scholarly journals Automatic Encoding of Visual Numerosity

2018 ◽  
Vol 18 (10) ◽  
pp. 316
Author(s):  
Nicholas DeWind ◽  
Marty Woldorff ◽  
Elizabeth Brannon
Keyword(s):  
Automatic Encoding

Automatic Encoding of Gender by Three-Year-Old Children

2017 ◽  
Author(s):  
Gina T Bednarek ◽  
Kristin Shutts
Keyword(s):  
Test Phase ◽  
Older Children ◽  
Memory Errors ◽  
Automatic Encoding ◽  
Target Children

The present research tested whether three-year-old children – like older children and adults – automatically encode other people’s gender. Three-year-old participants (N = 24) learned facts about unfamiliar target children who varied in gender and were asked to remember facts about the targets during a test phase. At test, children made more within-category memory errors (e.g., misattributing a fact associated with one girl to another girl) than between-category errors (e.g., misattributing a fact associated with a girl to a boy). The findings suggest that at least as early as three years of age, children automatically encode whether someone is a boy or a girl upon first meeting them. The results have implications for our understanding of the automaticity and emergence of stereotyping processes.

scholarly journals Does perceptual grouping improve visuospatial working memory? Optimized processing or encoding bias

2021 ◽  
Author(s):  
Antonio Prieto ◽  
Vanesa Peinado ◽  
Julia Mayas
Keyword(s):  
Working Memory ◽  
Change Detection ◽  
Visual Working Memory ◽  
Visual Information ◽  
Detection Accuracy ◽  
Visuospatial Working Memory ◽  
Gestalt Grouping ◽  
Color Similarity ◽  
Automatic Encoding ◽  
Change Detection Performance

AbstractVisual working memory has been defined as a system of limited capacity that enables the maintenance and manipulation of visual information. However, some perceptual features like Gestalt grouping could improve visual working memory effectiveness. In two different experiments, we aimed to explore how the presence of elements grouped by color similarity affects the change detection performance of both, grouped and non-grouped items. We combined a change detection task with a retrocue paradigm in which a six item array had to be remembered. An always valid, variable-delay retrocue appeared in some trials during the retention interval, either after 100 ms (iconic-trace period) or 1400 ms (working memory period), signaling the location of the probe. The results indicated that similarity grouping biased the information entered into the visual working memory, improving change detection accuracy only for previously grouped probes, but hindering change detection for non-grouped probes in certain conditions (Exp. 1). However, this bottom-up automatic encoding bias was overridden when participants were explicitly instructed to ignore grouped items as they were irrelevant for the task (Exp. 2).

Comments on the Use of the Systematized Nomenclature of Pathology

1975 ◽  
Vol 14 (02) ◽  
pp. 72-75 ◽  
Author(s):  
P. H. Graepel ◽  
D. E. Henson ◽  
A. W. Pratt
Keyword(s):  
Medical Data ◽  
Traditional Practices ◽  
Diagnostic Code ◽  
Medical Terminology ◽  
Lexical Structure ◽  
Language Data ◽  
Semantic Value ◽  
And Function ◽  
Automatic Encoding ◽  
Pathology Data

Any discussion of the use of the Systematized Nomenclature of Pathology (SNOP) requires comprehension of the principles of organization of SNOP.SNOP is a categorized nomenclature which lists »names« of elements and concepts of pathology. SNOP contains four lists of pathology names and concepts known as TOPOGRAPHY, MORPHOLO-GY, ETIOLOGY and FUNCTION. ‘Clearly, SNOP is not a diagnostic code or a coded medical terminology ; to use SNOP the user must specify the coding structure. Thus SNOP is a basis for building a medical data code. The advantage of a categorized nomenclature is that it is possible to construct a well-defined information space for medical data based on the semantic value of the data and apart from some predetermined numeric code where terms are assigned as a function of the number structure.Thus it has been possible to explore the automatic processing of pathology language data. An automatic encoding capability has been used at NIH to encode surgical pathology data. These encoded data were used to study the deficiencies of SNOP and to evaluate new structures for a truly categorized nomenclature of medicine.This new lexical structure can only be achieved if the pathologists waive traditional practices of assigning names to concepts of pathology and allow for new dimensions of medical ways of thinking which go beyond pure morphology.

Naplps Videotex Frame Creation System with Automatic Encoding fo Input Images

1985 ◽  
Vol CE-31 (3) ◽  
pp. 290-300
Author(s):  
Takahiro Fujimori ◽  
Tadashi Fujiwara ◽  
Masaichi Ishibashi ◽  
Kousuke Komatsu ◽  
Tadahiko Nakamura ◽  
...  
Keyword(s):  
Automatic Encoding

scholarly journals A New Deep Learning Based Multi-Spectral Image Fusion Method

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 570 ◽  
Author(s):  
Jingchun Piao ◽  
Yunfan Chen ◽  
Hyunchul Shin
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Pedestrian Detection ◽  
Fusion Rule ◽  
Activity Level ◽  
Fusion Method ◽  
Multi Scale ◽  
Level Measurement ◽  
Automatic Encoding ◽  
Image Fusion Method

In this paper, we present a new effective infrared (IR) and visible (VIS) image fusion method by using a deep neural network. In our method, a Siamese convolutional neural network (CNN) is applied to automatically generate a weight map which represents the saliency of each pixel for a pair of source images. A CNN plays a role in automatic encoding an image into a feature domain for classification. By applying the proposed method, the key problems in image fusion, which are the activity level measurement and fusion rule design, can be figured out in one shot. The fusion is carried out through the multi-scale image decomposition based on wavelet transform, and the reconstruction result is more perceptual to a human visual system. In addition, the visual qualitative effectiveness of the proposed fusion method is evaluated by comparing pedestrian detection results with other methods, by using the YOLOv3 object detector using a public benchmark dataset. The experimental results show that our proposed method showed competitive results in terms of both quantitative assessment and visual quality.

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