George A. Alvarez

About

George A. Alvarez is the Fred Kavli Professor of Neuroscience in the Department of Psychology at Harvard University. His research focuses on understanding how the human visual system manages its limited resources to efficiently process and interpret visual information. His projects explore key areas such as attentional selection, memory storage, fluid resource allocation, and efficient coding, aiming to uncover how the mind and brain optimize their use of limited cognitive resources. Based at William James Hall in Cambridge, MA, Alvarez's work contributes to the broader understanding of cognition, brain function, and behavior, particularly in the context of visual perception and cognition. His research seeks to elucidate the strategies employed by the human visual system to navigate complex environments and social situations with apparent ease.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Psychology
• Engineering
• Biology
• Neuroscience
• Philosophy
• Mathematics
• Environmental ethics

Selected publications

• A self-supervised domain-general learning framework for human ventral stream representation

  Nature Communications · 2022 · 136 citations

  Senior authorCorresponding

  Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinct…

  DOI

• A large-scale examination of inductive biases shaping high-level visual representation in brains and machines

  Nature Communications · 2024-10-30 · 74 citations

  articleOpen access

  The rapid release of high-performing computer vision models offers new potential to study the impact of different inductive biases on the emergent brain alignment of learned representations. Here, we perform controlled comparisons among a curated set of 224 diverse models to test the impact of specific model properties on visual brain predictivity - a process requiring over 1.8 billion regressions and 50.3 thousand representational similarity analyses. We find that models with qualitatively diff…

  PublisherOA PDFDOI

• What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?

  bioRxiv (Cold Spring Harbor Laboratory) · 2022 · 68 citations

  Abstract The rapid development and open-source release of highly performant computer vision models offers new potential for examining how different inductive biases impact representation learning and emergent alignment with the high-level human ventral visual system. Here, we assess a diverse set of 224 models, curated to enable controlled comparison of different model properties, testing their brain predictivity using large-scale functional magnetic resonance imaging data. We find that models w…

  DOI

• Doubting Driverless Dilemmas

  Perspectives on Psychological Science · 2020 · 45 citations

  Senior authorCorresponding

  The alarm has been raised on so-called driverless dilemmas, in which autonomous vehicles will need to make high-stakes ethical decisions on the road. We argue that these arguments are too contrived to be of practical use, are an inappropriate method for making decisions on issues of safety, and should not be used to inform engineering or policy.

  DOI

• Contrastive learning explains the emergence and function of visual category-selective regions

  Science Advances · 2024-09-25 · 37 citations

  articleOpen access

  Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework-contrastive coding-based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Furt…

  PublisherDOI

Recent grants

• NIH Grant R03MH086743

  NIH · $168k · 2012

• NIH Grant F31MH069095

  NIH · $35k · 2006

• COMPCOG: Intuitive Physics without Intuition or Physics: Leveraging Deep Neural Networks to Model Human Physical Reasoning

  NSF · $554k · 2020–2025

Frequent coauthors

• Talia Konkle

  Harvard University

  65 shared

• Timothy F. Brady

  42 shared

• Daryl Fougnie

  New York University

  37 shared

• Jeremy M. Wolfe

  Brigham and Women's Hospital

  34 shared

• Patrick Cavanagh

  York University

  23 shared

• Sarah Cormiea

  University of Pennsylvania

  21 shared

• Aude Oliva

  Massachusetts Institute of Technology

  20 shared

• Jordan W. Suchow

  20 shared

Similar researchers at Harvard University

• Randy L. Bucknerh-268
• Daniel L. Schacterh-181
• Matthew K. Nockh-126