scholarly journals Conscious perception of natural images is constrained by category-related visual features

2019 ◽  
Author(s):  
Daniel Lindh ◽  
Ilja G. Sligte ◽  
Sara Assecondi ◽  
Kimron L. Shapiro ◽  
Ian Charest
Keyword(s):  
Visual System ◽  
Natural Images ◽  
Visual Features ◽  
Adaptive Behaviour ◽  
Conscious Perception ◽  
Activation Patterns ◽  
Visual Objects ◽  
High Level ◽  
The Brain ◽  
Insight Into

AbstractConscious perception is crucial for adaptive behaviour yet access to consciousness varies for different types of objects. The visual system comprises regions with widely distributed category information and exemplar-level representations that cluster according to category. Does this categorical organisation in the brain provide insight into object-specific access to consciousness? We address this question using the Attentional Blink (AB) approach with visual objects as targets. We find large differences across categories in the AB then employ activation patterns extracted from a deep convolutional neural network (DCNN) to reveal that these differences depend on mid- to high-level, rather than low-level, visual features. We further show that these visual features can be used to explain variance in performance across trials. Taken together, our results suggest that the specific organisation of the higher-tier visual system underlies important functions relevant for conscious perception of differing natural images.

scholarly journals Conscious perception of natural images is constrained by category-related visual features

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Daniel Lindh ◽  
Ilja G. Sligte ◽  
Sara Assecondi ◽  
Kimron L. Shapiro ◽  
Ian Charest
Keyword(s):  
Visual System ◽  
Attentional Blink ◽  
Natural Images ◽  
Visual Features ◽  
Adaptive Behaviour ◽  
Conscious Perception ◽  
Activation Patterns ◽  
Visual Objects ◽  
High Level ◽  
The Brain

Abstract Conscious perception is crucial for adaptive behaviour yet access to consciousness varies for different types of objects. The visual system comprises regions with widely distributed category information and exemplar-level representations that cluster according to category. Does this categorical organisation in the brain provide insight into object-specific access to consciousness? We address this question using the Attentional Blink approach with visual objects as targets. We find large differences across categories in the attentional blink. We then employ activation patterns extracted from a deep convolutional neural network to reveal that these differences depend on mid- to high-level, rather than low-level, visual features. We further show that these visual features can be used to explain variance in performance across trials. Taken together, our results suggest that the specific organisation of the higher-tier visual system underlies important functions relevant for conscious perception of differing natural images.

scholarly journals Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

2020 ◽  
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Visual Pathway ◽  
Functional Magnetic Resonance ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
High Level ◽  
Feedback Connections ◽  
The Brain ◽  
Insight Into

AbstractWhile vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

What the eye tells the brain: retinal feature extraction

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Klaudia P. Szatko ◽  
Katrin Franke
Keyword(s):  
Feature Extraction ◽  
Visual System ◽  
Visual Information ◽  
Functional Organization ◽  
Neural Mechanisms ◽  
Visual Features ◽  
Incoming Information ◽  
Motion Contrast ◽  
Parallel Feature ◽  
The Brain

Abstract To provide a compact and efficient input to the brain, sensory systems separate the incoming information into parallel feature channels. In the visual system, parallel processing starts in the retina. Here, the image is decomposed into multiple retinal output channels, each selective for a specific set of visual features like motion, contrast, or edges. In this article, we will summarize recent findings on the functional organization of the retinal output, the neural mechanisms underlying its diversity, and how single visual features, like color, are extracted by the retinal network. Unraveling how the retina – as the first stage of the visual system – filters the visual input is an important step toward understanding how visual information processing guides behavior.

