People

Yael Niv is a professor of neuroscience and psychology at Princeton University. Her lab studies the computational processes underlying reinforcement learning, focusing on how attention, memory and learning interact to construct task representations that allow efficient learning through optimal generalization. She is co-founder and co-director of the Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry, where she is applying ideas from reinforcement learning to understanding and treating mental illness. Her proudest career accomplishment is winning a graduate mentoring award. In her nonexistent spare time, she is a mom to two awesome boys, and an activist within and outside academia.

I am interested in how people use valenced (i.e. positive and negative) information to understand and divide the world and our experiences. I am the most excited when this is shown to be mood-congruent in some way! To do this I design behavioral experiments, use computational modeling (e.g. reinforcement learning and bayesian inference) and neuroimaging techniques (e.g. fMRI). Specifically, I would like to understand and describe the algorithmic and neural mechanisms that underly rumination and I will use any method I can to do this (including collaboration!).    
 I joined the Niv lab as postdoctoral researcher in July 2021 after completing my PhD in Computational Psychiatry at the Max Planck UCL Centre in London. I am a practicing artist, and enjoy combining scientific research and art to create new insights (whilst also having a traditional painting practice). I am passionate about making computational modeling accessible and our field welcoming and empowering.

The goal of my research in the lab is to develop computational tools to examine mechanisms of change in psychotherapy and subsequently use these computational tools to identify and establish predictors of treatment response to specific psychotherapy interventions, ideally, such that they can be deployed in clinical practice. To achieve this goal, I am building generative computational models of learning and behaviour implicated in psychopathology, in particular depression, and psychotherapy interventions, as well as behavioural paradigms to experimentally assess the behaviour and test the models.
My research interests are strongly shaped by my educational background. I did a Bachelor of Arts in Experimental Psychology at the University of Oxford and a Master of Science in Psychology with a focus on clinical psychology at the University of Zurich. Subsequently, I conducted a PhD at the Translational Neuromodeling Unit at the University of Zurich under the supervision of Dr. Quentin Huys and Prof. Klaas Enno Stephan. During my PhD, I was involved in the AIDA study, a patient study examining mechanisms underlying antidepressant discontinuation and predictors of subsequent relapse. Trying to identify such mechanisms and predictors, I applied a machine learning approach to demographic and clinical data, analyzed neuroimaging data collected during “unconstrained cognition” and applied computational modelling to behavioural data of a physical effort task. In parallel to my research PhD, I underwent training as a psychotherapist.

My research focuses on elucidating the fundamental computational principles underlying experience-based learning of purposeful behaviors. For this, I take an interdisciplinary approach using tools from reinforcement learning, control and information theory to formalize and test ideas inspired by Eastern and Western philosophy of mind. Specifically, I find that the Buddhist epistemological school of thought provides a particularly rich source of insight into the interdependence of descriptive and normative aspects of goal-directed learning in embodied cognitive agents. Most recently, I have been developing a computational theory of how goals shape state representation learning within a perception-action cycle setting. This theory introduces a novel notion of goal-directed, or telic, states, defined as sets of equally preferred experience-sequence distributions. Telic states provide a parsimonious way to quantify goal-directed learning in terms of the statistical divergence between learned behavioral policies and desired experiential sequences and provide a novel perspective relating neural and behavioral data in terms of intrinsically motivated cognitive states.

Hii, I’m Dan! 🙂 I’m originally from Romania, went to college (‘uni’) in the UK at Cambridge where I majored in biochem and computational biology, and I’m now a second-year graduate student here in Psychology (I truly came a long way, both spatially and metaphorically). I am interested in how we learn from feedback and how that affects and is affected by our mental health and emotions. In particular, I am currently studying how this unfolds on social media, i.e how the social feedback we get in the form of likes, shares or comments affects how and what we post and how that relates to mental health concerns such as depression. The interest is also pretty personal as in my spare time I make TikToks and Reels about psychology and linguistics @danniesbrain.

Hi, I’m Jamie. I am trained as a clinical psychologist and previously worked with adolescents struggling with depression, anxiety, and suicidal ideation. Ultimately, I am interested in how we change our behaviours (especially in a therapy context) and how emotions, motivations, costs and rewards influence our decision-making and learning process. I currently have two projects: one is about how emotional states change the way we evaluate effort and the other is about trying to model ambivalence and readiness for behavioural change. I also work together with Isabel Berwian on computational psychiatry projects. On the side, I am building a science journal on child and adolescent mental health for parents and mentor PSY and COS students on a variety of projects.

Hello! I’m Sevan Harootonian, but I go by Sev. I’m interested in studying mentorship and understanding the cognitive mechanisms that contribute to its success. One project I’ve been working on examines how people infer others’ knowledge to determine the best way to teach them. I use Reinforcement Learning and Bayesian models to figure out what mental strategies people apply in teaching situations. Teaching is just one aspect of mentorship, and I’m also interested in other important factors, such as inspiration and motivation.

Hello! I’m Branson. I’m interested in reward, especially for learning and change. Goals are a good example. We decide which goals to set and how to measure our progress, even when we set our sights on new destinations. Though we have to be careful. Our choices of measurement change our journeys, and even our destinations. Broadly, I’m interested in when we decide to be flexible or stable. We face this balance in our beliefs, our disagreements with other people, our self-image, and our mental health. I try to understand these parts of life using scientific tools from reinforcement learning and Bayesian inference. You can also catch me playing table top role playing games, making playlists, cooking, and hoarding sweaters. If you want to chat — just send me an email. ✧

I am fascinated by the human mind. Specifically, I am interested in understanding the computational basis of human intelligence, focusing on learning, exploration, and information seeking. I study these topics via theoretical and behavioral models employing, mainly, Bayesian statistics and Reinforcement Learning. I have completed my B.Sc. from Minerva University majoring in Data Science & Statistics and Cognitive Science. Before then, I was also Israel’s judo champion.

Hi! I’m interested in studying how humans assess and adapt to their environments to reduce information processing demands and extend the natural limits on their cognition, particularly in the context of an increasingly technologically integrated world. In this pursuit, I joined the Niv Lab in July 2024, prior to which I was a research assistant at the Cognition Lab (Indian Institute of Science), and completed my degrees in Psychology (BA, FLAME University) and Cognitive Neuroscience (MSc, University of York).