About Us

Note: the Niv Lab is currently quite full and we are not actively searching for students, nor do we currently have open positions for postdocs.

The Niv Lab, co-run by Dr. Yael Niv and Dr. Angela Langdon, focuses on several distinct and inter-related lines of research all related to understanding the computational basis of learning and decision making in the brain. We use behavior – and sometimes neural data – to constrain computational models of how experience changes future decisions and choices (i.e., learning). 

One major theme of our research is understanding how animals and humans learn useful task representations, effectively grouping experience into latent causes or clusters that allow fast and generalizable learning. 

Another major theme, led by Dr. Langdon, focuses on timing as a ubiquitous but unobservable dimension of tasks and their solutions. This line of research uses neural recordings as well as behavioral data from rodents from our collaborators (mainly: the lab of Geoff Schoenbaum at NIDA) to understand how the brain uses timing to make inferences and predictions. Within the brain, the focus is on dopamine, the basal ganglia and the orbitofrontal cortex (though we study other brain areas too). Note that we do not run experiments on rodents ourselves, and do not have a “wet lab.”

A third focus, led by Dr. Yael Niv, is on computational psychiatry/psychotherapy. Here we use computational models of learning and decision making to study mental illnesses such as mood and anxiety disorders, and transdiagnostic symptoms such as rumination. We are not focused on a single illness, and are developing behavioral assays that cut across many (e.g., measuring individual differences in representation learning or reinforcement learning) in order to understand the underlying axes of mental illnesses, and to predict treatment response. 

As an integral part of our research, we run experiments on human participants. These mostly consist of behavioral experiments, many of which are conducted on large online samples. Occasionally, we conduct fMRI experiments when we have a hypothesis regarding brain mechanisms that can be answered better by looking into the brain. We also run studies on patient populations (through the Rutgers-Princeton Center for Computational Cognitive NeuroPsychiatry), and run human versions of some of the rodent experiments we model. 

Our primary modeling frameworks are reinforcement learning and (Bayesian) latent-cause inference, and we make heavy use of (hierarchical) model-fitting and statistical model comparison. We also use machine-learning techniques of multivariate pattern analysis and representation similarity analysis of fMRI data, as well as classification and decoding techniques in neural data analysis. Computational modeling is not only the tool we use to analyze data; it is really how we think about function in the brain, so everything we do involves modeling in a two-way feedback loop between experimental data and model. 

We also have other interests that are somewhat loosely tied to our main focus areas. These include projects as diverse as modeling social interactions (in particular, mentoring), developing tasks with good psychometric properties for computational psychiatry, and modeling curiosity. In general, our focus shifts slightly over time, through the interests of lab members, as we encourage lab members to study what they are most interested in (of course, we accept to the lab those whose interests align with our current interests). This means that while we have some overarching themes to our work, projects may not always feel strongly interconnected. In any case, we encourage crosstalk between projects, collaboration within the lab and with others outside it, and we support individual exploration and growth. In an effort to uncover the “hidden curriculum” of academic work, we have a detailed lab manual that expresses our co-created values, expectations, and lab rules.

 

List of grants currently supporting our research: 

NIDA R01 (ends 2022) - Orbitofrontal Cortex as a Cognitive Map of Task States - The goal of this research is to test the hypothesis that the orbitofrontal cortex represents the current task state at any point in time (a “cognitive map” of the current task). 

NIMH R21 (ends 2022) - Quantifying the latent-cause inference process in humans - We are developing a novel task, together with a modeling framework, that will allow us to precisely quantify key parameters of the process of latent cause inference in humans.

NIMH R01 - A Computational Psychiatry Investigation of the effects of Mood on Reward Learning and Attention - The goal of this research is to test the prediction that bipolar disorder and major depression can be distinguished from one another at both a behavioral and a neural level, in terms of different patterns of abnormal interaction between mood, reinforcement learning, and attention.

NIDA R01 - Decoding the dynamic representation of reward predictions across mesocorticostriatal circuits during learning - This project uses a treasure trove of previously recorded neural data from throughout the mesocorticostriatal circuitry that supports reward learning, to elucidate the computational role of each component of the circuit, their interactions, and how these components are affected by cocaine.

Wellcome Leap MCPsych project - Precision psychiatry for treatment selection in depression - The goal of this project is to develop a suite of behavioral tasks that can help predict treatment response for patients with depression focusing on cognitive behavioral therapy and/or pharmacotherapy. 

NIMH R01 - CRCNS US-Israel Research Proposal: Computational Phenotyping of Decision Making in Adolescent Psychopathology - The aim of this project is to characterize computationally and neurally the decision-making phenotype of adolescents, and to relate this phenotype to psychopathology. Collaborative proposal with co-Investigators: Catherine Hartley (NYU), Eran Eldar (Hebrew University), and Gal Shoval (Geha Mental Health Center and Tel Aviv University).

 

For the auditorily inclined: An informal podcast about what the lab does (starts around min 9)
 

Lab interests in progress [image] !

Read more about us in the press: