Michael Tarr

Professor and Department Head Carnegie Mellon University

  • Pittsburgh PA

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

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Carnegie Mellon University

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Biography

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.

Areas of Expertise

Cognitive Neuroscience
Cognitive Science
Computational
Perception

Media Appearances

CMU startup Neon honored by World Economic Forum

The Business Journals  online

2015-08-05

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.

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Social

Industry Expertise

Biotechnology

Accomplishments

Fellow, American Association for the Advancement of Science (AAAS

2017

Education

Massachusetts Institute of Technology

Ph.D.

Brain and Cognitive Sciences

Cornell University

B.S.

Psychology

Articles

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

Journal of Vision

2023

Representations of visual and semantic information can overlap in human visual cortex, with the same neural populations exhibiting sensitivity to low-level features (orientation, spatial frequency, retinotopic position) and high-level semantic categories (faces, scenes). It has been hypothesized that this relationship between low-level visual and high-level category neural selectivity reflects natural scene statistics, such that neurons in a given category-selective region are tuned for low-level features or spatial positions that are diagnostic of the region's preferred category. To address the generality of this “natural scene statistics” hypothesis, as well as how well it can account for responses to complex naturalistic images across visual cortex, we performed two complementary analyses.

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

Perception

2023

Midlevel features, such as contour and texture, provide a computational link between low- and high-level visual representations. Although the nature of midlevel representations in the brain is not fully understood, past work has suggested a texture statistics model, called the P–S model , is a candidate for predicting neural responses in areas V1–V4 as well as human behavioral data. However, it is not currently known how well this model accounts for the responses of higher visual cortex to natural scene images. To examine this, we constructed single-voxel encoding models based on P–S statistics and fit the models to fMRI data from human subjects (both sexes) from the Natural Scenes Dataset.

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Selectivity for food in human ventral visual cortex

Communications Biology

2023

Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI “localizer” using grayscale food images.

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