1
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Prince JS, Alvarez GA, Konkle T. Contrastive learning explains the emergence and function of visual category-selective regions. SCIENCE ADVANCES 2024; 10:eadl1776. [PMID: 39321304 PMCID: PMC11423896 DOI: 10.1126/sciadv.adl1776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 08/21/2024] [Indexed: 09/27/2024]
Abstract
Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework-contrastive coding-based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Further, lesions of these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles in information processing. Finally, these pre-identified units can predict neural responses in all corresponding face-, scene-, body-, and word-selective regions of human visual cortex, under a highly constrained sparse positive encoding procedure. The success of this single model indicates that brain-like functional specialization can emerge without category-specific learning pressures, as the system learns to untangle rich image content. Contrastive coding, therefore, provides a unifying account of object category emergence and representation in the human brain.
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Affiliation(s)
- Jacob S Prince
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - George A Alvarez
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Talia Konkle
- Department of Psychology, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Kempner Institute for Biological and Artificial Intelligence, Harvard University, Cambridge, MA, USA
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2
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Doostani N, Hossein-Zadeh GA, Cichy RM, Vaziri-Pashkam M. Attention Modulates Human Visual Responses to Objects by Tuning Sharpening. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.01.543205. [PMID: 37333078 PMCID: PMC10274640 DOI: 10.1101/2023.06.01.543205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA. We demonstrated that the strength of the attentional bias towards the target is not fixed but decreases with increasing target-distractor similarity. Simulations provided evidence that this result pattern is explained by tuning sharpening rather than an increase in gain. Our findings provide a mechanistic explanation for behavioral effects of target-distractor similarity on attentional biases and suggest tuning sharpening as the underlying mechanism in object-based attention.
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3
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Contier O, Baker CI, Hebart MN. Distributed representations of behaviour-derived object dimensions in the human visual system. Nat Hum Behav 2024:10.1038/s41562-024-01980-y. [PMID: 39251723 DOI: 10.1038/s41562-024-01980-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 08/06/2024] [Indexed: 09/11/2024]
Abstract
Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioural goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgements directly onto the brain. Our results reveal broadly distributed representations of behaviourally relevant information, demonstrating selectivity to a wide variety of novel dimensions while capturing known selectivities for visual features and categories. Behaviour-derived dimensions were superior to categories at predicting brain responses, yielding mixed selectivity in much of visual cortex and sparse selectivity in category-selective clusters. This framework reconciles seemingly disparate findings regarding regional specialization, explaining category selectivity as a special case of sparse response profiles among representational dimensions, suggesting a more expansive view on visual processing in the human brain.
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Affiliation(s)
- Oliver Contier
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Max Planck School of Cognition, Leipzig, Germany.
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Martin N Hebart
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Medicine, Justus Liebig University Giessen, Giessen, Germany
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4
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Contier O, Baker CI, Hebart MN. Distributed representations of behavior-derived object dimensions in the human visual system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.23.553812. [PMID: 37662312 PMCID: PMC10473665 DOI: 10.1101/2023.08.23.553812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioral goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgments directly onto the brain. Our results reveal broadly distributed representations of behaviorally-relevant information, demonstrating selectivity to a wide variety of novel dimensions while capturing known selectivities for visual features and categories. Behavior-derived dimensions were superior to categories at predicting brain responses, yielding mixed selectivity in much of visual cortex and sparse selectivity in category-selective clusters. This framework reconciles seemingly disparate findings regarding regional specialization, explaining category selectivity as a special case of sparse response profiles among representational dimensions, suggesting a more expansive view on visual processing in the human brain.
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Affiliation(s)
- O Contier
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Max Planck School of Cognition, Leipzig, Germany
| | - C I Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda MD, USA
| | - M N Hebart
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Medicine, Justus Liebig University Giessen, Giessen, Germany
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5
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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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6
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Peelen MV, Downing PE. Testing cognitive theories with multivariate pattern analysis of neuroimaging data. Nat Hum Behav 2023; 7:1430-1441. [PMID: 37591984 PMCID: PMC7616245 DOI: 10.1038/s41562-023-01680-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 07/12/2023] [Indexed: 08/19/2023]
Abstract
Multivariate pattern analysis (MVPA) has emerged as a powerful method for the analysis of functional magnetic resonance imaging, electroencephalography and magnetoencephalography data. The new approaches to experimental design and hypothesis testing afforded by MVPA have made it possible to address theories that describe cognition at the functional level. Here we review a selection of studies that have used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the 'how' of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions.
