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Kliger L, Yovel G. Distinct Yet Proximal Face- and Body-Selective Brain Regions Enable Clutter-Tolerant Representations of the Face, Body, and Whole Person. J Neurosci 2024; 44:e1871232024. [PMID: 38641406 PMCID: PMC11170945 DOI: 10.1523/jneurosci.1871-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 03/08/2024] [Accepted: 03/22/2024] [Indexed: 04/21/2024] Open
Abstract
Faces and bodies are processed in separate but adjacent regions in the primate visual cortex. Yet, the functional significance of dividing the whole person into areas dedicated to its face and body components and their neighboring locations remains unknown. Here we hypothesized that this separation and proximity together with a normalization mechanism generate clutter-tolerant representations of the face, body, and whole person when presented in complex multi-category scenes. To test this hypothesis, we conducted a fMRI study, presenting images of a person within a multi-category scene to human male and female participants and assessed the contribution of each component to the response to the scene. Our results revealed a clutter-tolerant representation of the whole person in areas selective for both faces and bodies, typically located at the border between the two category-selective regions. Regions exclusively selective for faces or bodies demonstrated clutter-tolerant representations of their preferred category, corroborating earlier findings. Thus, the adjacent locations of face- and body-selective areas enable a hardwired machinery for decluttering of the whole person, without the need for a dedicated population of person-selective neurons. This distinct yet proximal functional organization of category-selective brain regions enhances the representation of the socially significant whole person, along with its face and body components, within multi-category scenes.
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Affiliation(s)
- Libi Kliger
- The School of Psychological Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Galit Yovel
- The School of Psychological Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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2
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Yeh 葉律君 LC, Thorat S, Peelen MV. Predicting Cued and Oddball Visual Search Performance from fMRI, MEG, and DNN Neural Representational Similarity. J Neurosci 2024; 44:e1107232024. [PMID: 38331583 PMCID: PMC10957208 DOI: 10.1523/jneurosci.1107-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 12/13/2023] [Accepted: 01/10/2024] [Indexed: 02/10/2024] Open
Abstract
Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the questions of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and a deep neural network (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 ms after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed task-specific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing.
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Affiliation(s)
- Lu-Chun Yeh 葉律君
- Department of Mathematics and Computer Science, Physics, Geography, Mathematical Institute, Justus Liebig University Gießen, Gießen 35392, Germany
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 GD, The Netherlands
| | - Sushrut Thorat
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 GD, The Netherlands
- Institute of Cognitive Science, University of Osnabrück, Osnabrück 49090, Germany
| | - Marius V Peelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 GD, The Netherlands
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Nara S, Kaiser D. Integrative processing in artificial and biological vision predicts the perceived beauty of natural images. SCIENCE ADVANCES 2024; 10:eadi9294. [PMID: 38427730 PMCID: PMC10906925 DOI: 10.1126/sciadv.adi9294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 01/29/2024] [Indexed: 03/03/2024]
Abstract
Previous research shows that the beauty of natural images is already determined during perceptual analysis. However, it is unclear which perceptual computations give rise to the perception of beauty. Here, we tested whether perceived beauty is predicted by spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image parts into more efficient representations of the whole. We quantified integrative processing in an artificial deep neural network model, where the degree of integration was determined by the amount of deviation between activations for the whole image and its constituent parts. This quantification of integration predicted beauty ratings for natural images across four studies with different stimuli and designs. In a complementary functional magnetic resonance imaging study, we show that integrative processing in human visual cortex similarly predicts perceived beauty. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.
