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Perception and memory have distinct spatial tuning properties in human visual cortex

Author

Listed:
  • Serra E. Favila

    (New York University
    Columbia University)

  • Brice A. Kuhl

    (University of Oregon
    Institute of Neuroscience, University of Oregon)

  • Jonathan Winawer

    (New York University
    New York University)

Abstract

Reactivation of earlier perceptual activity is thought to underlie long-term memory recall. Despite evidence for this view, it is unclear whether mnemonic activity exhibits the same tuning properties as feedforward perceptual activity. Here, we leverage population receptive field models to parameterize fMRI activity in human visual cortex during spatial memory retrieval. Though retinotopic organization is present during both perception and memory, large systematic differences in tuning are also evident. Whereas there is a three-fold decline in spatial precision from early to late visual areas during perception, this pattern is not observed during memory retrieval. This difference cannot be explained by reduced signal-to-noise or poor performance on memory trials. Instead, by simulating top-down activity in a network model of cortex, we demonstrate that this property is well explained by the hierarchical structure of the visual system. Together, modeling and empirical results suggest that computational constraints imposed by visual system architecture limit the fidelity of memory reactivation in sensory cortex.

Suggested Citation

  • Serra E. Favila & Brice A. Kuhl & Jonathan Winawer, 2022. "Perception and memory have distinct spatial tuning properties in human visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33161-8
    DOI: 10.1038/s41467-022-33161-8
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    References listed on IDEAS

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    3. Adam Steel & Madeleine M. Billings & Edward H. Silson & Caroline E. Robertson, 2021. "Publisher Correction: A network linking scene perception and spatial memory systems in posterior cerebral cortex," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
    4. Tomoyasu Horikawa & Yukiyasu Kamitani, 2017. "Generic decoding of seen and imagined objects using hierarchical visual features," Nature Communications, Nature, vol. 8(1), pages 1-15, August.
    5. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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