66 citations · 236 across the 19 of their papers we have counts for
20 papers
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge +37
Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…
Capturing the Complexity of Human Strategic Decision-Making with Machine Learning
Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke +1
Understanding how people behave in strategic settings--where they make decisions based on their expectations about the behavior of others--is a long-standing problem in the behavio…
Large Language Models Assume People are More Rational than We Really are
Ryan Liu, Jiayi Geng, Joshua C. Peterson +2
In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit int…
The Universal Law of Generalization Holds for Naturalistic Stimuli
Raja Marjieh, Nori Jacoby, Joshua C. Peterson +1
Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that…
On the Informativeness of Supervision Signals
Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins +7
Supervised learning typically focuses on learning transferable representations from training examples annotated by humans. While rich annotations (like soft labels) carry more info…
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior
Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday +1
Traditional models of category learning in psychology focus on representation at the category level as opposed to the stimulus level, even though the two are likely to interact. Th…
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decisions
Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifac…
Human uncertainty makes classification more robust
Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths +1
The classification performance of deep neural networks has begun to asymptote at near-perfect levels. However, their ability to generalize outside the training set and their robust…
Cognitive Model Priors for Predicting Human Decisions
David D. Bourgin, Joshua C. Peterson, Daniel Reichman +2
Human decision-making underlies all economic behavior. For the past four decades, human decision-making under uncertainty has continued to be explained by theoretical models based…
Capturing human categorization of natural images at scale by combining deep networks and cognitive models
Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
Human categorization is one of the most important and successful targets of cognitive modeling in psychology, yet decades of development and assessment of competing models have bee…
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
Mayank Agrawal, Joshua C. Peterson, Thomas L. Griffiths
Large-scale behavioral datasets enable researchers to use complex machine learning algorithms to better predict human behavior, yet this increased predictive power does not always…
Learning a face space for experiments on human identity
Jordan W. Suchow, Joshua C. Peterson, Thomas L. Griffiths
Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that…
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels
Joshua C. Peterson, Paul Soulos, Aida Nematzadeh +1
Modern convolutional neural networks (CNNs) are able to achieve human-level object classification accuracy on specific tasks, and currently outperform competing models in explainin…
Capturing human category representations by sampling in deep feature spaces
Joshua C. Peterson, Jordan W. Suchow, Krisha Aghi +2
Understanding how people represent categories is a core problem in cognitive science. Decades of research have yielded a variety of formal theories of categories, but validating th…
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
Over the last few decades, psychologists have developed sophisticated formal models of human categorization using simple artificial stimuli. In this paper, we use modern machine le…
Evaluating vector-space models of analogy
Dawn Chen, Joshua C. Peterson, Thomas L. Griffiths
Vector-space representations provide geometric tools for reasoning about the similarity of a set of objects and their relationships. Recent machine learning methods for deriving ve…
Evaluating (and improving) the correspondence between deep neural networks and human representations
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stim…
Evidence for the size principle in semantic and perceptual domains
Joshua C. Peterson, Thomas L. Griffiths
Shepard's Universal Law of Generalization offered a compelling case for the first physics-like law in cognitive science that should hold for all intelligent agents in the universe.…
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC
Steven Tang, Joshua C. Peterson, Zachary A. Pardos
Digital learning environments generate a precise record of the actions learners take as they interact with learning materials and complete exercises towards comprehension. With thi…
Adapting Deep Network Features to Capture Psychological Representations
Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Deep neural networks have become increasingly successful at solving classic perception problems such as object recognition, semantic segmentation, and scene understanding, often re…