The ambivalent mind can be a wise mind: Emotional ambivalence increases judgment accuracy

2013 ◽  
Vol 49 (3) ◽  
pp. 360-367 ◽  
Author(s):  
Laura Rees ◽  
Naomi B. Rothman ◽  
Reuven Lehavy ◽  
Jeffrey Sanchez-Burks
2013 ◽  
Vol 27 (4) ◽  
pp. 283-293 ◽  
Author(s):  
Lars Behrmann ◽  
Elmar Souvignier

Single studies suggest that the effectiveness of certain instructional activities depends on teachers' judgment accuracy. However, sufficient empirical data is still lacking. In this longitudinal study (N = 75 teachers and 1,865 students), we assessed if the effectiveness of teacher feedback was moderated by judgment accuracy in a standardized reading program. For the purpose of a discriminant validation, moderating effects of teachers' judgment accuracy on their classroom management skills were examined. As expected, multilevel analyses revealed larger reading comprehension gains when teachers provided students with a high number of feedbacks and simultaneously demonstrated high judgment accuracy. Neither interactions nor main effects were found for classroom management skills on reading comprehension. Moreover, no significant interactions with judgment accuracy but main effects were found for both feedback and classroom management skills concerning reading strategy knowledge gains. The implications of the results are discussed.


2007 ◽  
Author(s):  
Matthew E. Jacovina ◽  
Richard J. Gerrig

2008 ◽  
Author(s):  
J. Frank Yates ◽  
Elizabeth Dries ◽  
Samuel R. Jackson ◽  
Nicole Mattise

2018 ◽  
Vol 13 (3) ◽  
pp. 287-307 ◽  
Author(s):  
Jan A. A. Engelen ◽  
Gino Camp ◽  
Janneke van de Pol ◽  
Anique B. H. de Bruin

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).


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