Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
about
Machine learning provides novel neurophysiological features that predict performance to inhibit automated responsesThe Psychophysiology of Action: A Multidisciplinary Endeavor for Integrating Action and CognitionOn the Neurophysiological Mechanisms Underlying the Adaptability to Varying Cognitive Control Demands
P2860
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
@ast
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
@en
type
label
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
@ast
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
@en
prefLabel
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
@ast
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
@en
P2860
P1433
P1476
Classifying Response Correctness across Different Task Sets: A Machine Learning Approach
@en
P2093
Edmund Wascher
Michael Falkenstein
P2860
P304
P356
10.1371/JOURNAL.PONE.0152864
P407
P577
2016-03-31T00:00:00Z