People

/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.

/I joined the lab as a postdoc in April 2022. I am broadly interested in elucidating the fundamental computational principles underlying high-level cognitive processes such as learning, attention and perceptual awareness. I find that valuable insights can be gained by using computational models as bridges between high-level cognitive concepts, which are sometimes vague and subjective, and exact mathematical formalism. Towards that aim, I have used the frameworks of dynamical systems, reinforcement learning, and information theory to explain diverse phenomena such as the tradeoff between value and complexity in navigational learning, the effects of mental training on temporal attentional capacity, and the relationship between visual ambiguity and speed of access to awareness. Finally, I am hopeful that a deeper understanding of the computational mechanisms underlying high-level cognition will also lead to improved treatment of debilitating mental disorders in which such mechanisms go awry.

/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. In particular, I think about how much choice people have in the rewards they go after and experience. Sometimes I think people can shape what is rewarding to them. Other times it might be very difficult. I think the amount of control people have over what is rewarding to them ultimately effects what they do. It might effect the goals we set for ourselves or how we go about pursuing those goals as well. What is rewarding to us also might play a role in how we see ourselves and the stories we tell about who we think we (and others) are. This all goes to say that I think reward seems to be related to our identities. It’s worth mentioning that maybe our identities shape what we find rewarding as well.

There are many related questions to this line of thinking. How does our control over reward help or hurt our mental health? When do narratives help or hurt us? Might this be related to mindfulness?

I approach this line of thinking using computational models. I think computational models are great ways to quantify how people act. I also think they are a great tool that can help scientists be specific about what they think is going on inside peoples’ heads when we make choices in our lives.

I am most familliar with methods like Bayesian Inference and Reinforcement Learning. Bayesian methods provide a very intuitive way of testing your ideas as a scientist, since you can see how much evidence supports your idea (hypothesis) directly, and compare that idea directly to other ideas you might have about what is going on. Other methods are better at ruling out out ideas, rather than directly testing them, but not everyone agrees about this.

Reinforcement Learning describes a way that machines can learn. Some people think it is similar to how people learn, but only in certain circumstances. I like Reinforcement Learning because these models (if designed well) are often interpretable, meaning people can look under the hood and understand what they are doing.

If you think these things are interesting too, totally disagree with me, or just want to say hello, send me an email. I’d love to talk.

/I am fascinated by the human mind. Specifically, I am interested in understanding the computational basis of human intelligence, focusing on learning and intuitive inference. I have completed my B.Sc. from Minerva schools majoring in Data Science & Statistics and Cognitive Science. In the past, I focused on developing models exploring reinforcement learning and computational psychiatry. I was also Israel’s judo champion.