, J., & Coppola, B. P. (2021). Explore, Exploit, and Prune in the Classroom: Strategic Resource Management Behaviors Predict Performance. AERA Open, 7(1), 1–14.

  • Chen, P., Chavez, O., Ong, D. C., & Gunderson, B. (2017). Strategic Resource Use for Learning: A Self-administered Intervention that Guides Effective Resource Use Enhances Academic Performance. Psychological Science, 28(6), 774-785.

Collaborators in this line of work include: Timothy McKay and the ECoach team at the University of Michigan, Brian Coppola.