401-863-1544 badrelab@brown.edu


How do we select appropriate actions to reach a desired goal? How do we implement control to retrieve information from memory?

Cognitive control involves choosing from a set of actions or representations in order to achieve a certain goal or outcome. We use cognitive control to carry out a variety of tasks, from making a cup of coffee to remembering where the house keys are. The frontal lobes broadly support cognitive control; regions within the frontal lobe guide action selection, so that we add the coffee grounds before turning on the coffee maker. Regions within the frontal lobe also guide memory retrieval, so that we can still search for our keys even when limited external cues are available.

Our lab is interested in how the organization of the frontal lobe influences goal-directed behavior, as well as dissociations and interactions between the frontal and medial temporal lobe regions in their contribution to memory function. In our research, we use a variety of methods to investigate these questions, including functional magnetic resonance imaging (fMRI) and the behavioral testing of healthy adults and patient populations.

The Badre Lab’s research is supported by The Alfred P. Sloan FoundationThe National Institutes of Health, and Brown University.


Graphical depiction of the difference between generality and separability

Balancing Control Demands In Neural Firing Space

Cognitive control is the brain’s ability to guide thoughts and actions based on varying goals and contexts. The human cognitive control system is incredibly flexible because any input stimulus can lead to any output based on internally maintained goals, contexts, and rules. A hallmark of human control is that the same input can lead to multiple outputs depending on these factors, and we posit that there must be some neural representation of a task which allows this flexibility.

Research has shown that these neural representations depend on a delicate balance between separability and generality. Why does this tradeoff matter for cognitive control? We know the same input can lead to multiple outputs. For example, you may have different actions in response to your phone buzzing. At home, you pick it up, but when you’re driving you ignore it. This rule needs to be general enough to apply to both a Twitter and a text chime, but separable enough to know the difference between being in the driver’s seat on the road and in your garage. Understanding how neural populations manage this separability / generality tradeoff in their representations can tell us a lot about their function. This project aims to quantify this balance in neural populations engaged in cognitive control tasks, through an emergent property in neural firing called dimensionality.

Project Lead: Haley Keglovits

Comparative boxplots of the correlation between stimulus accuracy and predicted utility

Neural Dynamics of Working Memory Gating

Working memory (WM) is a capacity-limited system that enables us to store information over a short period of time, and then later use that information to accomplish our behavioral goals. Because the working memory system is severely limited in storage space, it relies on gating mechanisms. Gating mechanisms control which information enters working memory (input gating), and control what information is selected from within working memory to guide behavior (output gating).

The goal of this project is to characterize how predicted utility, via gating mechanisms, transforms WM representations in the brain. Through a combination of behavioral, fMRI, and multivariate techniques, we have found that predicted utility influences the fidelity of WM representations, where higher-utility items are maintained in memory with greater precision and accuracy compared to lower-utility items.

Project Lead: Emily Levin

Figure depicting the structure of a task for this research project

Investigating how clustering and separation of memories enables behavioral flexibility

We live in a rapidly changing world that requires us to constantly adapt our ways of thinking and approaches to solving problems. While some changes in the world only require minor tweaks to our existing strategies, other changes represent major break points that necessitate radically new approaches. Moreover, adapting to a new situation sometimes means drawing parallels between the current problem and a previous problem we already know how to solve, while other times, older approaches can actually hinder us from discovering the solution. How do we form representations that are general enough to apply to various dissimilar situations, yet specific enough to be useful?

In this project, we investigate how the degree of change and novelty in a task people are performing influences whether they represent the task as a modification of a previous task, as opposed to a new one they’ve never seen before. We then test how this encoding of the task impacts their ability to learn similar tasks in the future, as well as their ability to relearn a task they’ve performed previously.

Project Lead: Olga Lositsky

Learning the dynamic structure of a task

Learning how to perform a typical cognitive task often involves figuring out its ‘rules’ – how stimuli and actions are related to outcomes. The mechanisms underlying this process are well studied. But, learning a task also involves adapting internal cognitive processes to the task’s ‘dynamic structure’ – i.e. the specific timing and order of events relevant to the task.

This study investigates this latter process and how it interacts with rule learning. In behavioral experiments, we find evidence that subjects learn a task’s dynamic structure in the initial trials, and they transfer this knowledge between tasks. Subjects showed both positive (same task structure) and negative (different task structure) transfer, independent of the rules of the task. fMRI experiments in the pipeline will investigate the neurobiological mechanisms underlying this process.

Project Lead: Apoorva Bhandari

Exception-handling in the learning and generalization of rules

The flexibility of human behavior relies on our ability to deploy a variety of previously learnt, abstract rules in novel situations. However, rules often fit a novel situation imperfectly. Our ability to deal with exceptions to rules, therefore, is crucial, both for maintaining abstract rules, and enabling their use in different tasks.

In this study, we examine exception learning strategies and how they impact rule learning. Behavioral experiments provide evidence for a dissociation between rule learning and exception learning, suggesting separate mechanisms. Moreover, different subjects are able to deploy different task rules to the same task by leveraging their ability to separately learn about exceptions. Further experiments will examine how exceptions impact rule learning and generalization.

Project Lead: Apoorva Bhandari

Cosine similarity matrix for a task

Adaptive Retrieval During Decision-making

How would you judge the combination of pickles and peanut butter on a bagel? If you have experience with North American cuisine, you likely know the flavor profiles of each ingredient, and could infer how this item might taste. However, in the less likely case that you had previously combined these ingredients in a desperate gamble at lunch, you might have a more detailed representation of this unique experience.

As this example illustrates, value judgment frequently depends on an ability to retrieve relevant information from different memory stores: episodic details about specific past experiences, or schema-level memory structures such as knowledge about sandwiches. The aim of this project is to examine the neural processes involved in retrieving information stored in memory systems to support value-based decision-making.

We are using computational modeling of behavior and fMRI to examine how subjects make choices based on schematic knowledge about real world items (like food ingredients), and the neural systems involved in retrieving this information, and assessing its value in relation to current goals.

Project Lead: Avinash Vaidya