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Saccone EJ, Tian M, Bedny M. Developing cortex is functionally pluripotent: Evidence from blindness. Dev Cogn Neurosci 2024; 66:101360. [PMID: 38394708 PMCID: PMC10899073 DOI: 10.1016/j.dcn.2024.101360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/25/2024] [Accepted: 02/19/2024] [Indexed: 02/25/2024] Open
Abstract
How rigidly does innate architecture constrain function of developing cortex? What is the contribution of early experience? We review insights into these questions from visual cortex function in people born blind. In blindness, occipital cortices are active during auditory and tactile tasks. What 'cross-modal' plasticity tells us about cortical flexibility is debated. On the one hand, visual networks of blind people respond to higher cognitive information, such as sentence grammar, suggesting drastic repurposing. On the other, in line with 'metamodal' accounts, sighted and blind populations show shared domain preferences in ventral occipito-temporal cortex (vOTC), suggesting visual areas switch input modality but perform the same or similar perceptual functions (e.g., face recognition) in blindness. Here we bring these disparate literatures together, reviewing and synthesizing evidence that speaks to whether visual cortices have similar or different functions in blind and sighted people. Together, the evidence suggests that in blindness, visual cortices are incorporated into higher-cognitive (e.g., fronto-parietal) networks, which are a major source long-range input to the visual system. We propose the connectivity-constrained experience-dependent account. Functional development is constrained by innate anatomical connectivity, experience and behavioral needs. Infant cortex is pluripotent, the same anatomical constraints develop into different functional outcomes.
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Affiliation(s)
- Elizabeth J Saccone
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA.
| | - Mengyu Tian
- Center for Educational Science and Technology, Beijing Normal University at Zhuhai, China
| | - Marina Bedny
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
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Tang Z, Liu X, Huo H, Tang M, Qiao X, Chen D, Dong Y, Fan L, Wang J, Du X, Guo J, Tian S, Fan Y. Eye movement characteristics in a mental rotation task presented in virtual reality. Front Neurosci 2023; 17:1143006. [PMID: 37051147 PMCID: PMC10083294 DOI: 10.3389/fnins.2023.1143006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/13/2023] [Indexed: 03/28/2023] Open
Abstract
IntroductionEye-tracking technology provides a reliable and cost-effective approach to characterize mental representation according to specific patterns. Mental rotation tasks, referring to the mental representation and transformation of visual information, have been widely used to examine visuospatial ability. In these tasks, participants visually perceive three-dimensional (3D) objects and mentally rotate them until they identify whether the paired objects are identical or mirrored. In most studies, 3D objects are presented using two-dimensional (2D) images on a computer screen. Currently, visual neuroscience tends to investigate visual behavior responding to naturalistic stimuli rather than image stimuli. Virtual reality (VR) is an emerging technology used to provide naturalistic stimuli, allowing the investigation of behavioral features in an immersive environment similar to the real world. However, mental rotation tasks using 3D objects in immersive VR have been rarely reported.MethodsHere, we designed a VR mental rotation task using 3D stimuli presented in a head-mounted display (HMD). An eye tracker incorporated into the HMD was used to examine eye movement characteristics during the task synchronically. The stimuli were virtual paired objects oriented at specific angular disparities (0, 60, 120, and 180°). We recruited thirty-three participants who were required to determine whether the paired 3D objects were identical or mirrored.ResultsBehavioral results demonstrated that the response times when comparing mirrored objects were longer than identical objects. Eye-movement results showed that the percent fixation time, the number of within-object fixations, and the number of saccades for the mirrored objects were significantly lower than that for the identical objects, providing further explanations for the behavioral results.DiscussionIn the present work, we examined behavioral and eye movement characteristics during a VR mental rotation task using 3D stimuli. Significant differences were observed in response times and eye movement metrics between identical and mirrored objects. The eye movement data provided further explanation for the behavioral results in the VR mental rotation task.
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Affiliation(s)
- Zhili Tang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Xiaoyu Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
- *Correspondence: Xiaoyu Liu,
| | - Hongqiang Huo
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Min Tang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Xiaofeng Qiao
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Duo Chen
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Ying Dong
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Linyuan Fan
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Jinghui Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Xin Du
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Jieyi Guo
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Shan Tian
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
| | - Yubo Fan
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering and School of Engineering Medicine, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
- Yubo Fan,
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Mulders D, Seymour B, Mouraux A, Mancini F. Confidence of probabilistic predictions modulates the cortical response to pain. Proc Natl Acad Sci U S A 2023; 120:e2212252120. [PMID: 36669115 PMCID: PMC9942789 DOI: 10.1073/pnas.2212252120] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/21/2022] [Indexed: 01/21/2023] Open
Abstract
Pain typically evolves over time, and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the vertex potential: the more confident were participants about their predictions, the smaller the vertex potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning.
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Affiliation(s)
- Dounia Mulders
- Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, UK
- Institute of Neuroscience, UCLouvain, 1200 Woluwe-Saint-Lambert, Belgium
- Institute for Information and Communication Technologies, Electronics and Applied Mathematics, UCLouvain, 1348 Louvain-la-NeuveBelgium
- Department of Brain and Cognitive Sciences and McGovern Institute, Massachusetts Institute of Technology, MA02139
| | - Ben Seymour
- Wellcome Centre for Integrative Neuroimaging, John Radcliffe Hospital, Headington, OxfordOX3 9DU, UK
- Center for Information and Neural Networks (CiNet), Osaka565-0871, Japan
| | - André Mouraux
- Institute of Neuroscience, UCLouvain, 1200 Woluwe-Saint-Lambert, Belgium
| | - Flavia Mancini
- Computational and Biological Learning Unit, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, UK
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