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Affiliation(s)
- Sanjeev Nara
- Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Gießen, Gießen Germany
| | - Daniel Kaiser
- Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Gießen, Gießen Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-University Marburg and Justus Liebig University Gießen, Marburg, Germany
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4
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Barany DA, Lacey S, Matthews KL, Nygaard LC, Sathian K. Neural basis of sound-symbolic pseudoword-shape correspondences. Neuropsychologia 2023; 188:108657. [PMID: 37543139 PMCID: PMC10529692 DOI: 10.1016/j.neuropsychologia.2023.108657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/23/2023] [Accepted: 08/02/2023] [Indexed: 08/07/2023]
Abstract
Non-arbitrary mapping between the sound of a word and its meaning, termed sound symbolism, is commonly studied through crossmodal correspondences between sounds and visual shapes, e.g., auditory pseudowords, like 'mohloh' and 'kehteh', are matched to rounded and pointed visual shapes, respectively. Here, we used functional magnetic resonance imaging (fMRI) during a crossmodal matching task to investigate the hypotheses that sound symbolism (1) involves language processing; (2) depends on multisensory integration; (3) reflects embodiment of speech in hand movements. These hypotheses lead to corresponding neuroanatomical predictions of crossmodal congruency effects in (1) the language network; (2) areas mediating multisensory processing, including visual and auditory cortex; (3) regions responsible for sensorimotor control of the hand and mouth. Right-handed participants (n = 22) encountered audiovisual stimuli comprising a simultaneously presented visual shape (rounded or pointed) and an auditory pseudoword ('mohloh' or 'kehteh') and indicated via a right-hand keypress whether the stimuli matched or not. Reaction times were faster for congruent than incongruent stimuli. Univariate analysis showed that activity was greater for the congruent compared to the incongruent condition in the left primary and association auditory cortex, and left anterior fusiform/parahippocampal gyri. Multivoxel pattern analysis revealed higher classification accuracy for the audiovisual stimuli when congruent than when incongruent, in the pars opercularis of the left inferior frontal (Broca's area), the left supramarginal, and the right mid-occipital gyri. These findings, considered in relation to the neuroanatomical predictions, support the first two hypotheses and suggest that sound symbolism involves both language processing and multisensory integration.
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Affiliation(s)
- Deborah A Barany
- Department of Kinesiology, University of Georgia and Augusta University/University of Georgia Medical Partnership, Athens, GA, 30602, USA
| | - Simon Lacey
- Department of Neurology, Penn State College of Medicine, Hershey, PA, 17033-0859, USA; Department of Neural & Behavioral Sciences, Penn State College of Medicine, Hershey, PA, 17033-0859, USA; Department of Psychology, Penn State College of Liberal Arts, University Park, PA, 16802, USA
| | - Kaitlyn L Matthews
- Department of Psychology, Emory University, Atlanta, GA, 30322, USA; Present address: Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Lynne C Nygaard
- Department of Psychology, Emory University, Atlanta, GA, 30322, USA
| | - K Sathian
- Department of Neurology, Penn State College of Medicine, Hershey, PA, 17033-0859, USA; Department of Neural & Behavioral Sciences, Penn State College of Medicine, Hershey, PA, 17033-0859, USA; Department of Psychology, Penn State College of Liberal Arts, University Park, PA, 16802, USA.
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Barany DA, Lacey S, Matthews KL, Nygaard LC, Sathian K. Neural Basis Of Sound-Symbolic Pseudoword-Shape Correspondences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.14.536865. [PMID: 37425853 PMCID: PMC10327042 DOI: 10.1101/2023.04.14.536865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Non-arbitrary mapping between the sound of a word and its meaning, termed sound symbolism, is commonly studied through crossmodal correspondences between sounds and visual shapes, e.g., auditory pseudowords, like 'mohloh' and 'kehteh', are matched to rounded and pointed visual shapes, respectively. Here, we used functional magnetic resonance imaging (fMRI) during a crossmodal matching task to investigate the hypotheses that sound symbolism (1) involves language processing; (2) depends on multisensory integration; (3) reflects embodiment of speech in hand movements. These hypotheses lead to corresponding neuroanatomical predictions of crossmodal congruency effects in (1) the language network; (2) areas mediating multisensory processing, including visual and auditory cortex; (3) regions responsible for sensorimotor control of the hand and mouth. Right-handed participants ( n = 22) encountered audiovisual stimuli comprising a simultaneously presented visual shape (rounded or pointed) and an auditory pseudoword ('mohloh' or 'kehteh') and indicated via a right-hand keypress whether the stimuli matched or not. Reaction times were faster for congruent than incongruent stimuli. Univariate analysis showed that activity was greater for the congruent compared to the incongruent condition in the left primary and association auditory cortex, and left anterior fusiform/parahippocampal gyri. Multivoxel pattern analysis revealed higher classification accuracy for the audiovisual stimuli when congruent than when incongruent, in the pars opercularis of the left inferior frontal (Broca's area), the left supramarginal, and the right mid-occipital gyri. These findings, considered in relation to the neuroanatomical predictions, support the first two hypotheses and suggest that sound symbolism involves both language processing and multisensory integration. HIGHLIGHTS fMRI investigation of sound-symbolic correspondences between auditory pseudowords and visual shapesFaster reaction times for congruent than incongruent audiovisual stimuliGreater activation in auditory and visual cortices for congruent stimuliHigher classification accuracy for congruent stimuli in language and visual areasSound symbolism involves language processing and multisensory integration.
