Visual Attention and Saliency Detection

Digit-Tracking Reveals Curiosity-Driven Visual Attention in Macaque Monkeys

This study uses digit-tracking to show macaque monkeys focus on information-rich regions during visual exploration, driven by curiosity rather than rewards.

Learned statistical regularity drives anticipatory micro-saccades toward suppressed distractor locations

The paper shows that statistical learning reduces attentional capture by directing anticipatory micro-saccades toward high-probability distractor locations, supporting a reactive suppression mechanism.

Modeling Attention and Binding in the Brain through Bidirectional Recurrent Gating

This paper introduces a bidirectional recurrent model of attention inspired by biological mechanisms and neural networks, capable of replicating behavioral and neural findings related to visual attention.

Narrative Context Shifts Gaze from Visual to Semantic Salience

The paper tests how semantic context influences eye movements compared to visual salience in narrative viewing.

Rapid inversion of singleton distractor representations underlies learned attentional suppression

The paper identifies a rapid representational inversion of singleton distractor signals in EEG data that supports attentional suppression during visual search.

Horizontal saccade bias results from combination of saliency anisotropies and egocentric biases

The paper investigates the mechanisms behind horizontal saccade bias by analyzing image features and implementing a model combining allocentric and egocentric biases.

Contributions of distinct attention mechanisms to saccadic choices in a gamified, dynamic environment

The paper investigates how exogenous, endogenous, and history-driven attention mechanisms interact in a dynamic, gamified visual scanning task to influence saccadic choices and perceptual performance.

Pre-search attentional focus supports learned suppression in visual search

The paper shows that participants learn to suppress high-probability distractor locations in visual search by allocating anticipatory attentional focus, as evidenced by EEG frequency tagging and alpha power changes.

Motor "laziness" constrains fixation selection in real-world tasks

The paper investigates fixation selection in real-world tasks and finds that motor 'laziness' drives return saccades and center biases, contradicting lab-based principles like inhibition-of-return.

Patterns of Saliency and Semantic Features Distinguish Gaze of Expert and Novice Viewers of Surveillance Footage

This study compares gaze patterns between CCTV experts and novices to identify differences in visual feature attention during surveillance footage analysis.

Objects Guide Human Gaze Behavior in Dynamic Real-World Scenes

This paper introduces a computational framework to simulate human gaze behavior in dynamic scenes, showing that object-based attention improves the accuracy of simulated eye movements compared to spatial-based models.

The influence of stereopsis on visual saliency in a proto-object based model of selective attention

This study develops a proto-object based model of selective attention that incorporates stereoscopic vision, showing improved prediction of human eye movements when stereoscopic information is considered.

Spatiotemporal bias of the human gaze toward hierarchical visual features during natural scene viewing

This study shows that human gaze is more attracted to higher-order visual features than lower-order features or conventional saliency, both spatially and temporally during natural scene viewing.

The Electrophysiological Markers of Statistically Learned Attentional Enhancement: Evidence for a Saliency Based Mechanism

The paper investigates the electrophysiological markers of statistically learned attentional enhancement and finds it differs from top-down attention by lacking alpha lateralization but showing increased N2pc amplitudes at high-probability locations.

Electrophysiological indices of distractor processing in visual search are shaped by target expectations.

The paper examines how target expectations influence electrophysiological indices of distractor processing in visual search, showing that distractor positivity (PD) is attenuated when target features vary randomly.

Learning to suppress likely distractor locations in visual search is driven by the local distractor frequency

The paper investigates how distractor-location frequency affects visual search performance and finds that local distractor frequency, not global frequency, primarily influences distractor interference.

Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps

The paper shows that semantically inconsistent objects attract more fixations than consistent ones, but contextualized meaning maps fail to capture this relationship.

What the flip? What the P-N flip can tell us about proactive suppression

The paper uses a computational model to explain an ERP pattern associated with proactive suppression in visual search, offering a mechanistic account distinct from prior interpretations.

Neurally-constrained modeling of human gaze strategies in a change blindness task

The paper develops a neurally constrained Bayesian model to explain differences in change detection success during a change blindness task by analyzing gaze metrics.

Identification of Target Objects from Gaze Behavior during a Virtual Navigation Task

This study examines gaze behavior during a virtual navigation task, finding that participants fixate more on target objects than distractors and adjust their search patterns under divided attention.

Spatial cueing effects are not what we thought: on the timing of attentional deployment

The paper challenges the traditional interpretation of spatial-cueing effects by showing attention is deployed when search-relevant context appears, not immediately upon a salient cue.

Spatially guided distractor suppression during visual search

The paper shows that spatial suppression of salient distractors improves target selection during visual search, as evidenced by reaction time and neural signal changes.

Characterization of neural communication dynamics in the Ventral Attention Network across distinct spatial and spatio-temporal scales

This study investigates neural communication dynamics in the Ventral Attention Network using EEG and fMRI, revealing context-specific alpha-band interactions during saliency processing.

EEG signatures of contextual influences on visual search with real scenes

The paper shows that EEG activity during visual search increases when targets appear in contextually appropriate locations in real scenes, and this neural response correlates with behavioral decisions.

A Mathematical Model of Exploration and Exploitation in Natural Scene Viewing

The paper develops a mathematical model of exploration and exploitation in natural scene viewing and demonstrates its predictive power using Bayesian inference.

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

This study compares meaning maps and saliency models in predicting human fixations, finding that deep neural networks outperform meaning maps in accuracy and responsiveness to meaning changes.

A dual foveal-peripheral visual processing model implements efficient saccade selection

The paper develops a dual visual processing model that improves saccade selection efficiency by combining foveal accuracy with peripheral visual field analysis.

Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition

This paper introduces a model of reflexive attention that simulates neural attractor states and their role in visual processing, linking them to behavioral and neural correlates like accuracy and EEG components.

Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

The paper develops a neurodynamic model of V1 to predict visual attention by simulating retinal, thalamic, and cortical processes, and demonstrates its superiority over existing models in predicting saccade sequences.

Modeling Attention Control Using A Convolutional Neural Network Designed After The Ventral Visual Pathway

The paper designs a brain-inspired CNN based on the ventral visual pathway to extract category-consistent features and shows it predicts attention control better than other models.