1
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Ahn S, Adeli H, Zelinsky GJ. The attentive reconstruction of objects facilitates robust object recognition. PLoS Comput Biol 2024; 20:e1012159. [PMID: 38870125 PMCID: PMC11175536 DOI: 10.1371/journal.pcbi.1012159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 05/11/2024] [Indexed: 06/15/2024] Open
Abstract
Humans are extremely robust in our ability to perceive and recognize objects-we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward pass of processing through the ventral stream and less on the top-down feedback that likely underlies robust object perception and recognition. Aligned with the generative approach, we propose that the visual system actively facilitates recognition by reconstructing the object hypothesized to be in the image. Top-down attention then uses this reconstruction as a template to bias feedforward processing to align with the most plausible object hypothesis. Building on auto-encoder neural networks, our model makes detailed hypotheses about the appearance and location of the candidate objects in the image by reconstructing a complete object representation from potentially incomplete visual input due to noise and occlusion. The model then leverages the best object reconstruction, measured by reconstruction error, to direct the bottom-up process of selectively routing low-level features, a top-down biasing that captures a core function of attention. We evaluated our model using the MNIST-C (handwritten digits under corruptions) and ImageNet-C (real-world objects under corruptions) datasets. Not only did our model achieve superior performance on these challenging tasks designed to approximate real-world noise and occlusion viewing conditions, but also better accounted for human behavioral reaction times and error patterns than a standard feedforward Convolutional Neural Network. Our model suggests that a complete understanding of object perception and recognition requires integrating top-down and attention feedback, which we propose is an object reconstruction.
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Affiliation(s)
- Seoyoung Ahn
- Department of Molecular and Cell Biology, University of California, Berkeley, California, United States of America
| | - Hossein Adeli
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, New York, United States of America
| | - Gregory J. Zelinsky
- Department of Psychology, Stony Brook University, Stony Brook, New York, United States of America
- Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America
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2
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Vacher J, Launay C, Mamassian P, Coen-Cagli R. Measuring uncertainty in human visual segmentation. ARXIV 2023:arXiv:2301.07807v3. [PMID: 36824425 PMCID: PMC9949179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same-different judgments and perform model-based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures. We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.
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Affiliation(s)
- Jonathan Vacher
- Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, Paris, France
| | - Claire Launay
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Pascal Mamassian
- Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, Paris, France
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department. of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
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3
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Abstract
Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perception. In this article, we review evidence regarding current DNNs as adequate behavioral models of human core object recognition. To this end, we argue that it is important to distinguish between statistical tools and computational models and to understand model quality as a multidimensional concept in which clarity about modeling goals is key. Reviewing a large number of psychophysical and computational explorations of core object recognition performance in humans and DNNs, we argue that DNNs are highly valuable scientific tools but that, as of today, DNNs should only be regarded as promising-but not yet adequate-computational models of human core object recognition behavior. On the way, we dispel several myths surrounding DNNs in vision science.
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Affiliation(s)
- Felix A Wichmann
- Neural Information Processing Group, University of Tübingen, Tübingen, Germany;
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4
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Pan X, DeForge A, Schwartz O. Generalizing biological surround suppression based on center surround similarity via deep neural network models. PLoS Comput Biol 2023; 19:e1011486. [PMID: 37738258 PMCID: PMC10550176 DOI: 10.1371/journal.pcbi.1011486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 10/04/2023] [Accepted: 09/04/2023] [Indexed: 09/24/2023] Open
Abstract
Sensory perception is dramatically influenced by the context. Models of contextual neural surround effects in vision have mostly accounted for Primary Visual Cortex (V1) data, via nonlinear computations such as divisive normalization. However, surround effects are not well understood within a hierarchy, for neurons with more complex stimulus selectivity beyond V1. We utilized feedforward deep convolutional neural networks and developed a gradient-based technique to visualize the most suppressive and excitatory surround. We found that deep neural networks exhibited a key signature of surround effects in V1, highlighting center stimuli that visually stand out from the surround and suppressing responses when the surround stimulus is similar to the center. We found that in some neurons, especially in late layers, when the center stimulus was altered, the most suppressive surround surprisingly can follow the change. Through the visualization approach, we generalized previous understanding of surround effects to more complex stimuli, in ways that have not been revealed in visual cortices. In contrast, the suppression based on center surround similarity was not observed in an untrained network. We identified further successes and mismatches of the feedforward CNNs to the biology. Our results provide a testable hypothesis of surround effects in higher visual cortices, and the visualization approach could be adopted in future biological experimental designs.