The Use of Visual Statistical Features in Empirical Aesthetics

2020 ◽  
Author(s):  
Daniel Graham
Keyword(s):  
Visual System ◽  
Natural Environment ◽  
Natural Images ◽  
Natural Scenes ◽  
Statistical Features ◽  
Statistical Structure ◽  
Future Directions ◽  
Visual Aesthetics ◽  
Processing Strategies ◽  
The Brain

Evolution generally demands that the brain take advantage of the probable statistical structure in the natural environment. Much research in recent decades has confirmed that regular statistical features in natural scenes—especially low-level spatial regularities—can help explain processing strategies in the human visual system. Basic statistical features in various classes of human-created images broadly match those found in natural scenes. Such regularities can be seen as evolved constraints on the visual structure of aesthetic images and therefore human visual aesthetics. Some researchers have also attempted to find statistical features whose variation from natural images is associated with variations in preference and other aesthetic variables. There is evidence that variations in statistical features of luminance and color could be exploited by the visual system in certain situations. However, there is much ambiguity and variability in most reported relationships between variations in image statistical features and variations in measures of human aesthetics. In contrast, basic statistical constraints that align with efficient visual system processing are almost never violated in aesthetic images. Put simply, statistical features may constrain but may not explain variability in visual aesthetics. The chapter concludes with an outlook on future directions for research.

scholarly journals Finding decodable information that is read out in behaviour

2018 ◽  
Author(s):  
Tijl Grootswagers ◽  
Radoslaw M. Cichy ◽  
Thomas A. Carlson
Keyword(s):  
Temporal Cortex ◽  
Brain Activation ◽  
Object Perception ◽  
Neural Activation ◽  
Experimental Conditions ◽  
Activation Patterns ◽  
Visual Objects ◽  
Neuroimaging Data ◽  
Ventral Temporal Cortex ◽  
The Brain

AbstractMultivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

scholarly journals Characterizing the response to face pareidolia in human category-selective visual cortex

2017 ◽  
Author(s):  
Susan G Wardle ◽  
Kiley Seymour ◽  
Jessica Taubert
Keyword(s):  
Face Detection ◽  
Temporal Cortex ◽  
Visual Features ◽  
Activation Patterns ◽  
Lateral Occipital Complex ◽  
Face Area ◽  
Visual Areas ◽  
Ventral Pathway ◽  
High Level ◽  
Visual Properties

AbstractThe neural mechanisms underlying face and object recognition are understood to originate in ventral occipital-temporal cortex. A key feature of the functional architecture of the visual ventral pathway is its category-selectivity, yet it is unclear how category-selective regions process ambiguous visual input which violates category boundaries. One example is the spontaneous misperception of faces in inanimate objects such as the Man in the Moon, in which an object belongs to more than one category and face perception is divorced from its usual diagnostic visual features. We used fMRI to investigate the representation of illusory faces in category-selective regions. The perception of illusory faces was decodable from activation patterns in the fusiform face area (FFA) and lateral occipital complex (LOC), but not from other visual areas. Further, activity in FFA was strongly modulated by the perception of illusory faces, such that even objects with vastly different visual features were represented similarly if all images contained an illusory face. The results show that the FFA is broadly-tuned for face detection, not finely-tuned to the homogenous visual properties that typically distinguish faces from other objects. A complete understanding of high-level vision will require explanation of the mechanisms underlying natural errors of face detection.

scholarly journals Understanding the development of amblyopia using macaque monkey models

2019 ◽  
Vol 116 (52) ◽  
pp. 26217-26223 ◽  
Author(s):  
Lynne Kiorpes
Keyword(s):  
Macaque Monkey ◽  
Therapeutic Approaches ◽  
Ongoing Research ◽  
History Of ◽  
Visual Motor ◽  
Perceptual Abilities ◽  
High Level ◽  
And Function ◽  
The Brain ◽  
Insight Into

Amblyopia is a sensory developmental disorder affecting as many as 4% of children around the world. While clinically identified as a reduction in visual acuity and disrupted binocular function, amblyopia affects many low- and high-level perceptual abilities. Research with nonhuman primate models has provided much needed insight into the natural history of amblyopia, its origins and sensitive periods, and the brain mechanisms that underly this disorder. Amblyopia results from abnormal binocular visual experience and impacts the structure and function of the visual pathways beginning at the level of the primary visual cortex (V1). However, there are multiple instances of abnormalities in areas beyond V1 that are not simply inherited from earlier stages of processing. The full constellation of deficits must be taken into consideration in order to understand the broad impact of amblyopia on visual and visual–motor function. The data generated from studies of animal models of the most common forms of amblyopia have provided indispensable insight into the disorder, which has significantly impacted clinical practice. It is expected that this translational impact will continue as ongoing research into the neural correlates of amblyopia provides guidance for novel therapeutic approaches.

