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D-Index & Metrics

Computer Science

D-Index
41
Citations
21279
World Ranking
8564
National Ranking
3660

Overview

Robert A. Jacobs is affiliated with the University of Rochester in the United States. Their research primarily spans the fields of Neuroscience and Computer Science, with a focus on various specialized subfields such as Cognitive Neuroscience, Artificial Intelligence, Computer Vision and Pattern Recognition, Geometry and Topology, and Social Psychology.

Their work emphasizes several core topics including visual perception and processing mechanisms, neural and behavioral psychology studies, neural dynamics and brain function, visual attention and saliency detection, morphological variations and asymmetry, face recognition and perception, as well as gaze tracking and assistive technology.

Among the recent published papers are:

  • Efficient data compression in perception and perceptual memory, 2020, Psychological Review
  • Semantic influence on visual working memory of object identity and location, 2021, Cognition
  • Can machine learning account for human visual object shape similarity judgments?, 2020, Vision Research
  • Analogy-Related Information Can Be Accessed by Simple Addition and Subtraction of fMRI Activation Patterns, Without Participants Performing any Analogy Task, 2021, Neurobiology of Language
  • Optimal attentional allocation in the presence of capacity constraints in uncued and cued visual search, 2021, Journal of Vision

Frequent co-authors in Jacobs' research include Christopher Bates, Joseph German, Ruoyang Hu, Meng-Huan Wu, and Rajeev D. S. Raizada. This collaboration spans multiple papers reflecting ongoing work within related domains.

Their research outputs have appeared in notable venues such as Behavioral and Brain Sciences, Psychological Review, Cognition, Vision Research, and Neurobiology of Language. These venues indicate a multidisciplinary approach combining experimental, theoretical, and computational methods in studying cognition and brain function.

Best Publications

  • Adaptive mixtures of local experts

    Robert A. Jacobs;Michael I. Jordan;Steven J. Nowlan;Geoffrey E. Hinton

  • Hierarchical mixtures of experts and the EM algorithm

    Michael I. Jordan;Robert A. Jacobs

  • Increased Rates of Convergence Through Learning Rate Adaptation

    Robert A. Jacobs

  • Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks

    Robert A. Jacobs;Michael I. Jordan;Andrew G. Barto

  • Bayesian integration of visual and auditory signals for spatial localization

    Peter W. Battaglia;Robert A. Jacobs;Richard N. Aslin

  • Methods for combining experts' probability assessments

    Robert A. Jacobs

  • Perception of speech reflects optimal use of probabilistic speech cues

    Meghan Clayards;Michael K. Tanenhaus;Richard N. Aslin;Robert A. Jacobs

  • Optimal integration of texture and motion cues to depth.

    Robert A. Jacobs

  • Comparing perceptual learning tasks: a review.

    Ione Fine;Robert A. Jacobs

  • Learning piecewise control strategies in a modular neural network architecture

    R.A. Jacobs;M.I. Jordan

  • Hierarchies of adaptive experts

    Michael I. Jordan;Robert A. Jacobs

  • What determines visual cue reliability

    Robert A. Jacobs

  • A competitive modular connectionist architecture

    Robert A. Jacobs;Michael I. Jordan

  • Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models with an Application to Speech Recognition

    Fengchun Peng;Robert A. Jacobs;Martin A. Tanner

  • An ideal observer analysis of visual working memory.

    Chris R. Sims;Robert A. Jacobs;David C. Knill

  • Learning to Control an Unstable System with Forward Modeling

    Michael I. Jordan;Robert A. Jacobs

  • Computational consequences of a bias toward short connections

    Robert A. Jacobs;Michael I. Jordan

  • Encoding Shape and Spatial Relations: The Role of -Receptive Field Size in Coordinating Complementary Representations

    Robert A. Jacobs;Stephen M. Kosslyn

  • Experience-dependent visual cue integration based on consistencies between visual and haptic percepts.

    Joseph E. Atkins;József Fiser;Robert A. Jacobs

  • Bayesian learning theory applied to human cognition

    Robert A. Jacobs;John K. Kruschke

Frequent Co-Authors

Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Richard N. Aslin
Richard N. Aslin Yale University
David C. Knill
David C. Knill University of Rochester
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
Andrew G. Barto
Andrew G. Barto University of Massachusetts Amherst
John A. Tarduno
John A. Tarduno University of Rochester
Stephen M. Kosslyn
Stephen M. Kosslyn Harvard University
Bradford Z. Mahon
Bradford Z. Mahon Carnegie Mellon University
Christopher F. Chabris
Christopher F. Chabris Geisinger Health System
Michael K. Tanenhaus
Michael K. Tanenhaus University of Rochester

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