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Showing 1–9 of 9 results
Advanced filters: Author: Nicholas B Turk-Browne Clear advanced filters

  • A manifold learning method called T-PHATE is developed for high-dimensional time-series data. T-PHATE is applied to brain data (functional magnetic resonance imaging) where it faithfully denoises signals and unveils latent brain-state trajectories which correspond with cognitive processing.

    • Erica L. Busch
    • Jessie Huang
    • Nicholas B. Turk-Browne
    Research
    Nature Computational Science
    Volume: 3, P: 240-253

  • In familiar environments, humans automatically anticipate the sensory consequences of their motor actions. Here, the authors show how action-based predictions arise from interactions between the hippocampus and visual cortex, and how these interactions strengthen and weaken over time.

    • Nicholas C. Hindy
    • Emily W. Avery
    • Nicholas B. Turk-Browne
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-13

  • Revealing how the human mind represents information is a longstanding goal of cognitive science. Here, the authors develop a method to reconstruct the mental representations of multiple visual concepts using behavioral judgments.

    • Laurent Caplette
    • Nicholas B. Turk-Browne
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-19

  • Using fMRI on awake infants could help us understand the contents of a baby’s mind, long before they can speak. Here, the authors report advances in how to collect, preprocess, and analyze task-based fMRI data from infants, and they share the resulting datasets and software.

    • C. T. Ellis
    • L. J. Skalaban
    • N. B. Turk-Browne
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-12

  • Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. Here the authors report behavioral and neuroimaging evidence that suggests that event representations can emerge even in the absence of such cues. They propose that this learning occurs in a manner analogous to the learning of semantic categories.

    • Anna C Schapiro
    • Timothy T Rogers
    • Matthew M Botvinick
    Research
    Nature Neuroscience
    Volume: 16, P: 486-492

  • Lapses of attention are commonplace, potentially because they are detected too late to be prevented. The authors use real-time fMRI to provide participants continuous access to their attentional state. Real-time feedback, particularly from frontoparietal cortex, improved sustained attention abilities and modified representations in visual cortex and basal ganglia.

    • Megan T deBettencourt
    • Jonathan D Cohen
    • Nicholas B Turk-Browne
    Research
    Nature Neuroscience
    Volume: 18, P: 470-475

  • Expectations about what will appear next guide perception. Using high-resolution fMRI and multivariate pattern analysis, the authors find that such predictive coding in early visual cortex could arise from pattern completion in hippocampal subfields. They show that these two processes are related and explore their behavioral significance and relative timing.

    • Nicholas C Hindy
    • Felicia Y Ng
    • Nicholas B Turk-Browne
    Research
    Nature Neuroscience
    Volume: 19, P: 665-667

  • A revolution is underway in cognitive neuroscience, where tools and techniques from computer science and the tech industry are helping to extract more meaningful cognitive signals from noisy and increasingly large fMRI datasets. In this paper, the authors review the cutting edge of such computational analyses and discuss future opportunities and challenges.

    • Jonathan D Cohen
    • Nathaniel Daw
    • Theodore L Willke
    Reviews
    Nature Neuroscience
    Volume: 20, P: 304-313