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Affiliation(s)
- Marius V Peelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Paul E Downing
- Cognitive Neuroscience Institute, Department of Psychology, Bangor University, Bangor, UK.
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7
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Doshi FR, Konkle T. Cortical topographic motifs emerge in a self-organized map of object space. SCIENCE ADVANCES 2023; 9:eade8187. [PMID: 37343093 PMCID: PMC10284546 DOI: 10.1126/sciadv.ade8187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with a large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflect a smooth mapping of a unified representational space.
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Affiliation(s)
- Fenil R. Doshi
- Department of Psychology and Center for Brain Sciences, Harvard University, Cambridge, MA, USA
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8
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Yargholi E, Op de Beeck H. Category Trumps Shape as an Organizational Principle of Object Space in the Human Occipitotemporal Cortex. J Neurosci 2023; 43:2960-2972. [PMID: 36922027 PMCID: PMC10124953 DOI: 10.1523/jneurosci.2179-22.2023] [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: 11/24/2022] [Revised: 02/22/2023] [Accepted: 03/03/2023] [Indexed: 03/17/2023] Open
Abstract
The organizational principles of the object space represented in the human ventral visual cortex are debated. Here we contrast two prominent proposals that, in addition to an organization in terms of animacy, propose either a representation related to aspect ratio (stubby-spiky) or to the distinction between faces and bodies. We designed a critical test that dissociates the latter two categories from aspect ratio and investigated responses from human fMRI (of either sex) and deep neural networks (BigBiGAN). Representational similarity and decoding analyses showed that the object space in the occipitotemporal cortex and BigBiGAN was partially explained by animacy but not by aspect ratio. Data-driven approaches showed clusters for face and body stimuli and animate-inanimate separation in the representational space of occipitotemporal cortex and BigBiGAN, but no arrangement related to aspect ratio. In sum, the findings go in favor of a model in terms of an animacy representation combined with strong selectivity for faces and bodies.SIGNIFICANCE STATEMENT We contrasted animacy, aspect ratio, and face-body as principal dimensions characterizing object space in the occipitotemporal cortex. This is difficult to test, as typically faces and bodies differ in aspect ratio (faces are mostly stubby and bodies are mostly spiky). To dissociate the face-body distinction from the difference in aspect ratio, we created a new stimulus set in which faces and bodies have a similar and very wide distribution of values along the shape dimension of the aspect ratio. Brain imaging (fMRI) with this new stimulus set showed that, in addition to animacy, the object space is mainly organized by the face-body distinction and selectivity for aspect ratio is minor (despite its wide distribution).
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Affiliation(s)
- Elahe' Yargholi
- Department of Brain and Cognition, Leuven Brain Institute, Faculty of Psychology & Educational Sciences, KU Leuven, 3000 Leuven, Belgium
| | - Hans Op de Beeck
- Department of Brain and Cognition, Leuven Brain Institute, Faculty of Psychology & Educational Sciences, KU Leuven, 3000 Leuven, Belgium
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9
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Disentangling Object Category Representations Driven by Dynamic and Static Visual Input. J Neurosci 2023; 43:621-634. [PMID: 36639892 PMCID: PMC9888510 DOI: 10.1523/jneurosci.0371-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 10/01/2022] [Accepted: 10/06/2022] [Indexed: 12/12/2022] Open
Abstract
Humans can label and categorize objects in a visual scene with high accuracy and speed, a capacity well characterized with studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed by the brain in the absence of luminance-defined form information, we created a novel stimulus set of "object kinematograms" to isolate motion-defined signals from other sources of visual information. Object kinematograms were generated by extracting motion information from videos of 6 object categories and applying the motion to limited-lifetime random dot patterns. Using functional magnetic resonance imaging (fMRI) (n = 15, 40% women), we investigated whether category information from the object kinematograms could be decoded within the occipitotemporal and parietal cortex and evaluated whether the information overlapped with category responses to static images from the original videos. We decoded object category for both stimulus formats in all higher-order regions of interest (ROIs). More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition, while more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Further, decoding across the two stimulus formats was possible in all regions. These results demonstrate that motion cues can elicit widespread and robust category responses on par with those elicited by static luminance cues, even in ventral regions of visual cortex that have traditionally been associated with primarily image-defined form processing.SIGNIFICANCE STATEMENT Much research on visual object recognition has focused on recognizing objects in static images. However, motion is a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic motion cues. Our study shows that, while higher-order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.
