What and where: a Bayesian inference theory of attention.

No abstract is available for this paper.

• Publication year

  2010

• Venue

  Vision Research

• Publication date

  2010-10-28

• Fields of study

  Medicine, Psychology

• Identifiers
  DOI 10.1016/j.visres.2010.05.013PMID 20493206
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Mechanisms underlying the representation of angles embedded within contour stimuli in area V2 of macaque monkeys

  2011influential reference
• Probabilistic Inference Using Markov Chain Monte Carlo Methods

  2011cited by this paper
• The normalization model of attention.

  2009cited by this paper
• Belief Propagation in Networks of Spiking Neurons

  2009cited by this paper
• VISION A Computational Investigation into the Human Representation and Processing of Visual Information

  2009cited by this paper
• Cortical Circuitry Implementing Graphical Models

  2009cited by this paper
• Attention capture by eye of origin singletons even without awareness--a hallmark of a bottom-up saliency map in the primary visual cortex.

  2008cited by this paper
• Bayesian Spiking Neurons I: Inference

  2008cited by this paper
• Graphical Models, Exponential Families, and Variational Inference

  2008cited by this paper
• How the brain might work: a hierarchical and temporal model for learning and recognition

  2008cited by this paper
• SUN: A Bayesian framework for saliency using natural statistics.

  2008cited by this paper
• Image interpretation by a single bottom-up top-down cycle

  2008cited by this paper
• LabelMe: A Database and Web-Based Tool for Image Annotation

  2008cited by this paper
• Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices

  2007influential reference
• A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms

  2007cited by this paper
• Learning multiple layers of representation.

  2007cited by this paper
• Bottom-up saliency is a discriminant process

  2007cited by this paper
• Computational neuroscience : theoretical insights into brain function

  2007cited by this paper
• Response to Comment on "Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices"

  2007cited by this paper
• Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning

  2007cited by this paper
• Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition

  2007cited by this paper
• Robust Object Recognition with Cortex-Like Mechanisms

  2007cited by this paper
• Guided Search 4.0: Current Progress With a Model of Visual Search

  2007influential reference
• Beyond bottom-up: Incorporating task-dependent influences into a computational model of spatial attention

  2007cited by this paper
• Exact Inferences in a Neural Implementation of a Hidden Markov Model

  2007cited by this paper
• An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed

  2006influential reference
• A theory of a saliency map in primary visual cortex (V1) tested by psychophysics of colour–orientation interference in texture segmentation

  2006cited by this paper
• Feature-based attention in visual cortex.

  2006cited by this paper
• Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search.

  2006influential reference
• Multiclass Object Recognition with Sparse, Localized Features

  2006cited by this paper
• Street Scenes: towards scene understanding in still images

  2006cited by this paper
• Spatial priors for part-based recognition using statistical models

  2005cited by this paper
• A hierarchical Bayesian model of invariant pattern recognition in the visual cortex

  2005cited by this paper
• Bayesian inference and attentional modulation in the visual cortex

  2005influential reference
• Saliency Based on Information Maximization

  2005influential reference
• A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex

  2005influential reference
• Feature congestion: a measure of display clutter

  2005influential reference
• Learning hierarchical models of scenes, objects, and parts

  2005cited by this paper
• Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4

  2005influential reference
• A Computational Model of the Cerebral Cortex

  2005cited by this paper
• Pictorial Structures for Object Recognition

  2004cited by this paper
• On the computational architecture of the neocortex

  2004cited by this paper
• Object perception as Bayesian inference.

  2004influential reference
• Bayesian Computation in Recurrent Neural Circuits

  2004influential reference
• A neurodynamical cortical model of visual attention and invariant object recognition.

  2004cited by this paper
• On the computational architecture of the neocortex

  2004cited by this paper
• Sharing features: efficient boosting procedures for multiclass object detection

  2004cited by this paper
• Inference, Attention, and Decision in a Bayesian Neural Architecture

  2004cited by this paper
• Fast Binary Feature Selection with Conditional Mutual Information

  2004cited by this paper
• Is bottom-up attention useful for object recognition?

  2004cited by this paper
• Modeling global scene factors in attention.

