Table 2

Betas from a logistic mixed-effect model predicting memory accuracy. The train condition was coded as 1 and the untrain condition was coded as 0. Therefore, the beta for “Train” represents the difference in memory accuracy between conditions. Note MPFC encode similarity is scaled, so that a unit change represents a 0.1 change

Estimates
(log-odds)
SEZ-valueP-value
Intercept1.160.1110.31P < 0.001 *
Train0.310.093.29P < 0.001 *
MPFC train vs untrain encode Similarity-0.080.14-0.55P = 0.57
Train * MPFC train vs untrain encode similarity0.330.122.69P = 0.007 *
Estimates
(log-odds)
SEZ-valueP-value
Intercept1.160.1110.31P < 0.001 *
Train0.310.093.29P < 0.001 *
MPFC train vs untrain encode Similarity-0.080.14-0.55P = 0.57
Train * MPFC train vs untrain encode similarity0.330.122.69P = 0.007 *

Note: * indicates significant coefficients at P < 0.001.

Table 2

Betas from a logistic mixed-effect model predicting memory accuracy. The train condition was coded as 1 and the untrain condition was coded as 0. Therefore, the beta for “Train” represents the difference in memory accuracy between conditions. Note MPFC encode similarity is scaled, so that a unit change represents a 0.1 change

Estimates
(log-odds)
SEZ-valueP-value
Intercept1.160.1110.31P < 0.001 *
Train0.310.093.29P < 0.001 *
MPFC train vs untrain encode Similarity-0.080.14-0.55P = 0.57
Train * MPFC train vs untrain encode similarity0.330.122.69P = 0.007 *
Estimates
(log-odds)
SEZ-valueP-value
Intercept1.160.1110.31P < 0.001 *
Train0.310.093.29P < 0.001 *
MPFC train vs untrain encode Similarity-0.080.14-0.55P = 0.57
Train * MPFC train vs untrain encode similarity0.330.122.69P = 0.007 *

Note: * indicates significant coefficients at P < 0.001.

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