Redundancy masking is the reduction of the perceived number of items in repeating patterns. It shares a number of characteristics with crowding, the impairment of target identification in visual clutter. Crowding strongly depends on the location of the target in the visual field. For example, it is stronger in the upper compared to the lower visual field and is usually weakest on the horizontal meridian. This pattern of visual field asymmetries is common in spatial vision, as revealed by tasks measuring, for example, spatial resolution and contrast sensitivity. Here, to characterize redundancy masking and reveal its similarities to and differences from other spatial tasks, we investigated whether redundancy masking shows the same typical visual field asymmetries. Observers were presented with three to six radially arranged lines at 10° eccentricity at one of eight locations around fixation and were asked to report the number of lines. We found asymmetries that differed pronouncedly from those found in crowding. Redundancy masking did not differ between upper and lower visual fields. Importantly, redundancy masking was stronger on the horizontal meridian than on the vertical meridian, the opposite of what is usually found in crowding. These results show that redundancy masking diverges from crowding in regard to visual field asymmetries, suggesting different underlying mechanisms of redundancy masking and crowding. We suggest that the observed atypical visual field asymmetries in redundancy masking are due to the superior extraction of regularity and a more pronounced compression of visual space on the horizontal compared to the vertical meridian.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012886PMC
http://dx.doi.org/10.1167/jov.22.5.4DOI Listing

Publication Analysis

Top Keywords

redundancy masking
36
visual field
28
field asymmetries
20
redundancy
9
masking
9
visual
9
atypical visual
8
asymmetries redundancy
8
lower visual
8
horizontal meridian
8

Similar Publications

Background: Patients with end-stage renal disease (ESRD) undergoing hemodialysis (HD) exhibit a high mortality risk, particularly at the onset of treatment. Conventional risk assessment models, dependent on extensive temporal data accumulation, frequently encounter issues of data incompleteness and lengthy collection periods.

Objective: This study addresses the imbalance in short-term HD data and the issue of missing data features, achieving a robust assessment of mortality risk for HD patients over the subsequent 30 to 450 days.

View Article and Find Full Text PDF

Background: Accurate musculoseletal ultrasound (MSKUS) image segmentation is crucial for diagnosis and treatment planning. Compared with traditional segmentation methods, deploying deep learning segmentation methods that balance segmentation efficiency, accuracy, and model size on edge devices has greater advantages.

Purpose: This paper aims to design a MSKUS image segmentation method that has fewer parameters, lower computation complexity and higher segmentation accuracy.

View Article and Find Full Text PDF

Fusion Attention for Action Recognition: Integrating Sparse-Dense and Global Attention for Video Action Recognition.

Sensors (Basel)

October 2024

Department of Computer Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea.

Conventional approaches to video action recognition perform global attention over the entire video patches, which may be ineffective due to the temporal redundancy of video frames. Recent works on masked video modeling adopt a high-ratio tube masking and reconstruction strategy as a pre-training method to mitigate the problem of focusing on spatial features well but not on temporal features. Inspired by this pre-training method, we propose Fusion Attention for Action Recognition (FAR), which fuses the sparse-dense attention patterns specialized for temporal features with global attention during fine-tuning.

View Article and Find Full Text PDF
Article Synopsis
  • Wearable sensors like Seismocardiography (SCG) offer non-invasive cardiac health monitoring, but challenges from motion artefacts, primarily caused by walking, limit their effectiveness.
  • The Adaptive Bidirectional Filtering (ABF) technique improves SCG signal quality by using advanced noise-cancellation and a unique filtering process that distinguishes between actual heart signals and motion noise.
  • Empirical tests show ABF achieves high accuracy in heart rate estimation (r-squared value of 0.95 at -20 dB SNR) and effectively reduces motion artefacts without needing ECG inputs, making it suitable for use in noisy settings.
View Article and Find Full Text PDF
Article Synopsis
  • Dynamic functional connectivity (DFC) from resting-state fMRI is useful in classifying neuropsychiatric disorders but faces challenges like information redundancy and inadequacy of deep learning models for connectivity data.
  • The study introduces a novel approach called region-state masked autoencoder (RS-MAE) that reduces redundancy in DFC matrices and adapts well to the specific structure of connectivity data.
  • RS-MAE shows promising results by achieving classification accuracies of around 76-89% for disorders like ADHD, autism, Alzheimer’s, and schizophrenia when tested on four datasets, proving its effectiveness for neuropsychiatric disorder classification.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!