Sonia Bishop, Professor

Closed (1) (1) Biased priors or inference in depressed and anxious individuals?

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

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.


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).


Day-to-day supervisor for this project: Jennifer Senta, Graduate Student

Qualifications: RA must have some programming ability (preferably in python). Experience running participants in psychology experiments is also useful.

Weekly Hours: 9-11 hrs

Related website: http://bishoplab.berkeley.edu

Closed (2) Cognitive and computational investigation of influences of anxiety and depression on human decision making under uncertainty.

Applications for fall 2021 are now closed for this project.

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:
We have previously shown that anxiety and depression are linked to difficulties adjusting behavior to the volatility of the environment (i.e. whether one's actions tend to result in similar or different outcomes across time) - Browning et al. Nature Neuroscience,2015. We are interested in extending this in two ways. First, by looking at whether this holds for other forms of psychopathology (e.g. individuals high in mania or schizotypy) and second, by looking at whether other forms of decision-making under uncertainty are also impacted - for example decision-making under ambiguity (where information needed to calculate outcome probabilities is missing).

To assist with designing the tasks to be used (e.g. preparing stimuli, scripting, adapting tasks for use both in the lab and online), to assist with data collection using tasks and symptom questionnaires (online and possibly in-lab depending on your preference), where appropriate skill-set - to assist with preliminary analysis of data., Post-Doc

Qualifications: cog sci or computer sci or psych major or minor; strong programming ability in python or javascript a bonus

Weekly Hours: 9-11 hrs

Off-Campus Research Site: Assistance can either be fully remote or include testing in our laboratory using covid-approved protocols. We. are happy to work with your preference.

Related website: http://bishoplab.berkeley.edu/

Closed (3) Effort based decision-making

Applications for fall 2021 are now closed for this project.

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.
, Graduate Student

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.

Weekly Hours: 9-11 hrs

Related website: http://bishoplab.berkeley.edu/

Closed (4) Exploring the brain's response to faces using maps of the cortical surface

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

In this project, we are using multi-feature encoding models to analyse functional magnetic resonance imaging data collected when participants look at naturalistic examples of emotional faces. The goal is to better understand how tuning to facial information (expression, gender, age etc) varies across the cortical surface of the human brain. We also seek to understand how this varies as a function of individual differences in Autism related traits and face processing abilities.

As part of this project, we need to create maps of the cortical surface of participants' brains. We will guide you through leveraging Python packages and applications to build flatmaps, that is flattened images of the brain, that help visualize its 3D surface as a 2D surface, from MRI images.
Working on this project offers the opportunity to learn more about structures in the brain, get experience directly exploring brain images and processing complex neuroimaging data and to improve your understanding of MRI analysis and coding in Python. It will also provide the opportunity to see a large, long-term research project from behind the scenes, and actively participate in lab meetings and discussions.

Qualifications: - Some programming experience in Python - Basic navigation abilities in Command Line or Terminal - Interested in the intersection between psychology, neuroscience and computer science

Weekly Hours: 9-11 hrs

Off-Campus Research Site: remote working, zoom supervision
you need to have a laptop you can remote log in from - either a mac or pc

Related website: http://bishoplab.berkeley.edu