Investigating Human Emotion Perception.
David Whitney, Professor
Psychology
Applications for Fall 2024 are closed for this project.
The ability to quickly and accurately perceive emotion is essential in our daily lives. However, how does the brain process multiple sources of emotional information when making emotional judgments? The brain must take into consideration facial expression, tone of voice, body movement, contextual information, and even beliefs in its judgment of emotion. Our research investigates how human observers perceive emotion through the use of dynamic stimuli (like Hollywood movies) that include much of the contextual information that we experience when making emotional inferences in the real world. To achieve this goal, we will investigate individual differences in emotion perception of dynamic context-rich stimuli. We will use eye-tracking and neuroimaging techniques like EEG while observers are watching various movies and images to answer our research questions.
Role: The research apprentice will be involved in designing stimuli, running experiments, and analyzing data. Students will be trained in research methods, statistics, and data analysis through the use of Python. Students will also be trained to collect and analyze data using eye-tracking and EEG. Students will be responsible for writing Python code to analyze the data. Students will also be expected to read the relevant literature and write reports on the ongoing results of the project. The student will also meet with the supervisor weekly/bi-weekly to discuss preliminary data and background literature, in addition to the opportunity to attend weekly lab meetings.
Qualifications: Required: High motivation, interest in visual perception and cognitive science, basic programming skills, ability to work independently on certain projects.
Individuals who are first-gen/transfer students from underrepresented backgrounds are encouraged to apply. No prior experience in research is needed.
Hours: 9-11 hrs
Related website: https://whitneylab.berkeley.edu/PDFs/Chen.PNAS.2019.pdf
Social Sciences Digital Humanities and Data Science Education, Cognition & Psychology