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10
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Li J, Kean H, Fedorenko E, Saygin Z. Intact reading ability despite lacking a canonical visual word form area in an individual born without the left superior temporal lobe. Cogn Neuropsychol 2023; 39:249-275. [PMID: 36653302 DOI: 10.1080/02643294.2023.2164923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The visual word form area (VWFA), a region canonically located within left ventral temporal cortex (VTC), is specialized for orthography in literate adults presumbly due to its connectivity with frontotemporal language regions. But is a typical, left-lateralized language network critical for the VWFA's emergence? We investigated this question in an individual (EG) born without the left superior temporal lobe but who has normal reading ability. EG showed canonical typical face-selectivity bilateraly but no wordselectivity either in right VWFA or in the spared left VWFA. Moreover, in contrast with the idea that the VWFA is simply part of the language network, no part of EG's VTC showed selectivity to higher-level linguistic processing. Interestingly, EG's VWFA showed reliable multivariate patterns that distinguished words from other categories. These results suggest that a typical left-hemisphere language network is necessary for acanonical VWFA, and that orthographic processing can otherwise be supported by a distributed neural code.
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Affiliation(s)
- Jin Li
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Hope Kean
- Department of Brain and Cognitive Sciences / McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences / McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Zeynep Saygin
- Department of Psychology, The Ohio State University, Columbus, OH, USA
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11
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Kob L. Exploring the role of structuralist methodology in the neuroscience of consciousness: a defense and analysis. Neurosci Conscious 2023; 2023:niad011. [PMID: 37205986 PMCID: PMC10191193 DOI: 10.1093/nc/niad011] [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: 08/25/2022] [Revised: 02/27/2023] [Accepted: 04/13/2023] [Indexed: 05/21/2023] Open
Abstract
Traditional contrastive analysis has been the foundation of consciousness science, but its limitations due to the lack of a reliable method for measuring states of consciousness have prompted the exploration of alternative approaches. Structuralist theories have gained attention as an alternative that focuses on the structural properties of phenomenal experience and seeks to identify their neural encoding via structural similarities between quality spaces and neural state spaces. However, the intertwining of philosophical assumptions about structuralism and structuralist methodology may pose a challenge to those who are skeptical of the former. In this paper, I offer an analysis and defense of structuralism as a methodological approach in consciousness science, which is partly independent of structuralist assumptions on the nature of consciousness. By doing so, I aim to make structuralist methodology more accessible to a broader scientific and philosophical audience. I situate methodological structuralism in the context of questions concerning mental representation, psychophysical measurement, holism, and functional relevance of neural processes. At last, I analyze the relationship between the structural approach and the distinction between conscious and unconscious states.
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Affiliation(s)
- Lukas Kob
- *Corresponding author. Philosophy Department, Otto-von-Guericke University, Zschokkestraße 32, Magdeburg 39104, Germany. E-mail:
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12
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Janini D, Hamblin C, Deza A, Konkle T. General object-based features account for letter perception. PLoS Comput Biol 2022; 18:e1010522. [PMID: 36155642 PMCID: PMC9536565 DOI: 10.1371/journal.pcbi.1010522] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 10/06/2022] [Accepted: 08/29/2022] [Indexed: 11/30/2022] Open
Abstract
After years of experience, humans become experts at perceiving letters. Is this visual capacity attained by learning specialized letter features, or by reusing general visual features previously learned in service of object categorization? To explore this question, we first measured the perceptual similarity of letters in two behavioral tasks, visual search and letter categorization. Then, we trained deep convolutional neural networks on either 26-way letter categorization or 1000-way object categorization, as a way to operationalize possible specialized letter features and general object-based features, respectively. We found that the general object-based features more robustly correlated with the perceptual similarity of letters. We then operationalized additional forms of experience-dependent letter specialization by altering object-trained networks with varied forms of letter training; however, none of these forms of letter specialization improved the match to human behavior. Thus, our findings reveal that it is not necessary to appeal to specialized letter representations to account for perceptual similarity of letters. Instead, we argue that it is more likely that the perception of letters depends on domain-general visual features. For over a century, scientists have conducted behavioral experiments to investigate how the visual system recognizes letters, but it has proven difficult to propose a model of the feature space underlying this capacity. Here we leveraged recent advances in machine learning to model a wide variety of features ranging from specialized letter features to general object-based features. Across two large-scale behavioral experiments we find that general object-based features account well for letter perception, and that adding letter specialization did not improve the correspondence to human behavior. It is plausible that the ability to recognize letters largely relies on general visual features unaltered by letter learning.