  2003influential reference
• Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes

  2003cited by this paper
• Contextual Priming for Object Detection

  2003influential reference
• Learning Optimized Features for Hierarchical Models of Invariant Object Recognition

  2003cited by this paper
• Hierarchical Bayesian inference in the visual cortex.

  2003cited by this paper
• Top-down control of visual attention in object detection

  2003cited by this paper
• Object class recognition by unsupervised scale-invariant learning

  2003cited by this paper
• Bayesian models of object perception.

  2003cited by this paper
• How Selective Are V1 Cells for Pop-Out Stimuli?

  2003cited by this paper
• An integrated network for invariant visual detection and recognition.

  2003cited by this paper
• Learning and inference in the brain

  2003cited by this paper
• Neuronal Activity in the Lateral Intraparietal Area and Spatial Attention

  2003cited by this paper
• Visual features of intermediate complexity and their use in classification

  2002cited by this paper
• Probabilistic Models of the Brain: Perception and Neural Function

  2002cited by this paper
• Motion illusions as optimal percepts

  2002cited by this paper
• Attentional modulation strength in cortical area MT depends on stimulus contrast.

  2002cited by this paper
• Ultra-Rapid Scene Categorization with a Wave of Spikes

  2002cited by this paper
• Shape representation in area V4: position-specific tuning for boundary conformation.

  2001cited by this paper
• Computational modelling of visual attention

  2001influential reference
• From Knowing What to Knowing Where: Modeling Object-Based Attention with Feedback Disinhibition of Activation

  2001cited by this paper
• A neural model combining attentional orienting to object recognition: preliminary explorations on the interplay between where and what

  2001cited by this paper
• Categorical representation of visual stimuli in the primate prefrontal cortex.

  2001cited by this paper
• Attention increases sensitivity of V4 neurons.

  2000cited by this paper
• Information processing with population codes

  2000cited by this paper
• Selectivity for Complex Shapes in Primate Visual Area V2

  2000cited by this paper
• Visual attention: Insights from brain imaging

  2000cited by this paper
• A Model of Invariant Object Recognition in the Visual System: Learning Rules, Activation Functions, Lateral Inhibition, and Information-Based Performance Measures

  2000cited by this paper
• Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4

  1999cited by this paper
• How does the cerebral cortex work? Learning, attention, and grouping by the laminar circuits of visual cortex.

  1999cited by this paper
• Space and attention in parietal cortex.

  1999influential reference
• Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4

  1999influential reference
• Feature-based attention influences motion processing gain in macaque visual cortex

  1999cited by this paper
• A simple saliency model predicts a number of motion popout phenomena.

  1999cited by this paper
• Statistical models and sensory attention

  1999cited by this paper
• Hierarchical models of object recognition in cortex

  1999cited by this paper
• A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

  1998cited by this paper
• Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model

  1998cited by this paper
• Visual attention mediated by biased competition in extrastriate visual cortex.

  1998cited by this paper
• SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition

  1997cited by this paper
• Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex

  1997cited by this paper
• Limited capacity of any realizable perceptual system is a sufficient reason for attentive behavior.

  1997cited by this paper
• Invariant face and object recognition in the visual system.

  1997cited by this paper
• Limited Capacity of Any Realizable Perceptual System Is a Sufficient Reason for Attentive Behavior

  1997cited by this paper
• Inferotemporal cortex and object vision.

  1996influential reference
• Visual object recognition.

  1996cited by this paper
• Markov chain Monte Carlo in Practice

  1996cited by this paper
• Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey.

  1996cited by this paper
• A Model of Invariant Object Recognition in the Visual System

  1996cited by this paper
• An Information-Maximization Approach to Blind Separation and Blind Deconvolution

  1995cited by this paper
• The Helmholtz Machine

  1995cited by this paper

• Attentional filtering: Dynamic system state estimation as an attention

  2025cites this paper
• What and where in electromagnetic brain imaging

  2025cites this paper
• Listeners' convergence towards an artificial agent in a joint phoneme categorization task

  2024cites this paper
• Moral thin-slicing: Forming moral impressions from a brief glance

  2024cites this paper
• Emergent human-like covert attention in feedforward convolutional neural networks.

