Faculty

Tom Griffiths

Tom Griffiths, Lab Director

(webpage)


Postdocs

Robert Hawkins

Robert Hawkins

(webpage) I am interested in the cognitive mechanisms that allow people to flexibly coordinate and communicate with one another. My work uses computational models and multi-agent experiments to test theories of learning and adaptation in social interaction.


Jianqiao Zhu

Jianqiao Zhu

I am interested in understanding the rational and computational principles of human cognition, particularly in relation to judgment and decision-making processes. My work has been motivated by my love for probability and how it can be applied in the psychology of chance. This includes the idea that coherent judgments should adhere to the same mathematical structure as probabilities. However, it is not uncommon for human psychology to violate the rules of probability theory when making judgments about chance. To address this discrepancy between normative and descriptive aspects of judgmental probability and decision theory, I use a combination of computational statistics and Bayesian machine learning, with the Bayesian sampler model being my most recent approach.


Tom McCoy

Tom McCoy

(webpage) What type of computational system is the mind? I approach this question from the perspective of language, spanning the divide between linguistics and artificial intelligence. I focus on two core topics: reconciling neural and symbolic computation (how can a neural network, such as the brain, represent language - a domain traditionally viewed as symbolic?) and characterizing people's learning biases (how do people learn nuanced linguistic phenomena from so little data?). Through these research directions, I aim to bring AI and cognitive science into closer contact, so that progress in AI can improve our understanding of human cognition, and so that insights from cognitive science can inform the construction of more robust AI systems.


Joshua Peterson

Joshua Peterson

My research employs machine learning and large datasets as tools to understand human cognition and predict behavior. In many domains, machine learning models are the only models that approach human performance for complex naturalistic tasks, and thus provide candidate models of cognition that can be critiqued and improved. My most recent projects explore how machine learning can be used to supplement the ingenuity of researchers by automating the search for interpretable theories of human behavior.


Evan Russek

Evan Russek

Everyday decisions often require solving large computational problems. An impressive feature of human cognition is the ability to arrive at good approximate solutions to these problems. These approximations work quite well in a large set of situations, yet can sometimes lead to mistakes. In my research I’m interested in understanding the types of strategies and representations that allow us to reach these good solutions. I’m also interested in understanding whether trade-offs between making good decisions and preserving resources can help us understand choice biases, habits, and what situations our thoughts are drawn to when we decide. In the past, I’ve studied these questions using a mix of computational modeling of behavior and neuroimaging. Recently, I’ve been utilizing large online datasets of individuals playing board games like chess.


Jake Snell

Jake Snell

(webpage) I am interested in building machine learning algorithms that are adaptive, robust, and reliable. Much of my work centers on meta-learning and continual learning, where a learning algorithm must quickly adapt to new circumstances. I enjoy using tools such as deep learning, Bayesian nonparametrics, state-space models, and distribution-free uncertainty quantification to solve these challenging tasks. I received my Ph.D. in 2021 from the machine learning group at the University of Toronto under the supervision of Richard Zemel.


Ilia Sucholutsky

Ilia Sucholutsky

(webpage) I’m fascinated by deep learning and its ability to reach superhuman performance on so many different tasks. I want to better understand how neural networks achieve such impressive results… and why sometimes they don’t. Recently, I've been focused on improving deep learning in small data settings. The current paradigm in AI research is to train large models on large datasets using massive computational resources. While this trend does lead to improvements in predictive power, it leaves behind the multitude of researchers, companies, and practitioners who do not have access to sufficient funding, compute power, or volume of data. I'm interested in developing data-efficient methods that can help rectify this growing divide.


Cameron Turner

Cameron Turner

I am interested in the evolution of cognition. For animals to make successful decisions they must use information from the environment; including using information to learn. For instance, if a dove wants to avoid hawks adaptively they should both learn what a hawk looks like, and detect cues indicating if a hawk is present. I believe much about cognitive evolution can be understood by thinking about the quality and outcomes of using information. I also have a particular interest in social learning, which results from using information from others. I employ mathematical models to study how selection affects cognition, I also conduct empirical research to study how learning operates. I am part of the Diverse Intelligences project that aims at discovering why animals differ in intelligence.


