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George A. Alvarez

George A. Alvarez

D-Index & Metrics

Psychology

D-Index
59
Citations
17074
World Ranking
3658
National Ranking
2054

Overview

George A. Alvarez is affiliated with Harvard University in the United States and conducts research primarily in the fields of Neuroscience and Computer Science. Their work has a significant focus on Cognitive Neuroscience and Computer Vision and Pattern Recognition, with additional contributions to Artificial Intelligence, Biophysics, and Social Psychology.

The scientist's research encompasses a range of topics including:

  • Visual Attention and Saliency Detection
  • Face Recognition and Perception
  • Visual perception and processing mechanisms
  • Neural dynamics and brain function
  • Domain Adaptation and Few-Shot Learning
  • Neural and Behavioral Psychology Studies
  • Medical Image Segmentation Techniques

Alvarez has contributed extensively to academic literature with notable recent publications such as:

  • "A self-supervised domain-general learning framework for human ventral stream representation" (2022, Nature Communications)
  • "What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?" (2022, bioRxiv - Cold Spring Harbor Laboratory)
  • "A large-scale examination of inductive biases shaping high-level visual representation in brains and machines" (2024, Nature Communications)
  • "Doubting Driverless Dilemmas" (2020, Perspectives on Psychological Science)
  • "Contrastive learning explains the emergence and function of visual category-selective regions" (2024, Science Advances)

Their publications appear frequently in venues including:

  • Journal of Vision
  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Nature Communications
  • Science Advances

Collaboration is a significant aspect of Alvarez's scholarly activity. Frequent co-authors include:

  • Talia Konkle
  • Jacob S. Prince
  • Colin Conwell
  • Fenil R. Doshi
  • C. Hamblin

Alvarez's interdisciplinary approach bridges neuroscience and computational methods, contributing to understanding the neural mechanisms underlying visual perception and advancing machine learning models inspired by brain function.

Best Publications

  • The capacity of visual short-term memory is set both by visual information load and by number of objects

    George Alvarez;Patrick Cavanagh

  • Visual long-term memory has a massive storage capacity for object details

    Timothy F. Brady;Talia Konkle;George A. Alvarez;Aude Oliva

  • Tracking multiple targets with multifocal attention.

    Patrick Cavanagh;George A. Alvarez

  • A review of visual memory capacity: Beyond individual items and toward structured representations.

    Timothy F. Brady;Talia Konkle;George A. Alvarez

  • Representing multiple objects as an ensemble enhances visual cognition.

    George A. Alvarez

  • How many objects can you track? Evidence for a resource-limited attentive tracking mechanism.

    George A. Alvarez;Steven L. Franconeri

  • Conceptual distinctiveness supports detailed visual long-term memory for real-world objects.

    Talia Konkle;Timothy F. Brady;George A. Alvarez;Aude Oliva

  • Hierarchical Encoding in Visual Working Memory Ensemble Statistics Bias Memory for Individual Items

    Timothy F. Brady;George A. Alvarez

  • Independent Resources for Attentional Tracking in the Left and Right Visual Hemifields

    George A. Alvarez;Patrick Cavanagh

  • The Representation of Simple Ensemble Visual Features Outside the Focus of Attention

    George A. Alvarez;Aude Oliva

  • Scene Memory Is More Detailed Than You Think The Role of Categories in Visual Long-Term Memory

    Talia Konkle;Timothy F. Brady;George A. Alvarez;Aude Oliva

  • Compression in Visual Working Memory: Using Statistical Regularities to Form More Efficient Memory Representations.

    Timothy F. Brady;Talia Konkle;George A. Alvarez

  • Flexible cognitive resources: Competitive content maps for attention and memory

    Steven L. Franconeri;George A. Alvarez;Patrick Cavanagh

  • Object features fail independently in visual working memory: evidence for a probabilistic feature-store model.

    Daryl Fougnie;George A. Alvarez

  • Variability in the quality of visual working memory

    Daryl Fougnie;Jordan W. Suchow;George A. Alvarez

  • Modeling visual working memory with the MemToolbox.

    Jordan William Suchow;Timothy Francis Brady;Daryl Fougnie;George Angelo Alvarez

  • Spatial ensemble statistics are efficient codes that can be represented with reduced attention

    George A. Alvarez;Aude Oliva

  • Failure of working memory training to enhance cognition or intelligence.

    Todd W. Thompson;Michael L. Waskom;Michael L. Waskom;Keri-Lee Alyson Garel;Carlos Cardenas-Iniguez;Carlos Cardenas-Iniguez

  • Working memory is not fixed-capacity: More active storage capacity for real-world objects than for simple stimuli.

    Timothy F. Brady;Viola S. Störmer;George A. Alvarez

  • Visual Long-Term Memory Has the Same Limit on Fidelity as Visual Working Memory

    Timothy Francis Brady;Talia A Konkle;Jonathan Gill;Aude Oliva

Frequent Co-Authors

Patrick Cavanagh
Patrick Cavanagh York University
Jeremy M. Wolfe
Jeremy M. Wolfe Brigham and Women's Hospital
Steven Franconeri
Steven Franconeri Northwestern University
Ken Nakayama
Ken Nakayama Harvard University
Thomas A. Carlson
Thomas A. Carlson University of Sydney
Todd S. Horowitz
Todd S. Horowitz National Institutes of Health
Yuhong V. Jiang
Yuhong V. Jiang University of Minnesota
Michael C. Frank
Michael C. Frank Stanford University
Brian J. Scholl
Brian J. Scholl Yale University

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