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Affiliation(s)
- Daniel Janini
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
- * E-mail:
| | - Chris Hamblin
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Arturo Deza
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Talia Konkle
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
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13
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Nicholson DA, Prinz AA. Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study. J Vis 2022; 22:3. [PMID: 35675057 PMCID: PMC9187944 DOI: 10.1167/jov.22.7.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/04/2022] [Indexed: 11/24/2022] Open
Abstract
Visual search is a complex behavior influenced by many factors. To control for these factors, many studies use highly simplified stimuli. However, the statistics of these stimuli are very different from the statistics of the natural images that the human visual system is optimized by evolution and experience to perceive. Could this difference change search behavior? If so, simplified stimuli may contribute to effects typically attributed to cognitive processes, such as selective attention. Here we use deep neural networks to test how optimizing models for the statistics of one distribution of images constrains performance on a task using images from a different distribution. We train four deep neural network architectures on one of three source datasets-natural images, faces, and x-ray images-and then adapt them to a visual search task using simplified stimuli. This adaptation produces models that exhibit performance limitations similar to humans, whereas models trained on the search task alone exhibit no such limitations. However, we also find that deep neural networks trained to classify natural images exhibit similar limitations when adapted to a search task that uses a different set of natural images. Therefore, the distribution of data alone cannot explain this effect. We discuss how future work might integrate an optimization-based approach into existing models of visual search behavior.
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Affiliation(s)
- David A Nicholson
- Emory University, Department of Biology, O. Wayne Rollins Research Center, Atlanta, Georgia
| | - Astrid A Prinz
- Emory University, Department of Biology, O. Wayne Rollins Research Center, Atlanta, Georgia
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14
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Norman-Haignere SV, Feather J, Boebinger D, Brunner P, Ritaccio A, McDermott JH, Schalk G, Kanwisher N. A neural population selective for song in human auditory cortex. Curr Biol 2022; 32:1470-1484.e12. [PMID: 35196507 PMCID: PMC9092957 DOI: 10.1016/j.cub.2022.01.069] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 10/26/2021] [Accepted: 01/24/2022] [Indexed: 12/18/2022]
Abstract
How is music represented in the brain? While neuroimaging has revealed some spatial segregation between responses to music versus other sounds, little is known about the neural code for music itself. To address this question, we developed a method to infer canonical response components of human auditory cortex using intracranial responses to natural sounds, and further used the superior coverage of fMRI to map their spatial distribution. The inferred components replicated many prior findings, including distinct neural selectivity for speech and music, but also revealed a novel component that responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features, was located near speech- and music-selective responses, and was also evident in individual electrodes. These results suggest that representations of music are fractionated into subpopulations selective for different types of music, one of which is specialized for the analysis of song.