Graduate Students

Mayank Agrawal

Mayank Agrawal

(webpage) Computation functions as a formal language that can integrate the disciplines across the cognitive science diaspora. Pursuant to this end, I collaborate extensively with psychologists, neuroscientists, computer scientists, and philosophers to tackle foundational problems in high-level cognition (learning, memory, decision-making, control). I seek to embody a pragmatic approach to research, using the tools best suited for the problem at hand. My current research programs are (1) computational cognitive neuroscientific accounts of mental effort; (2) big data and machine learning for theoretical and explanatory progress; and (3) bounded rationality as a normative standard for philosophy.


Ruairidh McLennan Battleday

Ruairidh McLennan Battleday

(webpage) In my research, I study generalization: how our inference about the novel and unknown is guided by our evolved and encountered past. This entails studying and formalizing generalization and analogical learning in humans, and testing these ideas by using them to create better machine-learning algorithms. More broadly, I am interested in furthering our understanding of cognition and intelligence by uniting insights from high-level theories and ideologies of the brain, mind, and computation.


Gianluca Bencomo

Gianluca Bencomo

(webpage) I am interested in probabilistic models that capture the properties of realistic environments and human cognitive limitations. I am currently working on two projects related to continual meta-learning, where the goal is to evaluate inductive biases for task distributions observed sequentially. Methods of interest include Bayesian nonparametrics, state-space models, Bayesian filtering, and approximate inference.


Matt Hardy

Matt Hardy

(website) Every day people encounter a neverending set of complicated decisions, difficult tradeoffs, and unforeseeable developments. How do people navigate this complexity and uncertainty? I study this question using computational modeling, behavioral experiments, and observational data analysis. I am especially interested in investigating psychological phenomena in individuals situated in social networks and groups. Cognition is often studied as an isolated process and uses results from simple, individual-level experiments to predict behavior in real-world domains. However, people rarely make decisions in isolation, and many of life’s dilemmas would be impossible or intractable to solve alone. A better understanding of the relationship between individual and group cognition is key to understanding how people thrive in the complexity and uncertainty the real world presents.


Sreejan Kumar

Sreejan Kumar

(website) One hallmark of human cognition is the ability to form abstract representations to solve complex problems with relatively small amounts of data and strongly generalize these abstractions to other problems. Cognitive scientists have said that the acquisition of these abstractions can be modeled by Bayesian inference over discrete, symbolic, and structured representations such as graphs, grammars, predicate logic, programs, etc. However, some cognitive scientists have argued that abstract knowledge can be modeled as emergent phenomenon from statistical learning of distributed, sub-symbolic systems with relatively unstructured representations. Modern deep learning research have developed a variety of architectures for distributed, sub-symbolic systems that can solve difficult tasks, but the conditions in which these systems can emerge human-like abstractions is unclear. I am interested in combining symbolic probabilistic models with novel cognitive tasks that require abstract problem solving to formally characterize how humans acquire and utilize abstract knowledge. I am also interested in utilizing the same paradigm to figure out the architectures and training regimes in which modern deep learning systems can solve these problems with human-like abstractions.


Raja Marjieh

Raja Marjieh

How do humans derive semantic representations from perceptual objects? What are the computational principles underlying their structure? How can we characterize them? In my research, I engage with these problems by leveraging large-scale online experiments and designing paradigms that implement a human instantiation of various algorithms from physics and machine learning. I am also interested in understanding how these representations are modulated by social interactions, especially in the context of creative and aesthetic processes, such as what constitutes a pleasant chord or melody.


Kerem Oktar

Kerem Oktar

(website) My research aims to clarify the psychological and computational basis of disagreement – across scales, domains, and agents – from definition to intervention. I also study decision-making; in particular, I am interested in understanding people's preferences for relying on intuition vs. deliberation. I take a two-pronged approach to studying these questions. To generate theories, I combine insights from analytical philosophy, probability theory, and empirical psychology. To test these theories, I use behavioral experiments, computational models, and statistics.


Sunayana Rane

Sunayana Rane

To create AI systems that behave in ways we expect, or even share our "values," we need alignment at a more fundamental level. I work primarily on conceptual alignment between AI models and human cognition. How can we train AI systems to understand concepts in a human-like way? At what level (e.g. representational, behavioral) is alignment necessary to produce the behavior we expect? Can we use cognition-inspired methods to better understand AI models by contextualizing their behavior with respect to child and human behavior?


