attentional model
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2021 ◽  
Vol 22 (7) ◽  
pp. 1587-1597
Author(s):  
Zhichao Huang Zhichao Huang ◽  
Yuxi Sun Zhichao Huang ◽  
Yunming Ye Yuxi Sun ◽  
Wensheng Gan Yunming Ye ◽  
Wei-Che Chien Wensheng Gan
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2021 ◽  
pp. 53-64
Author(s):  
Alana de Santana Correia ◽  
Iury Cleveston ◽  
Viviane Bonadia dos Santos ◽  
Sandra Avila ◽  
Esther Luna Colombini

2019 ◽  
Author(s):  
Lingjun Zhao ◽  
Rabih Zbib ◽  
Zhuolin Jiang ◽  
Damianos Karakos ◽  
Zhongqiang Huang

2019 ◽  
Vol 51 ◽  
pp. 101-115 ◽  
Author(s):  
Naji Khosravan ◽  
Haydar Celik ◽  
Baris Turkbey ◽  
Elizabeth C. Jones ◽  
Bradford Wood ◽  
...  

Author(s):  
Lei Zheng ◽  
Yixue Wang ◽  
Lifang He ◽  
Sihong Xie ◽  
Fengjiao Wang ◽  
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2018 ◽  
Author(s):  
Steven Glautier

The relationship between predictive learning and attentional processing was investigated in twoexperiments. During a learning procedure participants viewed rapid serial visual presentation(RSVP) of stimuli in the context of a choice-reaction-time (CRT) task. Salient stimuli in theRSVP streams were either predictive or non-predictive for the outcome of the CRT task.Following this procedure we measured attentional blink (AB) to the predictive and non-predictive stimuli. In Experiment 1, despite the use of a large sample and checks demonstratingthe validity of the learning procedure and the AB measure, we did not observe reduced AB forpredictive stimuli. In contrast, in Experiment 2, where the predictive stimuli occurred alongsidesalient non-predictive comparison stimuli, we did find less AB for predictive than for non-predictive stimuli. Our results support an attentional model of learning in which relativeprediction error is used to increase learning rates for good predictors and reduce learning ratesfor poor predictors (Mackintosh, 1975) and provide confirmation of the AB learning effectoriginally reported by Livesey, Harris, and Harris (2009).


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