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Affiliation(s)
- Sam V Norman-Haignere
- Zuckerman Institute, Columbia University, New York, NY, USA; HHMI Fellow of the Life Sciences Research Foundation, Chevy Chase, MD, USA; Laboratoire des Sytèmes Perceptifs, Département d'Études Cognitives, ENS, PSL University, CNRS, Paris, France; Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, USA; Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA; Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Jenelle Feather
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Brains, Minds and Machines, Cambridge, MA, USA
| | - Dana Boebinger
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA; Program in Speech and Hearing Biosciences and Technology, Harvard University, Cambridge, MA, USA
| | - Peter Brunner
- Department of Neurology, Albany Medical College, Albany, NY, USA; National Center for Adaptive Neurotechnologies, Albany, NY, USA; Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Anthony Ritaccio
- Department of Neurology, Albany Medical College, Albany, NY, USA; Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Josh H McDermott
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Brains, Minds and Machines, Cambridge, MA, USA; Program in Speech and Hearing Biosciences and Technology, Harvard University, Cambridge, MA, USA
| | - Gerwin Schalk
- Department of Neurology, Albany Medical College, Albany, NY, USA
| | - Nancy Kanwisher
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Brains, Minds and Machines, Cambridge, MA, USA
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15
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Abstract
Categorization is the basis of thinking and reasoning. Through the analysis of infants’ gaze, we describe the trajectory through which visual object representations in infancy incrementally match categorical object representations as mapped onto adults’ visual cortex. Using a methodological approach that allows for a comparison of findings obtained with behavioral and brain measures in infants and adults, we identify the transition from visual exploration guided by perceptual salience to an organization of objects by categories, which begins with the animate–inanimate distinction in the first months of life and continues with a spurt of biologically relevant categories (human bodies, nonhuman bodies, nonhuman faces, small natural objects) through the second year of life. Humans make sense of the world by organizing things into categories. When and how does this process begin? We investigated whether real-world object categories that spontaneously emerge in the first months of life match categorical representations of objects in the human visual cortex. Using eye tracking, we measured the differential looking time of 4-, 10-, and 19-mo-olds as they looked at pairs of pictures belonging to eight animate or inanimate categories (human/nonhuman, faces/bodies, real-world size big/small, natural/artificial). Taking infants’ looking times as a measure of similarity, for each age group, we defined a representational space where each object was defined in relation to others of the same or of a different category. This space was compared with hypothesis-based and functional MRI-based models of visual object categorization in the adults’ visual cortex. Analyses across different age groups showed that, as infants grow older, their looking behavior matches neural representations in ever-larger portions of the adult visual cortex, suggesting progressive recruitment and integration of more and more feature spaces distributed over the visual cortex. Moreover, the results characterize infants’ visual categorization as an incremental process with two milestones. Between 4 and 10 mo, visual exploration guided by saliency gives way to an organization according to the animate–inanimate distinction. Between 10 and 19 mo, a category spurt leads toward a mature organization. We propose that these changes underlie the coupling between seeing and thinking in the developing mind.
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16
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Taylor J, Xu Y. Representation of Color, Form, and their Conjunction across the Human Ventral Visual Pathway. Neuroimage 2022; 251:118941. [PMID: 35122966 PMCID: PMC9014861 DOI: 10.1016/j.neuroimage.2022.118941] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/25/2022] [Indexed: 11/25/2022] Open
Abstract
Despite decades of research, our understanding of the relationship
between color and form processing in the primate ventral visual pathway remains
incomplete. Using fMRI multivoxel pattern analysis, we examined coding of color
and form, using a simple form feature (orientation) and a mid-level form feature
(curvature), in human ventral visual processing regions. We found that both
color and form could be decoded from activity in early visual areas V1 to V4, as
well as in the posterior color-selective region and shape-selective regions in
ventral and lateral occipitotemporal cortex defined based on their univariate
selectivity to color or shape, respectively (the central color region only
showed color but not form decoding). Meanwhile, decoding biases towards one
feature or the other existed in the color- and shape-selective regions,
consistent with their univariate feature selectivity reported in past studies.
Additional extensive analyses show that while all these regions contain
independent (linearly additive) coding for both features, several early visual
regions also encode the conjunction of color and the simple, but not the
complex, form feature in a nonlinear, interactive manner. Taken together, the
results show that color and form are encoded in a biased distributed and largely
independent manner across ventral visual regions in the human brain.
