scholarly journals Subitizing: Direct apprehension or serial processing?

1988 ◽  
Vol 44 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Charles L. Folk ◽  
Howard Egeth ◽  
Ho-Wan Kwak
Keyword(s):  
Serial Processing ◽  
Direct Apprehension

scholarly journals Visual word recognition: evidence for a serial bottleneck in lexical access

2019 ◽  
Author(s):  
Alex L. White ◽  
Geoffrey M. Boynton ◽  
John Palmer
Keyword(s):  
Lexical Access ◽  
Visual Processing ◽  
Semantic Processing ◽  
The Other ◽  
Focused Attention ◽  
Capacity Limits ◽  
Semantic Categorization ◽  
Serial Processing ◽  
Trial Stimulus ◽  
Limited Accuracy

Reading is a demanding task, constrained by inherent processing capacity limits. Do those capacity limits allow for multiple words to be recognized in parallel? In a recent study, we measured semantic categorization accuracy for nouns presented in pairs. The words were replaced by post-masks after an interval that was set to each subject’s threshold, such that with focused attention they could categorize one word with ~80% accuracy. When subjects tried to divide attention between both words, their accuracy was so impaired that it supported a serial processing model: on each trial, subjects could categorize one word but had to guess about the other (White, Palmer & Boynton, 2018). In the experiments reported here, we investigated how our previous result generalizes across two tasks that require lexical access but vary in the depth of semantic processing (semantic categorization and lexical decision), and across different masking stimuli, word lengths, lexical frequencies and visual field positions. In all cases, the serial processing model was supported by two effects: (1) a sufficiently large accuracy deficit with divided compared to focused attention; and (2) a trial-by-trial stimulus processing tradeoff, meaning that the response to one word was more likely to be correct if the response to the other was incorrect. However, when the task was to detect colored letters, neither of those effects occurred, even though the post-masks limited accuracy in the same way. Altogether, the results are consistent with the hypothesis that visual processing of words is parallel but lexical access is serial.

scholarly journals The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

2018 ◽  
Vol 173 ◽  
pp. 03071
Author(s):  
Wu Wenbin ◽  
Yue Wu ◽  
Jintao Li
Keyword(s):  
Image Compression ◽  
Clustering Algorithm ◽  
Compression Algorithm ◽  
Prediction Algorithm ◽  
Two Dimensional ◽  
Spectral Image ◽  
Compression Algorithms ◽  
Serial Processing ◽  
Compression Time ◽  
Hyper Spectral

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

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