66 citations · 236 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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 (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…
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…