Ted Sumers

Ted Sumers

(website) Language is the bedrock of human society, yet despite intensive study its role in cognition remains mysterious. My research combines reinforcement learning and language games to explore human communication in complex decision-making settings. In particular, I'm using formalisms from reinforcement learning to develop rational models of joint action. I'm applying these insights to both advance our understanding of uniquely human cognition (e.g., how language supports cultural evolution) and develop artificial intelligence which can interact successfully with people (e.g., improving natural language interfaces for value alignment).


Andrea Wynn

Andrea Wynn

(website) I am interested in topics related to building more robust machine learning models, especially by drawing on models of human cognition. Why are some tasks extremely difficult for machine learning models to successfully complete, yet seem to be second nature for humans? How can we ensure that machine learning models and humans are aligned in their definitions of success? These are some of the questions that I seek to answer in my research.


Xuechunzi Bai

Xuechunzi Bai

(website) Broadly, I am interested in applying computational methods and formal models to classic social psychological ideas. Currently, I am interested in two topics; both of them investigate the collateral damage from an otherwise functional approach. In one line of research, I examine how inaccurate stereotypes can result from rational explorations. In another line of research, I explore how social inequality can emerge from rational resource transmissions.


Lab Manager

Maya Malaviya

Maya Malaviya

Maya is fascinated by efficient learning and teaching. Her current research involves both modeling teaching interactions and understanding how humans categorize when given very few examples. She hopes to use her findings to improve pedagogical choices and learning environments. She completed her B.A. in Cognitive Science at University of California, Berkeley. Maya also works as a lab manager in Tania Lombrozo's Concepts and Cognition Lab.


Alumni and Long-Distance Affiliates

Joshua Abbott

Joshua Abbott

Joe Austerweil

Joe Austerweil

Vincent Berthiaume

Vincent Berthiaume

Wesley Baraff Bonawitz

Liz Bonawitz

David Bourgin

David Bourgin

Daphna Buchsbaum

Daphna Buchsbaum

Fred Callaway

Fred Callaway

Kevin Canini

Kevin Canini

Daniel Chada

Daniel Chada

Michael Chang

Michael Chang

Dawn Chen

Dawn Chen

Ishita Dasgupta

Ishita Dasgupta

Rachit Dubey

Rachit Dubey

Naomi Feldman

Naomi Feldman

Vael Gates

Vael Gates

Sharon Goldwater

Sharon Goldwater

Erin Grant

Erin Grant

Jessica Hamrick

Jessica Hamrick

Mark Ho

Mark Ho

Chris Holdgraf

Chris Holdgraf

Anne Hsu

Anne Hsu

Tiffany Hwu

Tiffany Hwu

Nori Jacoby

Nori Jacoby

Rachel Jansen

Rachel Jansen

Peaks Krafft

Peaks Krafft

Thomas Langlois

Thomas Langlois

Casey Lewry

Casey Lewry

Falk Lieder

Falk Lieder

Chris Lucas

Chris Lucas

Jay Martin

Jay Martin

Luke Maurits

Luke Maurits

Stephan Meylan

Stephan Meylan

Thomas Morgan

Thomas Morgan

Sonia Murthy

Sonia Murthy

Aida Nematzadeh

Aida Nematzadeh

M Pacer

M Pacer

Alexandra Paxton

Alexandra Paxton

Avi Press

Avi Press

Anna Rafferty

Anna Rafferty

Florencia Reali

Florencia Reali

Daniel Reichman

Daniel Reichman

Adam Sanborn

Adam Sanborn

Sophia Sanborn

Sophia Sanborn

Benj Shapiro

Benj Shapiro

Lei Shi

Lei Shi

Jordan Suchow

Jordan Suchow

Bill Thompson

Bill Thompson

Bas van Opheusden

Bas van Opheusden

Andrew Whalen

Andrew Whalen

Joseph Jay Williams

Joseph Jay Williams

Frank Wood

Frank Wood

Jing Xu

Jing Xu

Saiwing Yeung

Saiwing Yeung

Julia Ying

Julia Ying

Qiong Zhang

Qiong Zhang

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