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Affiliation(s)
- Xu Pan
- Department of Computer Science, University of Miami, Coral Gables, FL, United States of America
| | - Annie DeForge
- School of Information, University of California, Berkeley, CA, United States of America
- Bentley University, Waltham, MA, United States of America
| | - Odelia Schwartz
- Department of Computer Science, University of Miami, Coral Gables, FL, United States of America
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5
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Vacher J, Launay C, Mamassian P, Coen-Cagli R. Measuring uncertainty in human visual segmentation. PLoS Comput Biol 2023; 19:e1011483. [PMID: 37747914 PMCID: PMC10553811 DOI: 10.1371/journal.pcbi.1011483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 10/05/2023] [Accepted: 08/31/2023] [Indexed: 09/27/2023] Open
Abstract
Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same-different judgments and perform model-based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures. We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.
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Affiliation(s)
- Jonathan Vacher
- Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, Paris, France
| | - Claire Launay
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New-York, United States of America
| | - Pascal Mamassian
- Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, Paris, France
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New-York, United States of America
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New-York, United States of America
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New-York, United States of America
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6
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Doerig A, Sommers RP, Seeliger K, Richards B, Ismael J, Lindsay GW, Kording KP, Konkle T, van Gerven MAJ, Kriegeskorte N, Kietzmann TC. The neuroconnectionist research programme. Nat Rev Neurosci 2023:10.1038/s41583-023-00705-w. [PMID: 37253949 DOI: 10.1038/s41583-023-00705-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 06/01/2023]
Abstract
Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.
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Affiliation(s)
- Adrien Doerig
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany.
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
| | - Rowan P Sommers
- Department of Neurobiology of Language, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Katja Seeliger
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Blake Richards
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
- School of Computer Science, McGill University, Montréal, QC, Canada
- Mila, Montréal, QC, Canada
- Montréal Neurological Institute, Montréal, QC, Canada
- Learning in Machines and Brains Program, CIFAR, Toronto, ON, Canada
| | | | | | - Konrad P Kording
- Learning in Machines and Brains Program, CIFAR, Toronto, ON, Canada
- Bioengineering, Neuroscience, University of Pennsylvania, Pennsylvania, PA, USA
| | | | | | | | - Tim C Kietzmann
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
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7
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Baker N, Garrigan P, Phillips A, Kellman PJ. Configural relations in humans and deep convolutional neural networks. Front Artif Intell 2023; 5:961595. [PMID: 36937367 PMCID: PMC10014814 DOI: 10.3389/frai.2022.961595] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 12/23/2022] [Indexed: 03/05/2023] Open
Abstract
Deep convolutional neural networks (DCNNs) have attracted considerable interest as useful devices and as possible windows into understanding perception and cognition in biological systems. In earlier work, we showed that DCNNs differ dramatically from human perceivers in that they have no sensitivity to global object shape. Here, we investigated whether those findings are symptomatic of broader limitations of DCNNs regarding the use of relations. We tested learning and generalization of DCNNs (AlexNet and ResNet-50) for several relations involving objects. One involved classifying two shapes in an otherwise empty field as same or different. Another involved enclosure. Every display contained a closed figure among contour noise fragments and one dot; correct responding depended on whether the dot was inside or outside the figure. The third relation we tested involved a classification that depended on which of two polygons had more sides. One polygon always contained a dot, and correct classification of each display depended on whether the polygon with the dot had a greater number of sides. We used DCNNs that had been trained on the ImageNet database, and we used both restricted and unrestricted transfer learning (connection weights at all layers could change with training). For the same-different experiment, there was little restricted transfer learning (82.2%). Generalization tests showed near chance performance for new shapes. Results for enclosure were at chance for restricted transfer learning and somewhat better for unrestricted (74%). Generalization with two new kinds of shapes showed reduced but above-chance performance (≈66%). Follow-up studies indicated that the networks did not access the enclosure relation in their responses. For the relation of more or fewer sides of polygons, DCNNs showed successful learning with polygons having 3-5 sides under unrestricted transfer learning, but showed chance performance in generalization tests with polygons having 6-10 sides. Experiments with human observers showed learning from relatively few examples of all of the relations tested and complete generalization of relational learning to new stimuli. These results using several different relations suggest that DCNNs have crucial limitations that derive from their lack of computations involving abstraction and relational processing of the sort that are fundamental in human perception.