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Affiliation(s)
- Deborah A. Barany
- Department of Kinesiology, University of Georgia and Augusta University/University of Georgia Medical Partnership, Athens, GA, 30602, USA
| | - Simon Lacey
- Department of Neurology, Penn State Colleges of Medicine and Liberal Arts, Hershey, PA 17033-0859, USA
- Department of Neural & Behavioral Sciences, Penn State Colleges of Medicine and Liberal Arts, Hershey, PA 17033-0859, USA
- Department of Psychology, Penn State Colleges of Medicine and Liberal Arts, Hershey, PA 17033-0859, USA
| | - Kaitlyn L. Matthews
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
- Present address: Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130
| | - Lynne C. Nygaard
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
| | - K. Sathian
- Department of Neurology, Penn State Colleges of Medicine and Liberal Arts, Hershey, PA 17033-0859, USA
- Department of Neural & Behavioral Sciences, Penn State Colleges of Medicine and Liberal Arts, Hershey, PA 17033-0859, USA
- Department of Psychology, Penn State Colleges of Medicine and Liberal Arts, Hershey, PA 17033-0859, USA
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Mocz V, Jeong SK, Chun M, Xu Y. Multiple visual objects are represented differently in the human brain and convolutional neural networks. Sci Rep 2023; 13:9088. [PMID: 37277406 DOI: 10.1038/s41598-023-36029-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 05/27/2023] [Indexed: 06/07/2023] Open
Abstract
Objects in the real world usually appear with other objects. To form object representations independent of whether or not other objects are encoded concurrently, in the primate brain, responses to an object pair are well approximated by the average responses to each constituent object shown alone. This is found at the single unit level in the slope of response amplitudes of macaque IT neurons to paired and single objects, and at the population level in fMRI voxel response patterns in human ventral object processing regions (e.g., LO). Here, we compare how the human brain and convolutional neural networks (CNNs) represent paired objects. In human LO, we show that averaging exists in both single fMRI voxels and voxel population responses. However, in the higher layers of five CNNs pretrained for object classification varying in architecture, depth and recurrent processing, slope distribution across units and, consequently, averaging at the population level both deviated significantly from the brain data. Object representations thus interact with each other in CNNs when objects are shown together and differ from when objects are shown individually. Such distortions could significantly limit CNNs' ability to generalize object representations formed in different contexts.
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Affiliation(s)
- Viola Mocz
- Visual Cognitive Neuroscience Lab, Department of Psychology, Yale University, 2 Hillhouse Ave, New Haven, CT, 06520, USA
| | - Su Keun Jeong
- Department of Psychology, Chungbuk National University, Cheongju, South Korea
| | - Marvin Chun
- Visual Cognitive Neuroscience Lab, Department of Psychology, Yale University, 2 Hillhouse Ave, New Haven, CT, 06520, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Yaoda Xu
- Visual Cognitive Neuroscience Lab, Department of Psychology, Yale University, 2 Hillhouse Ave, New Haven, CT, 06520, USA.