scholarly journals Towards Sentence-Level Brain Decoding with Distributed Representations

2019 ◽  
Vol 33 ◽  
pp. 7047-7054
Author(s):  
Jingyuan Sun ◽  
Shaonan Wang ◽  
Jiajun Zhang ◽  
Chengqing Zong
Keyword(s):  
Human Brain ◽  
Language Processing ◽  
Comprehensive Evaluation ◽  
Brain Activation ◽  
Systematic Evaluation ◽  
Activation Patterns ◽  
Distributed Representations ◽  
Brain Decoding ◽  
High Level ◽  
The Brain

Decoding human brain activities based on linguistic representations has been actively studied in recent years. However, most previous studies exclusively focus on word-level representations, and little is learned about decoding whole sentences from brain activation patterns. This work is our effort to mend the gap. In this paper, we build decoders to associate brain activities with sentence stimulus via distributed representations, the currently dominant sentence representation approach in natural language processing (NLP). We carry out a systematic evaluation, covering both widely-used baselines and state-of-the-art sentence representation models. We demonstrate how well different types of sentence representations decode the brain activation patterns and give empirical explanations of the performance difference. Moreover, to explore how sentences are neurally represented in the brain, we further compare the sentence representation’s correspondence to different brain areas associated with high-level cognitive functions. We find the supervised structured representation models most accurately probe the language atlas of human brain. To the best of our knowledge, this work is the first comprehensive evaluation of distributed sentence representations for brain decoding. We hope this work can contribute to decoding brain activities with NLP representation models, and understanding how linguistic items are neurally represented.

scholarly journals Leveraging prior concept learning improves ability to generalize from few examples in computational models of human object recognition

2020 ◽  
Author(s):  
Joshua S. Rule ◽  
Maximilian Riesenhuber
Keyword(s):  
Object Recognition ◽  
Concept Learning ◽  
Computational Models ◽  
Sparse Data ◽  
Visual Features ◽  
Software Models ◽  
Computational Work ◽  
Visual Concepts ◽  
High Level ◽  
The Brain

AbstractHumans quickly learn new visual concepts from sparse data, sometimes just a single example. Decades of prior work have established the hierarchical organization of the ventral visual stream as key to this ability. Computational work has shown that networks which hierarchically pool afferents across scales and positions can achieve human-like object recognition performance and predict human neural activity. Prior computational work has also reused previously acquired features to efficiently learn novel recognition tasks. These approaches, however, require magnitudes of order more examples than human learners and only reuse intermediate features at the object level or below. None has attempted to reuse extremely high-level visual features capturing entire visual concepts. We used a benchmark deep learning model of object recognition to show that leveraging prior learning at the concept level leads to vastly improved abilities to learn from few examples. These results suggest computational techniques for learning even more efficiently as well as neuroscientific experiments to better understand how the brain learns from sparse data. Most importantly, however, the model architecture provides a biologically plausible way to learn new visual concepts from a small number of examples, and makes several novel predictions regarding the neural bases of concept representations in the brain.Author summaryWe are motivated by the observation that people regularly learn new visual concepts from as little as one or two examples, far better than, e.g., current machine vision architectures. To understand the human visual system’s superior visual concept learning abilities, we used an approach inspired by computational models of object recognition which: 1) use deep neural networks to achieve human-like performance and predict human brain activity; and 2) reuse previous learning to efficiently master new visual concepts. These models, however, require many times more examples than human learners and, critically, reuse only low-level and intermediate information. None has attempted to reuse extremely high-level visual features (i.e., entire visual concepts). We used a neural network model of object recognition to show that reusing concept-level features leads to vastly improved abilities to learn from few examples. Our findings suggest techniques for future software models that could learn even more efficiently, as well as neuroscience experiments to better understand how people learn so quickly. Most importantly, however, our model provides a biologically plausible way to learn new visual concepts from a small number of examples.

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