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Affiliation(s)
- Nicholas Baker
- Department of Psychology, Loyola University Chicago, Chicago, IL, United States
| | - Patrick Garrigan
- Department of Psychology, Saint Joseph's University, Philadelphia, PA, United States
| | - Austin Phillips
- UCLA Human Perception Laboratory, Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Philip J. Kellman
- UCLA Human Perception Laboratory, Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
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8
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Choung OH, Gordillo D, Roinishvili M, Brand A, Herzog MH, Chkonia E. Intact and deficient contextual processing in schizophrenia patients. Schizophr Res Cogn 2022; 30:100265. [PMID: 36119400 PMCID: PMC9477851 DOI: 10.1016/j.scog.2022.100265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 07/09/2022] [Accepted: 07/09/2022] [Indexed: 11/25/2022] Open
Abstract
Schizophrenia patients are known to have deficits in contextual vision. However, results are often very mixed. In some paradigms, patients do not take the context into account and, hence, perform more veridically than healthy controls. In other paradigms, context deteriorates performance much more strongly in patients compared to healthy controls. These mixed results may be explained by differences in the paradigms as well as by small or biased samples, given the large heterogeneity of patients' deficits. Here, we show that mixed results may also come from idiosyncrasies of the stimuli used because in variants of the same visual paradigm, tested with the same participants, we found intact and deficient processing.
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Affiliation(s)
- Oh-Hyeon Choung
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Corresponding author. http://lpsy.epfl.ch
| | - Dario Gordillo
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maya Roinishvili
- Laboratory of Vision Physiology, Ivane Beritashvili Centre of Experimental Biomedicine, Tbilisi, Georgia
- Institute of Cognitive Neurosciences, Free University of Tbilisi, Tbilisi, Georgia
| | - Andreas Brand
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael H. Herzog
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Eka Chkonia
- Department of Psychiatry, Tbilisi State Medical University, Tbilisi, Georgia
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9
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Herzog MH. The Irreducibility of Vision: Gestalt, Crowding and the Fundamentals of Vision. Vision (Basel) 2022; 6:vision6020035. [PMID: 35737422 PMCID: PMC9228288 DOI: 10.3390/vision6020035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/25/2022] [Accepted: 05/31/2022] [Indexed: 11/16/2022] Open
Abstract
What is fundamental in vision has been discussed for millennia. For philosophical realists and the physiological approach to vision, the objects of the outer world are truly given, and failures to perceive objects properly, such as in illusions, are just sporadic misperceptions. The goal is to replace the subjectivity of the mind by careful physiological analyses. Continental philosophy and the Gestaltists are rather skeptical or ignorant about external objects. The percepts themselves are their starting point, because it is hard to deny the truth of one own′s percepts. I will show that, whereas both approaches can well explain many visual phenomena with classic visual stimuli, they both have trouble when stimuli become slightly more complex. I suggest that these failures have a deeper conceptual reason, namely that their foundations (objects, percepts) do not hold true. I propose that only physical states exist in a mind independent manner and that everyday objects, such as bottles and trees, are perceived in a mind-dependent way. The fundamental processing units to process objects are extended windows of unconscious processing, followed by short, discrete conscious percepts.
