Biased priors or inference in depressed and anxious individuals?
Sonia Bishop, Professor
Psychology
Closed. This professor is continuing with Spring 2024 apprentices on this project; no new apprentices needed for Fall 2024.
General to all projects listed:
Computational models have been a powerful tool for studying decision-making in both psychology and neuroscience. They have recently become popular in psychiatry as well. Part of the appeal has been that computational approaches delineate individual differences in decision-making that can explain why people with different psychiatric disorders (such as anxiety or depression) often make poor decisions.
Our lab investigate potential decision-making biases exhibited by anxious and depressed individuals. To do so, we leverage behavioral experiments, fMRI, and computational models inspired by Bayesian statistics and reinforcement learning algorithms from AI.
Specific to this project:
This study aims to investigate how anxious and depressed individuals update beliefs about themselves. Specifically, we will look at whether anxious and/or depressive disposition (trait anhedonia, trait anxiety etc.) are associated with (1) excessively negative prior beliefs, (2) asymmetric updating of these beliefs for positive or negative feedback, and/or (3) different updating for beliefs about the self vs. others. Teasing apart the contribution of these potential biases will require the use of computational models.
Role: RA primary responsibilities will include data collection and assisting in data analysis. They are also expected to attend and participate in lab meetings.
Behavioral data collection will consist of administering online sessions for RPP participants, as well as in-person sessions in our lab. Code for the experiment has been written, but edits to the experimental code may need to be made during piloting. The experimental code is written in javascript and python and runs on a website hosted on Amazon’s web services.
Data analysis will consist of downloading data from web servers, organizing and cleaning data, and doing basic descriptive statistics and plotting. The RA may also help in fitting computational modes or doing other complex statistical analyses. Analyses will be done in python (and sometimes R).
Qualifications: RA must have some programming ability (preferably in python). Experience running participants in psychology experiments is also useful.
Day-to-day supervisor for this project: Jennifer Senta, Graduate Student
Hours: 9-11 hrs
Related website: http://bishoplab.berkeley.edu
Education, Cognition & Psychology Social Sciences