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We continue our study from [5], of how concepts that have hierarchical structure might be represented in brain-like neural networks, how these representations might be used to recognize the concepts, and how these representations might be learned. In [5], we considered simple tree-structured concepts and feed-forward layered networks. Here we extend the model in two ways: we allow limited overlap between children of different concepts, and we allow networks to include feedback edges. For these more general cases, we describe and analyze algorithms for recognition and algorithms for learning.
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Virtual Reality as a Context for Adaptation
The COVID-19 pandemic has accelerated interest in virtual reality (VR) for education, entertainment, telerehabilitation, and skills training. As the frequency and duration of VR engagement increases—the number of people in the United States using VR at least once per month is forecasted to exceed 95 million—it is critical to understand how VR engagement influences brain and behavior. Here, we evaluate neurophysiological effects of sensory conflicts induced by VR engagement and posit an intriguing hypothesis: the brain processes VR as a unique “context” leading to the formation and maintenance of independent sensorimotor representations. We discuss known VR-induced sensorimotor adaptations to illustrate how VR might manifest as a context for learning and how technological and human factors might mediate the context-dependency of sensorimotor representations learned in VR.
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- PAR ID:
- 10357673
- Date Published:
- Journal Name:
- Frontiers in Virtual Reality
- Volume:
- 2
- ISSN:
- 2673-4192
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3389/frvir.2021.733076