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Affiliation(s)
- JohnMark Taylor
- Visual Inference Laboratory, Zuckerman Institute, Columbia University.
| | - Yaoda Xu
- Department of Psychology, Yale University
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17
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Abstract
Face detection is a priority of both the human and primate visual system. However, occasionally we misperceive faces in inanimate objects -- "face pareidolia". A key feature of these 'false positives' is that face perception occurs in the absence of visual features typical of real faces. Human faces are known to be located faster than objects in visual search. Here we used a visual search paradigm to test whether illusory faces share this advantage. Search times were faster for illusory faces than for matched objects amongst both matched (Experiment 1) and diverse (Experiment 2) distractors, however search times for real human faces were faster and more efficient than objects with or without an illusory face. Importantly, this result indicates that illusory faces are processed quickly enough by the human brain to confer a visual search advantage, suggesting the engagement of a broadly-tuned mechanism that facilitates rapid face detection in cluttered environments.
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18
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Xu ZJ, Lleras A, Buetti S. Predicting how surface texture and shape combine in the human visual system to direct attention. Sci Rep 2021; 11:6170. [PMID: 33731840 PMCID: PMC7971056 DOI: 10.1038/s41598-021-85605-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 02/23/2021] [Indexed: 11/12/2022] Open
Abstract
Objects differ from one another along a multitude of visual features. The more distinct an object is from other objects in its surroundings, the easier it is to find it. However, it is still unknown how this distinctiveness advantage emerges in human vision. Here, we studied how visual distinctiveness signals along two feature dimensions—shape and surface texture—combine to determine the overall distinctiveness of an object in the scene. Distinctiveness scores between a target object and distractors were measured separately for shape and texture using a search task. These scores were then used to predict search times when a target differed from distractors along both shape and texture. Model comparison showed that the overall object distinctiveness was best predicted when shape and texture combined using a Euclidian metric, confirming the brain is computing independent distinctiveness scores for shape and texture and combining them to direct attention.
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Affiliation(s)
- Zoe Jing Xu
- University of Illinois, 603 E. Daniel St., Champaign, IL, 61820, USA.
| | - Alejandro Lleras
- University of Illinois, 603 E. Daniel St., Champaign, IL, 61820, USA
| | - Simona Buetti
- University of Illinois, 603 E. Daniel St., Champaign, IL, 61820, USA
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19
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Poncet M, Fabre‐Thorpe M, Chakravarthi R. A simple rule to describe interactions between visual categories. Eur J Neurosci 2020; 52:4639-4666. [DOI: 10.1111/ejn.14890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 06/12/2020] [Accepted: 06/24/2020] [Indexed: 11/27/2022]
Affiliation(s)
- Marlene Poncet
- CerCo Université de ToulouseCNRSUPS Toulouse France
- School of Psychology University of St Andrews St Andrews UK
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20
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21
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Gandolfo M, Downing PE. Asymmetric visual representation of sex from human body shape. Cognition 2020; 205:104436. [PMID: 32919115 DOI: 10.1016/j.cognition.2020.104436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 08/05/2020] [Accepted: 08/07/2020] [Indexed: 01/21/2023]
Abstract
We efficiently infer others' states and traits from their appearance, and these inferences powerfully shape our social behaviour. One key trait is sex, which is strongly cued by the appearance of the body. What are the visual representations that link body shape to sex? Previous studies of visual sex judgment tasks find observers have a bias to report "male", particularly for ambiguous stimuli. This finding implies a representational asymmetry - that for the processes that generate a sex percept, the default output is "male", and "female" is determined by the presence of additional perceptual evidence. That is, female body shapes are positively coded by reference to a male default shape. This perspective makes a novel prediction in line with Treisman's studies of visual search asymmetries: female body targets should be more readily detected amongst male distractors than vice versa. Across 10 experiments (N = 32 each) we confirmed this prediction and ruled out alternative low-level explanations. The asymmetry was found with profile and frontal body silhouettes, frontal photographs, and schematised icons. Low-level confounds were controlled by balancing silhouette images for size and homogeneity, and by matching physical properties of photographs. The female advantage was nulled for inverted icons, but intact for inverted photographs, suggesting reliance on distinct cues to sex for different body depictions. Together, these findings demonstrate a principle of the perceptual coding that links bodily appearance with a significant social trait: the female body shape is coded as an extension of a male default. We conclude by offering a visual experience account of how these asymmetric representations arise in the first place.