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Mocz V, Jeong SK, Chun M, Xu Y. Representing Multiple Visual Objects in the Human Brain and Convolutional Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530472. [PMID: 36909506 PMCID: PMC10002658 DOI: 10.1101/2023.02.28.530472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Objects in the real world often appear with other objects. To recover the identity of an object whether or not other objects are encoded concurrently, in primate object-processing regions, neural responses to an object pair have been shown to be well approximated by the average responses to each constituent object shown alone, indicating the whole is equal to the average of its parts. This is present at the single unit level in the slope of response amplitudes of macaque IT neurons to paired and single objects, and at the population level in response patterns of fMRI voxels in human ventral object processing regions (e.g., LO). Here we show that averaging exists in both single fMRI voxels and voxel population responses in human LO, with better averaging in single voxels leading to better averaging in fMRI response patterns, demonstrating a close correspondence of averaging at the fMRI unit and population levels. To understand if a similar averaging mechanism exists in convolutional neural networks (CNNs) pretrained for object classification, we examined five CNNs with varying architecture, depth and the presence/absence of recurrent processing. We observed averaging at the CNN unit level but rarely at the population level, with CNN unit response distribution in most cases did not resemble human LO or macaque IT responses. The whole is thus not equal to the average of its parts in CNNs, potentially rendering the individual objects in a pair less accessible in CNNs during visual processing than they are in the human brain.
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Affiliation(s)
- Viola Mocz
- Visual Cognitive Neuroscience Lab, Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Su Keun Jeong
- Department of Psychology, Chungbuk National University, South Korea
| | - Marvin Chun
- Visual Cognitive Neuroscience Lab, Department of Psychology, Yale University, New Haven, CT 06520, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA
| | - Yaoda Xu
- Visual Cognitive Neuroscience Lab, Department of Psychology, Yale University, New Haven, CT 06520, USA
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Zafirova Y, Cui D, Raman R, Vogels R. Keep the head in the right place: Face-body interactions in inferior temporal cortex. Neuroimage 2022; 264:119676. [PMID: 36216293 DOI: 10.1016/j.neuroimage.2022.119676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/23/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022] Open
Abstract
In primates, faces and bodies activate distinct regions in the inferior temporal (IT) cortex and are typically studied separately. Yet, primates interact with whole agents and not with random concatenations of faces and bodies. Despite its social importance, it is still poorly understood how faces and bodies interact in IT. Here, we addressed this gap by measuring fMRI activations to whole agents and to unnatural face-body configurations in which the head was mislocated with respect to the body, and examined how these relate to the sum of the activations to their corresponding faces and bodies. First, we mapped patches in the IT of awake macaques that were activated more by images of whole monkeys compared to objects and found that these mostly overlapped with body and face patches. In a second fMRI experiment, we obtained no evidence for superadditive responses in these "monkey patches", with the activation to the monkeys being less or equal to the summed face-body activations. However, monkey patches in the anterior IT were activated more by natural compared to unnatural configurations. The stronger activations to natural configurations could not be explained by the summed face-body activations. These univariate results were supported by regression analyses in which we modeled the activations to both configurations as a weighted linear combination of the activations to the faces and bodies, showing higher regression coefficients for the natural compared to the unnatural configurations. Deeper layers of trained convolutional neural networks also contained units that responded more to natural compared to unnatural monkey configurations. Unlike the monkey fMRI patches, these units showed substantial superadditive responses to the natural configurations. Our monkey fMRI data suggest configuration-sensitive face-body interactions in anterior IT, adding to the evidence for an integrated face-body processing in the primate ventral visual stream, and open the way for mechanistic studies using single unit recordings in these patches.
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Affiliation(s)
- Yordanka Zafirova
- Laboratorium voor Neuro- en Psychofysiologie, Department of Neurosciences, KU Leuven, Belgium; Leuven Brain Institute, KU Leuven, Belgium
| | - Ding Cui
- Laboratorium voor Neuro- en Psychofysiologie, Department of Neurosciences, KU Leuven, Belgium; Leuven Brain Institute, KU Leuven, Belgium
| | - Rajani Raman
- Laboratorium voor Neuro- en Psychofysiologie, Department of Neurosciences, KU Leuven, Belgium; Leuven Brain Institute, KU Leuven, Belgium
| | - Rufin Vogels
- Laboratorium voor Neuro- en Psychofysiologie, Department of Neurosciences, KU Leuven, Belgium; Leuven Brain Institute, KU Leuven, Belgium.
