Visual Attention and Saliency Detection

Human-like meaning maps from single-prompt VLM ratings of local scene meaning

This paper shows that vision-language models can generate human-like meaning maps of scenes using a single prompt without training, matching human and DeepMeaning results in predicting fixations and model performance.

Judging the reasons for fixations: A direct experimental method to assess the contribution of saliency and semantic factors to gaze control

This paper introduces a direct method to assess the contribution of saliency and semantic factors to gaze control by analyzing fixation reasons, finding that semantics generally dominate saliency.

Delaying the onset of aided target recognition highlights allows for a more dispersed allocation of overt attention

This study examines whether delaying the onset of Aided Target Recognition (AiTR) highlights by 250 ms allows for a more even distribution of attention during visual search tasks.

Contextual associations of real-world objects guide attention in visual search

The paper shows that contextual object associations guide attention in visual search, interacting with categorical information and affecting search performance.

Can Individual Internal Models Predict Idiosyncratic Scene Exploration?

The paper tests if individual internal models predict differences in gaze behavior during scene exploration, finding that internal models influence gaze under specific conditions.

Meaning-based guidance of attention in rhesus monkeys during naturalistic scene viewing

This study shows that rhesus monkeys use scene meaning to guide eye movements during natural scene viewing, similar to humans.

Seeing from the inside out: Intrinsic neural dynamics explain individual differences in movie watching

The paper shows that intrinsic neural dynamics in the right temporo-parieto-occipital junction predict upcoming visual gaze targets during low-saliency periods in movie watching.

Neural Computational Model Predicts Attentional Dynamics during Immersive Search in Virtual Reality

The paper evaluates a computational model of visual attention on immersive virtual reality search tasks, showing it can predict eye gaze and manual responses despite varying field-of-view.

Compatibility of a competition model for explaining eyefixation durations during free viewing

The paper adjusts a competition model to explain eye fixation duration distributions and finds it compatible with the exgaussian model.

Salient objects in a scene benefit from enhanced perceptual processing and memory encoding: Evidence for a spatial von Restorff effect

The paper shows that salient objects in visual search tasks trigger early attentional selection and enhance memory encoding, similar to the von Restorff effect but in spatial rather than temporal contexts.

Uncertainty during visual search: Insights from a computational model and behavioral experiment in natural stimuli

This study combines computational modeling and behavioral experiments to examine how uncertainty is perceived during visual search in natural stimuli.

Integrating Ideal Bayesian Searcher and Neural Networks Models for Eye Movement Prediction in a Hybrid Search Task

This paper introduces an enhanced neural network model for predicting eye movements in hybrid search tasks and presents a dataset of human eye movements in such tasks.

A recurrent vision transformer shows signatures of primate visual attention

This paper introduces a Recurrent Vision Transformer that mimics primate visual attention by integrating spatial memory and self-attention, showing improved detection accuracy and reaction times.

The Role of Predictive Processing and Perceptual Load in Selective Visual Attention: An Examination with Semantically Salient and Less Salient Distractors

This study examines how predictive processing and distractor saliency affect selective visual attention through reaction time measurements in letter search tasks with varying cues and distractors.

Neural Correlates of High-Level Visual Saliency Models

This study investigates the neural correlates of high-level visual saliency using computational models and the Natural Scenes Dataset, identifying hV4 as a key region for saliency computations.

Human and computer attention in assessing genetic conditions

This study compares human and computer attention patterns when assessing images of genetic conditions using eye-tracking and deep learning saliency maps.

Aging modulates the effects of scene complexity on visual search and target selection in virtual environments

This study examines how aging affects visual search efficiency in complex virtual environments, finding greater performance declines in older adults with increased complexity.

Focal vs Diffuse: Mechanisms of attention mediated performance enhancement in a hierarchical model of the visual system

This study investigates how spatial attention affects performance in a deep neural network model of the visual system, finding that suppression in unattended regions is more effective than enhancement in attended regions under certain conditions.

Modeling human eye movements during immersive visual search

This study models human eye movements during immersive visual search using a reinforcement learning framework, finding that model predictions align with human gaze behavior.

ASM-3D: An attentional search model fashioned after what andwhere/how pathways for target search in 3D environment

The paper introduces an attentional search model inspired by visual pathways to locate targets in 3D environments using classifier and camera motion networks.

Pupil Size Reflects Exploration and Exploitation in Visual Search (and It's Like Object-Based Attention)

The paper tests adaptive gain theory by linking pupil size to exploration and exploitation behaviors in a visual search task using eye movements.

Deep Neural Network Models of Object Recognition Exhibit Human-like Limitations When Performing Visual Search Tasks

This study shows deep neural networks exhibit human-like limitations in visual search tasks due to object recognition, as evidenced by inverse correlation with search difficulty scores from natural images.

Spatial cueing effects do not always index attentional capture: Evidence for a Priority Accumulation Framework

The paper challenges the standard interpretation of spatial-cueing effects and supports the Priority Accumulation Framework by showing search performance deteriorates with increased search difficulty on valid-cue trials.

Visual Attention Through Uncertainty Minimization in Recurrent Generative Models

This paper introduces a recurrent generative model that mimics human visual attention by minimizing predictive uncertainty through saccadic eye movements.

Spatial attention follows category-based attention during naturalistic visual search: evidence from MEG decoding

The paper uses MEG decoding to show that spatial attention emerges later than category-based attention during visual search.