  2024cites this paper
• How preferences enslave attention: calling into question the endogenous/exogenous dichotomy from an active inference perspective

  2024cites this paper
• Flexible Context-Driven Sensory Processing in Dynamical Vision Models

  2024cites this paper
• Top-Down Priors Disambiguate Target and Distractor Features in Simulated Covert Visual Search

  2024influential citation
• Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

  2024cites this paper
• Robust Tracking via Combing Top-Down and Bottom-Up Attention

  2024cites this paper
• Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

  2024cites this paper
• Improved Tomato Leaf Disease Recognition Based on the YOLOv5m with Various Soft Attention Module Combinations

  2024cites this paper
• Sequential Effects in Reaching Reveal Efficient Coding in Motor Planning

  2024cites this paper
• A-pruning: a lightweight pineapple flower counting network based on filter pruning

  2023cites this paper
• Top-Down Visual Attention from Analysis by Synthesis

  2023influential citation
• The role of the anatomical back-projections

  2023cites this paper
• An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease

  2022cites this paper
• Moral Thin-Slicing: How Snap Judgments Affect Online Sharing of Moral Content

  2022cites this paper
• Emotional Attention: From Eye Tracking to Computational Modeling

  2022cites this paper
• Efficient coding theory of dynamic attentional modulation

  2022cites this paper
• Impact of Attention on Perception in Cognitive Dynamic Systems

  2021cites this paper
• Understanding top-down attention using task-oriented ablation design

  2021cites this paper
• Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism

  2021cites this paper
• The Bayesian Brain: An Evolutionary Approach to Cognition

  2021cites this paper
• Scene memory and spatial inhibition in visual search

  2020cites this paper
• The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks

  2020cites this paper
• Spatiotemporal Saliency Detection Based on Maximum Consistency Superpixels Merging for Video Analysis

  2020cites this paper
• Attention in Psychology, Neuroscience, and Machine Learning

  2020cites this paper
• On Gaze Deployment to Audio-Visual Cues of Social Interactions

  2020influential citation
• A Real-time Trademark Detection Method

  2020cites this paper
• Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

  2020cites this paper
• Probabilistic Memory Model for Visual Images Categorization

  2020cites this paper
• Gaze-based Autism Detection for Adolescents and Young Adults using Prosaic Videos

  2020cites this paper
• Proto-Object Based Saliency Model With Texture Detection Channel

  2020cites this paper
• Research on Computational Simulation of Advertising Posters Visual Cognition

  2020cites this paper
• Probing the Neural Circuits along the Ventral Visual Stream

  2020cites this paper
• Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images.

  2020cites this paper
• Gaze-based Screening of Autistic Traits for Adolescents and Young Adults using Prosaic Videos

  2020cites this paper
• Visual Saliency Based on Two-Dimensional Fractional Fourier Transform

  2019cites this paper
• Rapid visual categorization is not guided by early salience-based selection

  2019cites this paper
• Video saliency detection based on low-level saliency fusion and saliency-aware geodesic

  2019cites this paper
• A neural network model for exogenous perceptual alternations of the Necker cube

  2019cites this paper
• Early Salient Region Selection Does Not Drive Rapid Visual Categorization

  2019cites this paper
• A Novel Probabilistic Contrast-Based Complex Salient Object Detection

  2019cites this paper
• Probabilistic Online Learning of Appearance and Structure for Robotics

  2019cites this paper
• Cognition as inference: a unifying account of some neural effects associated with mental imagery and attention

  2019cites this paper
• Sequences of discrete attentional shifts emerge from a neural dynamic architecture for conjunctive visual search that operates in continuous time

  2018cites this paper
• Fixation-indices based correlation between text and image visual features of webpages

  2018cites this paper
• Not All Predictions Are Equal: “What” and “When” Predictions Modulate Activity in Auditory Cortex through Different Mechanisms

  2018cites this paper
• Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC

  2018cites this paper
• Give Ear to My Face: Modelling Multimodal Attention to Social Interactions

  2018cites this paper
• Toward Human-Like Sentence Interpretation—a Syntactic Parser Implemented as a Restricted Quasi Bayesian Network—

  2018cites this paper
• Haptic Saliency Model for Rigid Textured Surfaces

  2018cites this paper
• A Biased Bayesian Inference for Decision-Making and Cognitive Control

  2018cites this paper
• Samples of cyanobacterium Calothrix sp. ISC 65 collected from oil polluted regions respond to combined effects of salinity, extremely low-carbon dioxide concentration and irradiance

  2018cites this paper
• The Cognitive Philosophical Problems in Visual Attention and Its Influence on Artificial Intelligence Modeling

  2018cites this paper
• Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices

  2018influential citation
• The Statistic Modeling of Eye Movement Viewing S3D Images

  2017cites this paper
• Self-supervised online learning of appearance for 3D tracking

  2017cites this paper
• Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition.