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Li L, Smith DM. Corrigendum: Neural Efficiency in Athletes: A Systematic Review. Front Behav Neurosci 2022; 16:841772. [PMID: 35221949 PMCID: PMC8874807 DOI: 10.3389/fnbeh.2022.841772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
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One object, two networks? Assessing the relationship between the face and body-selective regions in the primate visual system. Brain Struct Funct 2021; 227:1423-1438. [PMID: 34792643 DOI: 10.1007/s00429-021-02420-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022]
Abstract
Faces and bodies are often treated as distinct categories that are processed separately by face- and body-selective brain regions in the primate visual system. These regions occupy distinct regions of visual cortex and are often thought to constitute independent functional networks. Yet faces and bodies are part of the same object and their presence inevitably covary in naturalistic settings. Here, we re-evaluate both the evidence supporting the independent processing of faces and bodies and the organizational principles that have been invoked to explain this distinction. We outline four hypotheses ranging from completely separate networks to a single network supporting the perception of whole people or animals. The current evidence, especially in humans, is compatible with all of these hypotheses, making it presently unclear how the representation of faces and bodies is organized in the cortex.
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Abstract
During natural vision, our brains are constantly exposed to complex, but regularly structured environments. Real-world scenes are defined by typical part-whole relationships, where the meaning of the whole scene emerges from configurations of localized information present in individual parts of the scene. Such typical part-whole relationships suggest that information from individual scene parts is not processed independently, but that there are mutual influences between the parts and the whole during scene analysis. Here, we review recent research that used a straightforward, but effective approach to study such mutual influences: By dissecting scenes into multiple arbitrary pieces, these studies provide new insights into how the processing of whole scenes is shaped by their constituent parts and, conversely, how the processing of individual parts is determined by their role within the whole scene. We highlight three facets of this research: First, we discuss studies demonstrating that the spatial configuration of multiple scene parts has a profound impact on the neural processing of the whole scene. Second, we review work showing that cortical responses to individual scene parts are shaped by the context in which these parts typically appear within the environment. Third, we discuss studies demonstrating that missing scene parts are interpolated from the surrounding scene context. Bridging these findings, we argue that efficient scene processing relies on an active use of the scene's part-whole structure, where the visual brain matches scene inputs with internal models of what the world should look like.
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Affiliation(s)
- Daniel Kaiser
- Justus-Liebig-Universität Gießen, Germany.,Philipps-Universität Marburg, Germany.,University of York, United Kingdom
| | - Radoslaw M Cichy
- Freie Universität Berlin, Germany.,Humboldt-Universität zu Berlin, Germany.,Bernstein Centre for Computational Neuroscience Berlin, Germany
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Li L, Smith DM. Neural Efficiency in Athletes: A Systematic Review. Front Behav Neurosci 2021; 15:698555. [PMID: 34421553 PMCID: PMC8374331 DOI: 10.3389/fnbeh.2021.698555] [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: 04/21/2021] [Accepted: 07/09/2021] [Indexed: 11/24/2022] Open
Abstract
According to the neural efficiency hypothesis (NEH), professionals have more effective cortical functions in cognitive tasks. This study is focusing on providing a systematic review of sport-related NEH studies with functional neuroimaging or brain stimulation while performing a sport-specific task, with the aim to answer the question: How does long-term specialized training change an athlete's brain and improve efficiency? A total of 28 studies (N = 829, Experimental Group n = 430) from 2001 to 2020 (Median = 2014, SD = 5.43) were analyzed and results were organized into four different sections: expert-novice samples, perceptual-cognitive tasks and neuroimaging technologies, efficiency paradox, and the cluster analysis. Researchers examined a wide range of sport-specific videos and multiple object tracking (MOT) specific to 18 different sports and utilized blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalogram (EEG). Expert-novice comparisons were often adopted into investigations about the variations in general about optimal-controlled performance, neurophysiology, and behavioral brain research. Experts tended to perform at faster speeds, more accurate motor behavior, and with greater efficiency than novices. Experts report lower activity levels in the sensory and motor cortex with less energy expenditure, experts will possibly be more productive. These findings generally supported the NEH across the studies reviewed. However, an efficiency paradox and proficient brain functioning were revealed as the complementary hypothesis of the NEH. The discussion concentrates on strengths and key limitations. The conclusion highlights additional concerns and recommendations for prospective researchers aiming to investigate a broader range of populations and sports.
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Affiliation(s)
- Longxi Li
- Department of Physical Education and Health Education, Springfield College, Springfield, MA, United States
| | - Daniel M Smith
- Department of Physical Education and Health Education, Springfield College, Springfield, MA, United States
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