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Affiliation(s)
- Michael H Herzog
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
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10
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Neri P. Deep networks may capture biological behavior for shallow, but not deep, empirical characterizations. Neural Netw 2022; 152:244-266. [PMID: 35567948 DOI: 10.1016/j.neunet.2022.04.023] [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: 08/29/2021] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 11/19/2022]
Abstract
We assess whether deep convolutional networks (DCN) can account for a most fundamental property of human vision: detection/discrimination of elementary image elements (bars) at different contrast levels. The human visual process can be characterized to varying degrees of "depth," ranging from percentage of correct detection to detailed tuning and operating characteristics of the underlying perceptual mechanism. We challenge deep networks with the same stimuli/tasks used with human observers and apply equivalent characterization of the stimulus-response coupling. In general, we find that popular DCN architectures do not account for signature properties of the human process. For shallow depth of characterization, some variants of network-architecture/training-protocol produce human-like trends; however, more articulate empirical descriptors expose glaring discrepancies. Networks can be coaxed into learning those richer descriptors by shadowing a human surrogate in the form of a tailored circuit perturbed by unstructured input, thus ruling out the possibility that human-model misalignment in standard protocols may be attributable to insufficient representational power. These results urge caution in assessing whether neural networks do or do not capture human behavior: ultimately, our ability to assess "success" in this area can only be as good as afforded by the depth of behavioral characterization against which the network is evaluated. We propose a novel set of metrics/protocols that impose stringent constraints on the evaluation of DCN behavior as an adequate approximation to biological processes.
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Affiliation(s)
- Peter Neri
- Laboratoire des Systèmes Perceptifs (UMR8248), École normale supérieure, PSL Research University, Paris, France.
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11
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Singer JJD, Seeliger K, Kietzmann TC, Hebart MN. From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction. J Vis 2022; 22:4. [PMID: 35129578 PMCID: PMC8822363 DOI: 10.1167/jov.22.2.4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network.
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Affiliation(s)
- Johannes J D Singer
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Psychology, Ludwig Maximilian University, Munich, Germany.,
| | - Katja Seeliger
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,
| | - Tim C Kietzmann
- Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands.,
| | - Martin N Hebart
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,
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12
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Herzog MH, Schurger A, Doerig A. First-person experience cannot rescue causal structure theories from the unfolding argument. Conscious Cogn 2022; 98:103261. [PMID: 35032833 DOI: 10.1016/j.concog.2021.103261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 10/29/2021] [Accepted: 12/07/2021] [Indexed: 11/03/2022]
Abstract
We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the critics agree that the main question in this debate is whether first-person experience, independent of third-person data, is a sufficient foundation for theories of consciousness. Here, we argue that pure first-person experience cannot be a scientific foundation for IIT because science relies on taking measurements, and pure first-person experience is not measurable except through reports, brain activity, and the relationship between them. We also argue that pure first-person experience cannot be taken as ground truth because science is about backing up theories with data, not about asserting that we have ground truth independent of data. Lastly, we explain why no experiment based on third-person data can test IIT as a theory of consciousness. IIT may be a good theory of something, but not of consciousness. We conclude by exposing a deeper reason for the above conclusions: IIT's consciousness is by construction fully dissociated from any measurable thing and, for this reason, IIT implies that both the level and content of consciousness are epiphenomenal, with no causal power. IIT and other causal structure theories end up in a form of dissociative epiphenomenalism, in which we cannot even trust reports about first-person experiences. But reports about first-person experiences are taken as ground truth and the foundation for IIT's axioms. Therefore, accepting IIT leads to rejecting its own axioms. We also respond to several other criticisms against the UA.