  2017cites this paper
• Average activity, but not variability, is the dominant factor in the representation of object categories in the brain.

  2017cites this paper
• 2D and 3D Visual Attention for Computer Vision: Concepts, Measurement, and Modeling

  2017cites this paper
• Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models

  2017cites this paper
• How biological attention mechanisms improve task performance in a large-scale visual system model

  2017cites this paper
• Top-Down Visual Saliency via Joint CRF and Dictionary Learning

  2017cites this paper
• Restricted Quasi Bayesian Networks as a Prototyping Tool for Computational Models of Individual Cortical Areas

  2017cites this paper
• Understanding Biological Visual Attention Using Convolutional Neural Networks

  2017cites this paper
• Which Structures Are Out There ? Learning Predictive Compositional Concepts Based on Social Sensorimotor Explorations

  2017cites this paper
• A social Bayesian brain: How social knowledge can shape visual perception

  2017cites this paper
• The active construction of the visual world

  2017cites this paper
• Explicit information for category-orthogonal object properties increases along the ventral stream

  2016cites this paper
• Unsupervised learning of invariant representations

  2016cites this paper
• Toward a Unified Sub-symbolic Computational Theory of Cognition

  2016cites this paper
• Responses to Pop-Out Stimuli in the Barn Owl's Optic Tectum Can Emerge through Stimulus-Specific Adaptation

  2016cites this paper
• Semantic Indexing based on Focus of Attention Extended by Weakly Supervised Learning

  2016influential citation
• Content-based image retrieval by using deep kernel Machine with Gaussian Mixture Model

  2016cites this paper
• A Focus on Selection for Fixation

  2016cites this paper
• Scene analysis for effective visual search in rough three-dimensional-modeling scenes

  2016cites this paper
• Optimization and implementation of bio-inspired feature extraction frameworks for visual object recognition

  2016cites this paper
• Toward an Integration of Deep Learning and Neuroscience

  2016cites this paper
• Fabric defect detection via learned dictionary-based visual saliency

  2016cites this paper
• Optimization and implementation of bio-inspired feature extraction frameworks for visual object recognition. (Optimisation et implémentation de méthodes bio-inspirées d'extraction de caractéristiques pour la reconnaissance d'objets visuels)

  2016cites this paper
• Visual Clutter Study for Pedestrian Using Large Scale Naturalistic Driving Data

  2016cites this paper
• Transfer function optimization for volume visualization based on visibility and saliency

  2016cites this paper
• A probabilistic tour of visual attention and gaze shift computational models

  2016influential citation
• Eight open questions in the computational modeling of higher sensory cortex.

  2016cites this paper
• Bayesian Methods for Discovering Structure in Neural Spike Trains

  2016cites this paper
• The Performance of a Biologically Plausible Model of Visual Attention to Localize Objects in a Virtual Reality

  2016cites this paper
• One-Shot Generalization in Deep Generative Models

  2016cites this paper
• A Novel Biologically Mechanism-Based Visual Cognition Model-Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity

  2016cites this paper
• Learning of Spatial Invariances for Object-Ground Separation

  2016cites this paper
• Biologically inspired components in embedded vision systems

  2015cites this paper
• Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement

  2015cites this paper
• Predictive coding and multisensory integration: an attentional account of the multisensory mind

  2015cites this paper
• Towards the quantitative evaluation of visual attention models

  2015cites this paper
• RETRACTED ARTICLE: From the human visual system to the computational models of visual attention: a survey

  2015cites this paper
• Ecological Active Vision: Four Bioinspired Principles to Integrate Bottom–Up and Adaptive Top–Down Attention Tested With a Simple Camera-Arm Robot

  2015cites this paper
• Cortical Networks of Visual Recognition

  2015cites this paper
• Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

  2015cites this paper
• Neural mechanisms for reducing uncertainty in 3D depth perception

  2015cites this paper