Research

Emotions shape our conscious experience and guide our interactions with the world and people around us. Most of these emotions arise from external information that comes in through our senses 👁️👂👃👅✋.

The Computational Cognition & Affect Lab wants to know: How does perceptual information contribute to emotional experience?

We investigate the large-scale neural algorithms that the brain uses to process perceptual and emotional information. Specifically, we apply computational models of perception to human behavioral, self-report, and neuroimaging data collected while people experience emotional stimuli.

We use open-source scientific computing tools for our research. As members of the computational research community, we are also interested in meta-research about improving knowledge and use of scientific computing software.

PS: we sometimes apply tools of psychology and neuroscience to other personal interests!

Computational cognitive & affective neuroscience

Current projects include…

Perceptual building blocks for subjective emotions

Emotions like surprise often arise when we see something unexpected and personally meaningful to us. Personal meaning differs from person to person and can be harder to study, but one visual stimulus pretty reliably surprises people and might be useful to scientists: magic tricks! What are the perceptual ingredients of surprise induced by magic tricks?

Meta-science of computational research methods

Current projects include…

Computational training for behavioral scientists

Behavioral sciences like psychology and neuroscience are becoming increasingly computational in the age of Big Data. How can psychology and neuroscience programs offer the training that future behavioral scientists need? Specifically, how can stats courses in psychology and neuroscience act as a stepping stone to building more computational skills?

Building community resources for scientists working on public datasets

Many different groups of scientists are often conducting simultaneous studies on the same public datasets. How can this scientific community share knowledge and expertise about the data, both independently from and in harmony with the original stewards of the data?

Relevant publications:

Contributing to open-source scientific software

We try to leave our software better than we found it! This often takes the form of collaborating with users and maintainers on GitHub to fix and improve functionality for packages that are used by many other scientists.

Sample contributions:

  • emonet-pytorch: PyTorch version of the EmoNet convolutional neural network for emotion classification from images (Kragel et al., 2019)
  • ggseg: R package for plotting whole-brain parcel statistics using ggplot2

Other projects

Other current projects include…

Memory mechanisms of trivia expertise

How are some people predisposed to remember lots of random facts?

Current research questions include:

  • How are autobiographical memories for learning trivia facts similar to vs. different from autobiographical memories for other life events?
  • How does autobiographical memory for learning trivia facts relate to trivia competition performance?

Relevant publications:

This project is being conducted in collaboration with the Aly Lab at UC Berkeley.