Effort based decision-making
Sonia Bishop, Professor
Psychology
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
General to all projects:
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 one:
This study will investigate how individuals differ in the valuation of effort during decision making. Willingness to expend effort in order to obtain rewards and avoid harm may be a crucial distinguishing feature among individuals vulnerable to anxiety or depression.
Qualifications: RA responsibilities: For this project, the RA will help in the process of designing and collecting data. Study design will be driven by Dr. Bishop and Jennifer Senta (graduate student), coding of the experiment will be a joint effort between Jennifer and the RA, as will data collection and data analysis. Data collection will be conducted in-lab or online. RA’s are also expected to attend and participate in zoom lab meetings.
Qualifications: RA must have some programming ability (preferably in python). Experience running participants in psychology experiments is also useful.
Hours: 9-11 hrs
Related website: http://bishoplab.berkeley.edu/
Education, Cognition & Psychology Social Sciences