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Eye Direction Detection and Perception as Premises of a Social Brain: A Narrative Review of Behavioral and Neural Data. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 22:1-20. [PMID: 34642895 DOI: 10.3758/s13415-021-00959-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/23/2021] [Indexed: 11/08/2022]
Abstract
The eyes and the gaze are important stimuli for social interaction in humans. Impaired recognition of facial identity, facial emotions, and inference of the intentions of others may result from difficulties in extracting information relevant to the eye region, mainly the direction of gaze. Therefore, a review of these data is of interest. Behavioral data demonstrating the importance of the eye region and how humans respond to gaze direction are reviewed narratively, and several theoretical models on how visual information on gaze is processed are discussed to propose a unified hypothesis. Several issues that have not yet been investigated are identified. The authors tentatively suggest experiments that might help progress research in this area. The neural aspects are subsequently reviewed to best describe the low-level and higher-level visual information processing stages in the targeted subcortical and cortical areas. A specific neural network is proposed on the basis of the literature. Various gray areas, such as the temporality of the processing of visual information, the question of salience priority, and the coordination between the two hemispheres, remain unclear and require further investigations. Finally, disordered gaze direction detection mechanisms and their consequences on social cognition and behavior are discussed as key deficiencies in several conditions, such as autism spectrum disorder, 22q11.2 deletion, schizophrenia, and social anxiety disorder. This narrative review provides significant additional data showing that the detection and perception of someone's gaze is an essential part of the development of our social brain.
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Image luminance changes contrast sensitivity in visual cortex. Cell Rep 2021; 34:108692. [PMID: 33535047 PMCID: PMC7886026 DOI: 10.1016/j.celrep.2021.108692] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/16/2020] [Accepted: 01/04/2021] [Indexed: 12/21/2022] Open
Abstract
Accurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.
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Shirzhiyan Z, Keihani A, Farahi M, Shamsi E, GolMohammadi M, Mahnam A, Haidari MR, Jafari AH. Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction. PLoS One 2019; 14:e0213197. [PMID: 30840671 PMCID: PMC6402685 DOI: 10.1371/journal.pone.0213197] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 02/16/2019] [Indexed: 11/19/2022] Open
Abstract
Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.
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Affiliation(s)
- Zahra Shirzhiyan
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmadreza Keihani
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Morteza Farahi
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Elham Shamsi
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Mina GolMohammadi
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
| | - Amin Mahnam
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohsen Reza Haidari
- Section of Neuroscience, Department of Neurology, Faculty of Medicine, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Medical Physics & Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran
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Dai J, Wang Y. Contrast coding in the primary visual cortex depends on temporal contexts. Eur J Neurosci 2018; 47:947-958. [DOI: 10.1111/ejn.13893] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 02/06/2018] [Accepted: 02/20/2018] [Indexed: 11/30/2022]
Affiliation(s)
- Ji Dai
- State Key Laboratory of Brain and Cognitive Sciences; Institute of Biophysics; Chinese Academy of Sciences; Beijing 100101 China
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science; CAS Center for Excellence in Brain Science and Intelligence Technology; the Brain Cognition and Brain Disease Institute; Shenzhen Institutes of Advanced Technology; Chinese Academy of Sciences; Shenzhen 518055 China
| | - Yi Wang
- State Key Laboratory of Brain and Cognitive Sciences; Institute of Biophysics; Chinese Academy of Sciences; Beijing 100101 China
- University of Chinese Academy of Sciences; Beijing 100101 China
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Wang Y, Wang Y. Neurons in primary visual cortex represent distribution of luminance. Physiol Rep 2016; 4:4/18/e12966. [PMID: 27655797 PMCID: PMC5037916 DOI: 10.14814/phy2.12966] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 08/21/2016] [Indexed: 11/24/2022] Open
Abstract
To efficiently detect a wide range of light-intensity changes, visual neurons must adapt to ambient luminance. However, how neurons in the primary visual cortex (V1) code the distribution of luminance remains unknown. We designed stimuli that represent rapid changes in luminance under different luminance distributions and investigated V1 neuron responses to these novel stimuli. We demonstrate that V1 neurons represent luminance changes by dynamically adjusting their responses when the luminance distribution changes. Many cells (35%) detected luminance changes by responding to dark stimuli when the distribution was dominated by bright stimuli, bright stimuli when dominated by dark stimuli, and both dark and bright stimuli when dominated by intermediate luminance stimuli; 13% of cells signaled the mean luminance that was varied with different distributions; the remaining 52% of cells gradually shifted the responses that were most sensitive to luminance changes when the luminance distribution varied. The remarkable response changes of the former two cell groups suggest their crucial roles in detecting luminance changes. These response characteristics demonstrate that V1 neurons are not only sensitive to luminance change, but also luminance distribution change. They encode luminance changes according to the luminance distribution. Mean cells represent the prevailing luminance and reversal cells represent the salient stimuli in the environment.
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Affiliation(s)
- Yong Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China
| | - Yi Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics Chinese Academy of Sciences, Beijing, China
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