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Affiliation(s)
- Michael H Herzog
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale De Lausanne (EPFL), Lausanne, Switzerland
| | - Aaron Schurger
- Department of Psychology, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Irvine, CA, USA; INSERM, Cognitive Neuroimaging Unit, Gif sur Yvette 91191, France; Commissariat à l'Energie Atomique, Direction des Sciences du Vivant, I2BM, NeuroSpin, center, Gif sur Yvette 91191, France
| | - Adrien Doerig
- Donders Institute for Brain, Cognition & Behaviour, Nijmegen, Netherlands
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13
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Bornet A, Choung OH, Doerig A, Whitney D, Herzog MH, Manassi M. Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing. J Vis 2021; 21:10. [PMID: 34812839 PMCID: PMC8626847 DOI: 10.1167/jov.21.12.10] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 09/30/2021] [Indexed: 11/24/2022] Open
Abstract
In visual crowding, the perception of a target deteriorates in the presence of nearby flankers. Traditionally, target-flanker interactions have been considered as local, mostly deleterious, low-level, and feature specific, occurring when information is pooled along the visual processing hierarchy. Recently, a vast literature of high-level effects in crowding (grouping effects and face-holistic crowding in particular) led to a different understanding of crowding, as a global, complex, and multilevel phenomenon that cannot be captured or explained by simple pooling models. It was recently argued that these high-level effects may still be captured by more sophisticated pooling models, such as the Texture Tiling model (TTM). Unlike simple pooling models, the high-dimensional pooling stage of the TTM preserves rich information about a crowded stimulus and, in principle, this information may be sufficient to drive high-level and global aspects of crowding. In addition, it was proposed that grouping effects in crowding may be explained by post-perceptual target cueing. Here, we extensively tested the predictions of the TTM on the results of six different studies that highlighted high-level effects in crowding. Our results show that the TTM cannot explain any of these high-level effects, and that the behavior of the model is equivalent to a simple pooling model. In addition, we show that grouping effects in crowding cannot be predicted by post-perceptual factors, such as target cueing. Taken together, these results reinforce once more the idea that complex target-flanker interactions determine crowding and that crowding occurs at multiple levels of the visual hierarchy.
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Affiliation(s)
- Alban Bornet
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Oh-Hyeon Choung
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Adrien Doerig
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
| | - David Whitney
- Department of Psychology, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
- Vision Science Group, University of California, Berkeley, California, USA
| | - Michael H Herzog
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mauro Manassi
- School of Psychology, University of Aberdeen, King's College, Aberdeen, UK
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14
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Daube C, Xu T, Zhan J, Webb A, Ince RA, Garrod OG, Schyns PG. Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity. PATTERNS (NEW YORK, N.Y.) 2021; 2:100348. [PMID: 34693374 PMCID: PMC8515012 DOI: 10.1016/j.patter.2021.100348] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/30/2020] [Accepted: 08/20/2021] [Indexed: 01/24/2023]
Abstract
Deep neural networks (DNNs) can resolve real-world categorization tasks with apparent human-level performance. However, true equivalence of behavioral performance between humans and their DNN models requires that their internal mechanisms process equivalent features of the stimulus. To develop such feature equivalence, our methodology leveraged an interpretable and experimentally controlled generative model of the stimuli (realistic three-dimensional textured faces). Humans rated the similarity of randomly generated faces to four familiar identities. We predicted these similarity ratings from the activations of five DNNs trained with different optimization objectives. Using information theoretic redundancy, reverse correlation, and the testing of generalization gradients, we show that DNN predictions of human behavior improve because their shape and texture features overlap with those that subsume human behavior. Thus, we must equate the functional features that subsume the behavioral performances of the brain and its models before comparing where, when, and how these features are processed.
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Affiliation(s)
- Christoph Daube
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, Scotland, UK
| | - Tian Xu
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, England, UK
| | - Jiayu Zhan
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, Scotland, UK
| | - Andrew Webb
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, Scotland, UK
| | - Robin A.A. Ince
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, Scotland, UK
| | - Oliver G.B. Garrod
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, Scotland, UK
| | - Philippe G. Schyns
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, Scotland, UK
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15
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Lee RJ, Reuther J, Chakravarthi R, Martinovic J. Emergence of crowding: The role of contrast and orientation salience. J Vis 2021; 21:20. [PMID: 34709355 PMCID: PMC8556554 DOI: 10.1167/jov.21.11.20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 09/22/2021] [Indexed: 11/27/2022] Open
Abstract
Crowding causes difficulties in judging attributes of an object surrounded by other objects. We investigated crowding for stimuli that isolated either S-cone or luminance mechanisms or combined them. By targeting different retinogeniculate mechanisms with contrast-matched stimuli, we aim to determine the earliest site at which crowding emerges. Discrimination was measured in an orientation judgment task where Gabor targets were presented parafoveally among flankers. In the first experiment, we assessed flanked and unflanked orientation discrimination thresholds for pure S-cone and achromatic stimuli and their combinations. In the second experiment, to capture individual differences, we measured unflanked detection and orientation sensitivity, along with performance under flanker interference for stimuli containing luminance only or combined with S-cone contrast. We confirmed that orientation sensitivity was lower for unflanked S-cone stimuli. When flanked, the pattern of results for S-cone stimuli was the same as for achromatic stimuli with comparable (i.e. low) contrast levels. We also found that flanker interference exhibited a genuine signature of crowding only when orientation discrimination threshold was reliably surpassed. Crowding, therefore, emerges at a stage that operates on signals representing task-relevant featural (here, orientation) information. Because luminance and S-cone mechanisms have very different spatial tuning properties, it is most parsimonious to conclude that crowding takes place at a neural processing stage after they have been combined.