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22
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Long B, Moher M, Carey SE, Konkle T. Animacy and object size are reflected in perceptual similarity computations by the preschool years. VISUAL COGNITION 2019. [DOI: 10.1080/13506285.2019.1664689] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Bria Long
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Mariko Moher
- Department of Psychology, Williams College, Williamstown, MA, USA
| | - Susan E. Carey
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Talia Konkle
- Department of Psychology, Harvard University, Cambridge, MA, USA
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23
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Geng JJ, Witkowski P. Template-to-distractor distinctiveness regulates visual search efficiency. Curr Opin Psychol 2019; 29:119-125. [PMID: 30743200 PMCID: PMC6625942 DOI: 10.1016/j.copsyc.2019.01.003] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 12/13/2018] [Accepted: 01/04/2019] [Indexed: 11/18/2022]
Abstract
All models of attention include the concept of an attentional template (or a target or search template). The template is conceptualized as target information held in memory that is used for prioritizing sensory processing and determining if an object matches the target. It is frequently assumed that the template contains a veridical copy of the target. However, we review recent evidence showing that the template encodes a version of the target that is adapted to the current context (e.g. distractors, task, etc.); information held within the template may include only a subset of target features, real world knowledge, pre-existing perceptual biases, or even be a distorted version of the veridical target. We argue that the template contents are customized in order to maximize the ability to prioritize information that distinguishes targets from distractors. We refer to this as template-to-distractor distinctiveness and hypothesize that it contributes to visual search efficiency by exaggerating target-to-distractor dissimilarity.
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Affiliation(s)
- Joy J Geng
- Center for Mind and Brain, University of California Davis, Davis, CA, 95616, United States; Department of Psychology, University of California Davis, Davis, CA, 95616, United States.
| | - Phillip Witkowski
- Center for Mind and Brain, University of California Davis, Davis, CA, 95616, United States; Department of Psychology, University of California Davis, Davis, CA, 95616, United States
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24
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Yu CP, Liu H, Samaras D, Zelinsky GJ. Modelling attention control using a convolutional neural network designed after the ventral visual pathway. VISUAL COGNITION 2019. [DOI: 10.1080/13506285.2019.1661927] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Chen-Ping Yu
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Huidong Liu
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Dimitrios Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Gregory J. Zelinsky
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
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25
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The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks. J Neurosci 2019; 39:6513-6525. [PMID: 31196934 DOI: 10.1523/jneurosci.1714-18.2019] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 04/09/2019] [Accepted: 05/06/2019] [Indexed: 11/21/2022] Open
Abstract
Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here, we explore one such perceptual phenomenon, perceiving animacy, and use the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (what an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were better explained by object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, the appearance of an object interfered with proper object identification, such as failing to signal that a cow mug is a mug. The preference in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to represent objects when visual appearance is dissociated from animacy, probably due to a preferred processing of visual features typical of animate objects.SIGNIFICANCE STATEMENT How does the brain represent objects that we perceive around us? Recent advances in artificial intelligence have suggested that object categorization and its neural correlates have now been approximated by neural networks. Here, we show that neural networks can predict animacy according to human behavior but do not explain visual cortex representations. In ventral occipitotemporal cortex, neural activity patterns were strongly biased toward object appearance, to the extent that objects with visual features resembling animals were represented closely to real animals and separated from other objects from the same category. This organization that privileges animals and their features over objects might be the result of learning history and evolutionary constraints.
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26
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Kaiser D, Quek GL, Cichy RM, Peelen MV. Object Vision in a Structured World. Trends Cogn Sci 2019; 23:672-685. [PMID: 31147151 PMCID: PMC7612023 DOI: 10.1016/j.tics.2019.04.013] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/15/2019] [Accepted: 04/30/2019] [Indexed: 01/02/2023]
Abstract
In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world.
