INAMA: An Interactive Attentional Model for Node Alignment

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
Keyword(s):  
Attentional Model

The Role of Visual Distractors in the Simon Effect

2017 ◽  
Vol 64 (6) ◽  
pp. 387-397 ◽  
Author(s):  
Luisa Lugli ◽  
Stefania D’Ascenzo ◽  
Roberto Nicoletti ◽  
Carlo Umiltà
Keyword(s):  
Simon Effect ◽  
Simon Task ◽  
Automatic Generation ◽  
Spatial Code ◽  
Attentional Model ◽  
Previous Evidence ◽  
Spatial Stimulus ◽  
Attentional Resources ◽  
Code Changes

Abstract. The Simon effect lies on the automatic generation of a stimulus spatial code, which, however, is not relevant for performing the task. Results typically show faster performance when stimulus and response locations correspond, rather than when they do not. Considering reaction time distributions, two types of Simon effect have been individuated, which are thought to depend on different mechanisms: visuomotor activation versus cognitive translation of spatial codes. The present study aimed to investigate whether the presence of a distractor, which affects the allocation of attentional resources and, thus, the time needed to generate the spatial code, changes the nature of the Simon effect. In four experiments, we manipulated the presence and the characteristics of the distractor. Findings extend previous evidence regarding the distinction between visuomotor activation and cognitive translation of spatial stimulus codes in a Simon task. They are discussed with reference to the attentional model of the Simon effect.

Time Estimation Methods—Do They Influence Prospective Duration Estimates?

Perception ◽  
1993 ◽  
Vol 22 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Dan Zakay
Keyword(s):  
Information Processing ◽  
Positive Relationship ◽  
Negative Relationship ◽  
Estimation Method ◽  
Time Estimation ◽  
Estimation Methods ◽  
Clock Time ◽  
Processing Load ◽  
Attentional Model ◽  
Time Length

The validity of an attentional model of prospective time estimation was tested in three experiments. In the first experiment two variables were manipulated: (1) nontemporal information processing load during the estimated interval, and (2) time estimation method, ie production of time simultaneously with the performance of a second task, or reproduction of time immediately upon termination of a task whose duration has to be measured. As predicted, a positive relationship between produced time length and information processing load demanded by a simultaneous task, and a negative relationship between reproduced time length and information processing load during the estimated interval, were found. The results were replicated in a second experiment in which verbal estimates of time were also measured and the objective duration of the estimated interval was varied. The pattern of results obtained for verbal estimates was similar to that obtained for reproduced ones. The results of a third experiment indicated that produced and reproduced times were positively correlated with clock time. The results are interpreted as supporting an attentional model of prospective time estimation.

An Attentional Model for Autonomous Mobile Robots

2017 ◽  
Vol 11 (3) ◽  
pp. 1308-1319 ◽  
Author(s):  
Esther Luna Colombini ◽  
Alexandre da Silva Simoes ◽  
Carlos Henrique Costa Ribeiro
Keyword(s):  
Mobile Robots ◽  
Autonomous Mobile Robots ◽  
Attentional Model

Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval

2019 ◽  
Author(s):  
Lingjun Zhao ◽  
Rabih Zbib ◽  
Zhuolin Jiang ◽  
Damianos Karakos ◽  
Zhongqiang Huang
Keyword(s):  
Information Retrieval ◽  
Ad Hoc ◽  
Attentional Model ◽  
Low Resource ◽  
Weakly Supervised ◽  
Cross Lingual

Evidence for an attentional model of human intelligence using the competing task paradigm

1991 ◽  
Vol 12 (5) ◽  
pp. 445-455 ◽  
Author(s):  
Richard D. Roberts ◽  
Helen C. Beh ◽  
Georgina Spilsbury ◽  
Lazar Stankov
Keyword(s):  
Human Intelligence ◽  
Attentional Model

Finding regions of interest in pathological images: an attentional model approach

2009 ◽  
Author(s):  
Francisco Gómez ◽  
Julio Villalón ◽  
Ricardo Gutierrez ◽  
Eduardo Romero
Keyword(s):  
Regions Of Interest ◽  
Attentional Model ◽  
Model Approach

An attentional model of dimensional contrast.

1997 ◽  
Vol 23 (3) ◽  
pp. 295-311 ◽  
Author(s):  
John M. Hinson ◽  
Linda R. Tennison
Keyword(s):  
Attentional Model

scholarly journals Relative prediction error and protection from attentional blink in human associative learning

2018 ◽  
Author(s):  
Steven Glautier
Keyword(s):  
Reaction Time ◽  
Attentional Blink ◽  
Prediction Error ◽  
Visual Presentation ◽  
Attentional Model ◽  
Attentional Processing ◽  
Predictive Learning ◽  
Learning Rates ◽  
Learning Procedure ◽  
The Relationship

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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