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Table 3. 

Standardized Regression Weights and Fit Indices of Structural Equation Models for Latent Regression Analyses between ERP Components and Face Cognition Abilities

Latent Factor
Face Cognition Accuracy
Face Cognition Speed
CFI
RMSEA
χ2(df = 130)
P100 latency .07 .14 .943 .065 175 
P100 amplitude .09 −.01 .961 .061 170 
N170 latency −.30* .19 .932 .072 187 
N170 amplitude .08 −.10 .967 .056 164 
Dm latency −.20 .11 .947 .055 163 
Dm amplitude .19 −.07 .938 .075 192 
ERE for learned faces latency −.33* −.12 .958 .050 157 
ERE for learned faces amplitude .41** .46*** .943 .059 167 
ERE for unfamiliar faces latency −.11 .11 .900 .080 200 
ERE for unfamiliar faces amplitude .13 −.11 .938 .062 172 
LRE for learned faces latency −.48*** −.23 .965 .045 152 
LRE for learned faces amplitude .31* .35** .954 .052 160 
LRE for unfamiliar faces latency .14 .22 .923 .066 178 
LRE for unfamiliar faces amplitude .14 −.15 .932 .063 173 
Latent Factor
Face Cognition Accuracy
Face Cognition Speed
CFI
RMSEA
χ2(df = 130)
P100 latency .07 .14 .943 .065 175 
P100 amplitude .09 −.01 .961 .061 170 
N170 latency −.30* .19 .932 .072 187 
N170 amplitude .08 −.10 .967 .056 164 
Dm latency −.20 .11 .947 .055 163 
Dm amplitude .19 −.07 .938 .075 192 
ERE for learned faces latency −.33* −.12 .958 .050 157 
ERE for learned faces amplitude .41** .46*** .943 .059 167 
ERE for unfamiliar faces latency −.11 .11 .900 .080 200 
ERE for unfamiliar faces amplitude .13 −.11 .938 .062 172 
LRE for learned faces latency −.48*** −.23 .965 .045 152 
LRE for learned faces amplitude .31* .35** .954 .052 160 
LRE for unfamiliar faces latency .14 .22 .923 .066 178 
LRE for unfamiliar faces amplitude .14 −.15 .932 .063 173 

*p < .05.

**p < .01.

***p < .001.

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