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Affiliation(s)
- Daniel Kaiser
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
| | - Genevieve L Quek
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Radoslaw M Cichy
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Marius V Peelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
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27
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Representational similarity precedes category selectivity in the developing ventral visual pathway. Neuroimage 2019; 197:565-574. [PMID: 31077844 DOI: 10.1016/j.neuroimage.2019.05.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 04/30/2019] [Accepted: 05/04/2019] [Indexed: 11/24/2022] Open
Abstract
Many studies have investigated the development of face-, scene-, and body-selective regions in the ventral visual pathway. This work has primarily focused on comparing the size and univariate selectivity of these neural regions in children versus adults. In contrast, very few studies have investigated the developmental trajectory of more distributed activation patterns within and across neural regions. Here, we scanned both children (ages 5-7) and adults to test the hypothesis that distributed representational patterns arise before category selectivity (for faces, bodies, or scenes) in the ventral pathway. Consistent with this hypothesis, we found mature representational patterns in several ventral pathway regions (e.g., FFA, PPA, etc.), even in children who showed no hint of univariate selectivity. These results suggest that representational patterns emerge first in each region, perhaps forming a scaffold upon which univariate category selectivity can subsequently develop. More generally, our findings demonstrate an important dissociation between category selectivity and distributed response patterns, and raise questions about the relative roles of each in development and adult cognition.
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28
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Zopf R, Schweinberger SR, Rich AN. Limits on visual awareness of object targets in the context of other object category masks: Investigating bottlenecks in the continuous flash suppression paradigm with hand and tool stimuli. J Vis 2019; 19:17. [PMID: 31100133 DOI: 10.1167/19.5.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The continuous flash suppression (CFS) task can be used to investigate what limits our capacity to become aware of visual stimuli. In this task, a stream of rapidly changing mask images to one eye initially suppresses awareness for a static target image presented to the other eye. Several factors may determine the breakthrough time from mask suppression, one of which is the overlap in representation of the target/mask categories in higher visual cortex. This hypothesis is based on certain object categories (e.g., faces) being more effective in blocking awareness of other categories (e.g., buildings) than other combinations (e.g., cars/chairs). Previous work found mask effectiveness to be correlated with category-pair high-level representational similarity. As the cortical representations of hands and tools overlap, these categories are ideal to test this further as well as to examine alternative explanations. For our CFS experiments, we predicted longer breakthrough times for hands/tools compared to other pairs due to the reported cortical overlap. In contrast, across three experiments, participants were generally faster at detecting targets masked by hands or tools compared to other mask categories. Exploring low-level explanations, we found that the category average for edges (e.g., hands have less detail compared to cars) was the best predictor for the data. This low-level bottleneck could not completely account for the specific category patterns and the hand/tool effects, suggesting there are several levels at which object category-specific limits occur. Given these findings, it is important that low-level bottlenecks for visual awareness are considered when testing higher-level hypotheses.
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Affiliation(s)
- Regine Zopf
- Perception in Action Research Centre & Department of Cognitive Science, Faculty of Human Sciences, Macquarie University, Sydney, Australia.,ARC Centre of Excellence in Cognition and its Disorders, Australia
| | - Stefan R Schweinberger
- ARC Centre of Excellence in Cognition and its Disorders, Australia.,Department of Psychology, Friedrich Schiller University, Jena, Germany
| | - Anina N Rich
- Perception in Action Research Centre & Department of Cognitive Science, Faculty of Human Sciences, Macquarie University, Sydney, Australia.,ARC Centre of Excellence in Cognition and its Disorders, Australia.,Centre for Elite Performance, Training & Expertise, Macquarie University, Sydney, Australia
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29
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Störmer VS, Cohen MA, Alvarez GA. Tuning Attention to Object Categories: Spatially Global Effects of Attention to Faces in Visual Processing. J Cogn Neurosci 2019; 31:937-947. [PMID: 30912729 DOI: 10.1162/jocn_a_01400] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Feature-based attention is known to enhance visual processing globally across the visual field, even at task-irrelevant locations. Here, we asked whether attention to object categories, in particular faces, shows similar location-independent tuning. Using EEG, we measured the face-selective N170 component of the EEG signal to examine neural responses to faces at task-irrelevant locations while participants attended to faces at another task-relevant location. Across two experiments, we found that visual processing of faces was amplified at task-irrelevant locations when participants attended to faces relative to when participants attended to either buildings or scrambled face parts. The fact that we see this enhancement with the N170 suggests that these attentional effects occur at the earliest stage of face processing. Two additional behavioral experiments showed that it is easier to attend to the same object category across the visual field relative to two distinct categories, consistent with object-based attention spreading globally. Together, these results suggest that attention to high-level object categories shows similar spatially global effects on visual processing as attention to simple, individual, low-level features.
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