Michael J. Tarr

Michael J. Tarr

Carnegie Mellon University

H-index: 65

North America-United States

About Michael J. Tarr

Michael J. Tarr, With an exceptional h-index of 65 and a recent h-index of 37 (since 2020), a distinguished researcher at Carnegie Mellon University, specializes in the field of visual cognition, face recognition, object recognition, cognitive neuroscience.

His recent articles reflect a diverse array of research interests and contributions to the field:

HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models

Brain diffusion for visual exploration: Cortical discovery using large scale generative models

Neural selectivity for real-world object size in natural images

Thinking like an annotator: Generation of dataset labeling instructions

Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset

Gatekeeping Without Peer Review

A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex

Open-ended instructable embodied agents with memory-augmented large language models

Michael J. Tarr Information

University

Position

Head Department of Psychology

Citations(all)

23160

Citations(since 2020)

4460

Cited By

20243

hIndex(all)

65

hIndex(since 2020)

37

i10Index(all)

120

i10Index(since 2020)

76

Email

University Profile Page

Carnegie Mellon University

Google Scholar

View Google Scholar Profile

Michael J. Tarr Skills & Research Interests

visual cognition

face recognition

object recognition

cognitive neuroscience

Top articles of Michael J. Tarr

Title

Journal

Author(s)

Publication Date

HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models

arXiv preprint arXiv:2404.19065

Gabriel Sarch

Sahil Somani

Raghav Kapoor

Michael J Tarr

Katerina Fragkiadaki

2024/4/29

Brain diffusion for visual exploration: Cortical discovery using large scale generative models

Proceedings of the Conference on Neural Information Processing Systems (NeurIPS); oral presentation.

Andrew F Luo

Margaret M Henderson

Leila Wehbe

Michael J Tarr

2023/6/5

Neural selectivity for real-world object size in natural images

BioRxiv

Andrew F Luo

Leila Wehbe

Michael J Tarr

Margaret M Henderson

2023/3/18

Thinking like an annotator: Generation of dataset labeling instructions

arXiv preprint arXiv:2306.14035

Nadine Chang

Francesco Ferroni

Michael J Tarr

Martial Hebert

Deva Ramanan

2023/6/24

Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset

Nature Machine Intelligence

Aria Y Wang

Kendrick Kay

Thomas Naselaris

Michael J Tarr

Leila Wehbe

2023/12

Gatekeeping Without Peer Review

Michael Tarr

2023/3

A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex

Journal of Neuroscience

Margaret M Henderson

Michael J Tarr

Leila Wehbe

2023/5/31

Open-ended instructable embodied agents with memory-augmented large language models

Gabriel Sarch

Yue Wu

Michael J Tarr

Katerina Fragkiadaki

2023

Selectivity for food in human ventral visual cortex

Communications Biology

Nidhi Jain

Aria Wang

Margaret M Henderson

Ruogu Lin

Jacob S Prince

...

2023/2/15

Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images

bioRxiv

Gabriel H Sarch

Michael J Tarr

Katerina Fragkiadaki

Leila Wehbe

2023/5/30

BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity

arXiv preprint arXiv:2310.04420

Andrew F Luo

Margaret M Henderson

Michael J Tarr

Leila Wehbe

2023/10/6

Early experience with low-pass filtered images facilitates visual category learning in a neural network model

Plos one

Omisa Jinsi

Margaret M Henderson

Michael J Tarr

2023/1/6

3d view prediction models of the dorsal visual stream

arXiv preprint arXiv:2309.01782

Gabriel Sarch

Hsiao-Yu Fish Tung

Aria Wang

Jacob Prince

Michael Tarr

2023/9/4

Quantifying the Roles of Visual, Linguistic, and Visual-Linguistic Complexity in Verb Acquisition

arXiv preprint arXiv:2304.02492

Yuchen Zhou

Michael J Tarr

Daniel Yurovsky

2023/4/5

My pet pig won't fly and I want a refund

Behavioral and Brain Sciences

Michael J Tarr

2023/1

A texture statistics encoding model reveals sensitivity to mid-level features across human visual cortex

Journal of Vision

Margaret Henderson

Michael Tarr

Leila Wehbe

2023/8/1

Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features

Journal of Vision

Margaret M Henderson

Michael J Tarr

Leila Wehbe

2023/4/3

Learning neural acoustic fields

Advances in Neural Information Processing Systems

Andrew Luo

Yilun Du

Michael Tarr

Josh Tenenbaum

Antonio Torralba

...

2022/12/6

Interpretable mid-level encoding models of human visual cortex reveal associations between feature and semantic tuning for natural scene images

Journal of Vision

Margaret Henderson

Michael Tarr

Leila Wehbe

2022/12/5

Improving the accuracy of single-trial fMRI response estimates using GLMsingle

Elife

Jacob S Prince

Ian Charest

Jan W Kurzawski

John A Pyles

Michael J Tarr

...

2022/11/29

See List of Professors in Michael J. Tarr University(Carnegie Mellon University)

Co-Authors

H-index: 146
gore j or gore jc

gore j or gore jc

Vanderbilt University

H-index: 105
Steven Pinker

Steven Pinker

Harvard University

H-index: 92
Marlene Behrmann

Marlene Behrmann

Carnegie Mellon University

H-index: 90
Bruno Rossion

Bruno Rossion

Université de Lorraine

H-index: 71
Isabel Gauthier

Isabel Gauthier

Vanderbilt University

H-index: 63
James W. Tanaka

James W. Tanaka

University of Victoria

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