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Affiliation(s)
| | - Josephine Reuther
- School of Psychology, University of Aberdeen, Aberdeen, Scotland, UK
| | | | - Jasna Martinovic
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh & School of Psychology, University of Aberdeen, Aberdeen, Scotland, UK
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16
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Abstract
In crowding, perception of a target deteriorates in the presence of nearby flankers. Surprisingly, perception can be rescued from crowding if additional flankers are added (uncrowding). Uncrowding is a major challenge for all classic models of crowding and vision in general, because the global configuration of the entire stimulus is crucial. However, it is unclear which characteristics of the configuration impact (un)crowding. Here, we systematically dissected flanker configurations and showed that (un)crowding cannot be easily explained by the effects of the sub-parts or low-level features of the stimulus configuration. Our modeling results suggest that (un)crowding requires global processing. These results are well in line with previous studies showing the importance of global aspects in crowding.
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Affiliation(s)
- Oh-Hyeon Choung
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alban Bornet
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Adrien Doerig
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Michael H Herzog
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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17
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Lonnqvist B, Bornet A, Doerig A, Herzog MH. A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities. J Vis 2021; 21:17. [PMID: 34551062 PMCID: PMC8475290 DOI: 10.1167/jov.21.10.17] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 08/26/2021] [Indexed: 11/24/2022] Open
Abstract
Deep neural networks (DNNs) have revolutionized computer science and are now widely used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as neuroscientific models of the human visual system; the debate centers on to what extent certain shortcomings of DNNs are real failures and to what extent they are redeemable. Here, we argue that the main problem is that we often do not understand which human functions need to be modeled and, thus, what counts as a falsification. Hence, not only is there a problem on the DNN side, but there is also one on the brain side (i.e., with the explanandum-the thing to be explained). For example, should DNNs reproduce illusions? We posit that we can make better use of DNNs by adopting an approach of comparative biology by focusing on the differences, rather than the similarities, between DNNs and humans to improve our understanding of visual information processing in general.
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Affiliation(s)
- Ben Lonnqvist
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alban Bornet
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Adrien Doerig
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
| | - Michael H Herzog
- Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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18
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Peters B, Kriegeskorte N. Capturing the objects of vision with neural networks. Nat Hum Behav 2021; 5:1127-1144. [PMID: 34545237 DOI: 10.1038/s41562-021-01194-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 08/06/2021] [Indexed: 01/31/2023]
Abstract
Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked and predicted as we engage our surroundings. Object representations emancipate perception from the sensory input, enabling us to keep in mind that which is out of sight and to use perceptual content as a basis for action and symbolic cognition. Human behavioural studies have documented how object representations emerge through grouping, amodal completion, proto-objects and object files. By contrast, deep neural network models of visual object recognition remain largely tethered to sensory input, despite achieving human-level performance at labelling objects. Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature provides a starting point for the development of new experimental tasks that reveal mechanisms of human object perception and serve as benchmarks driving the development of deep neural network models that will put the object into object recognition.
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Affiliation(s)
- Benjamin Peters
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
| | - Nikolaus Kriegeskorte
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA. .,Department of Psychology, Columbia University, New York, NY, USA. .,Department of Neuroscience, Columbia University, New York, NY, USA. .,Department of Electrical Engineering, Columbia University, New York, NY, USA.
