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Michael Tarr -

Michael Tarr

Professor and Department Head

Michael Tarr is an expert in visual perception and how the brain transforms 2D images into high-level percepts.


Expertise

Topics:  Cognitive Neuroscience, Cognitive Science, Computational, Perception

Industries: Biotechnology

Michael Tarr is an expert in visual perception and how the brain transforms 2D images into high-level percepts. His work focuses on face, object and scene processing and recognition in both biological and artificial systems. Tarr studies the neural, cognitive and computational mechanisms underlying visual perception and cognition. He is interested in how humans effortlessly perceive, learn, remember and identify faces, scenes and objects, as well as how these visual processes interact with our other senses, thoughts and emotions. He also is interested in the connection between biological and artificial intelligence, in particular, focusing on how high-performing computer vision systems can be used to better understand human behavior and its neural basis. Conversely, he holds that effective models of biological vision will help inform and improve the performance of artificial vision systems.

Media Experience

CMU startup Neon honored by World Economic Forum  — The Business Journals
Neon was co-founded by Michael Tarr, head of the Psychology Department in CMU's Dietrich College of Humanities and Social Science, and Sophie Lebrecht, who received her postdoctoral training at CMU.

Education

Ph.D., Brain and Cognitive Sciences, Massachusetts Institute of Technology
B.S., Psychology, Cornell University

Spotlights

Accomplishments

Fellow, American Association for the Advancement of Science (AAAS (2017)

Links

Articles

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

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

Selectivity for food in human ventral visual cortex  —  Communications Biology

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

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

Photos

Videos