Anne Collins, Professor

Closed (1) Learning and generalization

Closed. This professor is continuing with Spring 2017 apprentices on this project; no new apprentices needed for Fall 2017.

To learn quickly in a new situation, we often simply figure out the best way to reuse skills we already learned in the past. Identifying which skills to generalize now is a challenging task. How do we do it?
The proposed research plans to investigate learning and decision making in healthy young adults. We use computerized tasks to probe how multiple systems contribute to let us learn efficiently in different situations; and how these different systems interact with each other. In this particular project, we investigate how we learn to generalize, and how this ability changes with time.

This research project involves three steps. First, we will develop the code needed to run the experiments. The mentor will provide a precisely defined experimental design as well as existing code for similar experiments; they will guide the student through adapting code for the proposed experiment. Second, we will recruit participants and collect data for this experiment. Third, we will analyze this behavioral data, potentially including computational model fitting.

Experiments for this project will take place online through the Amazon Mechanical Turk platform.


Research assistants will have an opportunity to participate in multiple stages of the research process and to receive mentoring along the way.

In particular, research assistants will be responsible for coding the experiment. They will then be responsible for running the experiment online, through the several steps required: advertising, running the task, monitoring the software and data collection, organizing collected data and analyzing data. They will receive training and supervision in these tasks.

Furthermore, research assistants will be expected to familiarize themselves with the goals and the contents of the project. They should be able to clearly explain the purpose and predictions of the experiment. The mentor will provide guidance in reading some of the relevant literature, to put the specific project in a broader context.

Research assistants will be required to meet regularly with their mentor and to keep track of their progress in the project.


Qualifications: To work on this project, we will use the Amazon Mechanical Turk platform (https://www.mturk.com/mturk/), with support from Psiturk (https://psiturk.org/). For this project, RAs will need to use code in javascript, html, python, and manipulate SQL databases. Mentors will provide support to learn these skills, but some coding skills are strongly recommended and preferred.

Weekly Hours: to be negotiated

Related website: https://www.ocf.berkeley.edu/~acollins/

Closed (2) Working memory and reward-based learning during adolescence

Applications for Fall 2017 are now closed for this project.

How does working memory develop during adolescence and how does its involvement in learning change?

We know that different parts of the brain mature at different stages during development. The Basal Ganglia, heavily involved in reward-based learning, mature much earlier than the prefrontal cortex, involved in working memory. Correspondingly, previous studies have shown that working memory changes during development - and other studies have shown that reward-based learning changes. But how do the two systems interact with each other? We want to answer this question by testing 7-18 year olds in working-memory and reward-based learning tasks and by creating cognitive models of their performance that can tell us exactly what strategy they used.

Furthermore, this project investigates the role of puberty in these systems. Hormones like testosterone and estradiol have large influences on brain development and might play a critical role in how working memory and reward-based learning change over time. We will therefore also test the hormone levels of our participants and assess how they influence the strategies they employ in the tasks.

Undergraduate researchers will be involved in participant recruitment, data collection, and data analysis. Data collection will involve distributing flyers to potential participants (or their parents), explaining the study, visit community events with potential participants, contact schools, etc. Data collection will involve testing the participants on 4 tasks and ask them to fill out several questionnaires, for sessions of approximately 2 hours.

Undergraduates will be expected to familiarize themselves with the project, reading suggested literature, so that they can learn more about the hypotheses and goal of the research. They will also be expected to participate in the more general lab experience, with regular group or lab meetings with other lab members and the PI.

Day-to-day supervisor for this project: Maria Eckstein, Graduate Student

Qualifications: Interested students should be interested in the topic of this study and should have an interest in working with children/teenagers. They should be available to work on this outside usual work hours (after school, holidays, week-ends), as these are the times at which families can come to the lab. Previous experience in working with children and families is a bonus!

Weekly Hours: to be negotiated

Related website: https://www.ocf.berkeley.edu/~acollins/

Closed (3) Modeling working memory and reward-based learning

Applications for Fall 2017 are now closed for this project.

Over the course of our lives we learn to make the choices that will maximize reward. As a young adult, you have probably figured out that eating a donut is more rewarding than eating a plain piece of bread. As such, you are more likely to choose to eat the donut. You have also learned more complex behaviors for maximizing reward, like the long-term strategies involved in earning good grades or the praise of your professors. Our question is: How do we learn those complex strategies? How do we generalize our previous knowledge to adapt quickly to new environments? Which brain systems contribute to learning from reward, and how do they interact to produce those behaviors?

The proposed research plans to investigate learning and decision making in healthy young adults. We use computerized tasks to probe how multiple systems contribute to let us learn efficiently in different situations; and how these different systems interact with each other. In this particular project, we investigate how rewarding outcomes influence how we act.

This research project is going to focus heavily on data analytics and computational modeling. Using both new and existing data sets, the students are going to work with the mentor on producing mathematical models of decision-making. Undergraduates will also spend time analyzing and processing neural data, like that obtained through EEG.

First, the apprentice will be involved in experimental design and the writing of experimental code in Matlab/Psychtoolbox, or in Javascript for online testing in Amazon Mechanical Turk. They may also be responsible for the recruitment and testing of human subjects. The undergraduate researcher will be involved in building and testing mathematical models of reinforcement learning and working memory. Additionally, the apprentice will be involved in analyzing behavioral data.

Undergraduates will be expected to familiarize themselves with the project and read suggested literature so that they can learn more about the hypotheses and goal of the research. They will also be expected to participate in the more general lab experience, with regular group or lab meetings with other lab members and the PI.

Day-to-day supervisor for this project: Sarah Master, Staff Researcher

Qualifications: Applicants should have a strong background in a quantitative field, such as mathematics or computer science. Knowledge of neuroscience or cognitive science is not necessary, but interest in either topic is required. Students who have previously worked in or taken a class on computational modeling are encouraged to apply, though any students with a basic understanding of modeling and/or programming are welcome to. Students must be motivated, organized, and reliable.

Weekly Hours: 9-12 hrs

Related website: https://www.ocf.berkeley.edu/~acollins/