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19
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Unraveling brain interactions in vision: The example of crowding. Neuroimage 2021; 240:118390. [PMID: 34271157 DOI: 10.1016/j.neuroimage.2021.118390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 11/22/2022] Open
Abstract
Crowding, the impairment of target discrimination in clutter, is the standard situation in vision. Traditionally, crowding is explained with (feedforward) models, in which only neighboring elements interact, leading to a "bottleneck" at the earliest stages of vision. It is with this implicit prior that most functional magnetic resonance imaging (fMRI) studies approach the identification of the "neural locus" of crowding, searching for the earliest visual area in which the blood-oxygenation-level-dependent (BOLD) signal is suppressed under crowded conditions. Using this classic approach, we replicated previous findings of crowding-related BOLD suppression starting in V2 and increasing up the visual hierarchy. Surprisingly, under conditions of uncrowding, in which adding flankers improves performance, the BOLD signal was further suppressed. This suggests an important role for top-down connections, which is in line with global models of crowding. To discriminate between various possible models, we used dynamic causal modeling (DCM). We show that recurrent interactions between all visual areas, including higher-level areas like V4 and the lateral occipital complex (LOC), are crucial in crowding and uncrowding. Our results explain the discrepancies in previous findings: in a recurrent visual hierarchy, the crowding effect can theoretically be detected at any stage. Beyond crowding, we demonstrate the need for models like DCM to understand the complex recurrent processing which most likely underlies human perception in general.
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Bornet A, Doerig A, Herzog MH, Francis G, Van der Burg E. Shrinking Bouma's window: How to model crowding in dense displays. PLoS Comput Biol 2021; 17:e1009187. [PMID: 34228703 PMCID: PMC8284675 DOI: 10.1371/journal.pcbi.1009187] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 07/16/2021] [Accepted: 06/16/2021] [Indexed: 11/22/2022] Open
Abstract
In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma's law, only the target's nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model's outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.
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Affiliation(s)
- Alban Bornet
- Laboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Adrien Doerig
- Laboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Michael H. Herzog
- Laboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Gregory Francis
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Erik Van der Burg
- TNO, Human Factors, Soesterberg, The Netherlands
- Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
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21
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Abstract
The Ouroboros Model has been proposed as a biologically-inspired comprehensive cognitive architecture for general intelligence, comprising natural and artificial manifestations. The approach addresses very diverse fundamental desiderata of research in natural cognition and also artificial intelligence, AI. Here, it is described how the postulated structures have met with supportive evidence over recent years. The associated hypothesized processes could remedy pressing problems plaguing many, and even the most powerful current implementations of AI, including in particular deep neural networks. Some selected recent findings from very different fields are summoned, which illustrate the status and substantiate the proposal.
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22
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Baker N, Lu H, Erlikhman G, Kellman PJ. Local features and global shape information in object classification by deep convolutional neural networks. Vision Res 2020; 172:46-61. [PMID: 32413803 DOI: 10.1016/j.visres.2020.04.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 03/13/2020] [Accepted: 04/06/2020] [Indexed: 01/19/2023]
Abstract
Deep convolutional neural networks (DCNNs) show impressive similarities to the human visual system. Recent research, however, suggests that DCNNs have limitations in recognizing objects by their shape. We tested the hypothesis that DCNNs are sensitive to an object's local contour features but have no access to global shape information that predominates human object recognition. We employed transfer learning to assess local and global shape processing in trained networks. In Experiment 1, we used restricted and unrestricted transfer learning to retrain AlexNet, VGG-19, and ResNet-50 to classify circles and squares. We then probed these networks with stimuli with conflicting global shape and local contour information. We presented networks with overall square shapes comprised of curved elements and circles comprised of corner elements. Networks classified the test stimuli by local contour features rather than global shapes. In Experiment 2, we changed the training data to include circles and squares comprised of different elements so that the local contour features of the object were uninformative. This considerably increased the network's tendency to produce global shape responses, but deeper analyses in Experiment 3 revealed the network still showed no sensitivity to the spatial configuration of local elements. These findings demonstrate that DCNNs' performance is an inversion of human performance with respect to global and local shape processing. Whereas abstract relations of elements predominate in human perception of shape, DCNNs appear to extract only local contour fragments, with no representation of how they spatially relate to each other to form global shapes.
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Affiliation(s)
- Nicholas Baker
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.
| | - Hongjing Lu
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States; Department of Statistics, University of California, Los Angeles, Los Angeles, CA, United States
| | - Gennady Erlikhman
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Philip J Kellman
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
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