Table 2 Higher trait psychopathy is associated with decreased other-relevant but not self-relevant learning rates

From: Neurocomputational basis of learning when choices simultaneously affect both oneself and others

Four-option task

b

SE

Z

p

CI 95%

Intercept

−2.182***

0.128

−17.000

<0.001

−2.434, −1.931

Valence (Positive > Negative)

0.454***

0.116

3.900

<0.001

0.226, 0.683

Target (Self > Other)

0.931***

0.116

7.995

<0.001

0.703, 1.159

Valence × Target

0.214

0.233

0.917

0.359

−0.243, 0.670

Psychopathy

−0.424

0.584

−0.727

0.468

−1.569, 0.720

Valence × Psychopathy

0.144

0.530

0.272

0.785

−0.894, 1.183

Target × Psychopathy

1.418**

0.530

2.675

0.007

0.379, 2.456

Valence × Target × Psychopathy

0.445

1.060

0.420

0.675

−1.632, 2.522

σ2

1.165

0.234

   

Two-option task

b

SE

Z

p

CI 95%

Intercept

−3.015***

0.095

−31.806

<0.001

−3.201, −2.829

Valence (Positive > Negative)

2.184***

0.113

19.251

<0.001

1.962, 2.407

Target (Self > Other)

1.796***

0.113

15.827

<0.001

1.573, 2.018

Valence × Target

−0.595**

0.227

−2.622

0.009

−1.040, −0.150

Psychopathy

−1.043***

0.276

−3.783

<0.001

−1.583, −0.502

Valence × Psychopathy

1.245***

0.330

3.774

<0.001

0.598, 1.891

Target × Psychopathy

1.553***

0.330

4.708

<0.001

0.906, 2.200

Valence × Target × Psychopathy

−0.452

0.660

−0.685

0.493

−1.745, 0.841

σ2

0.686

0.130

   
  1. Results from linear mixed-effects models predicting log learning rates as a function of trait psychopathy, target-relevance, and valence. The intercepts correspond to the grand mean log learning rate (log(α)). b coefficients represent unstandardized fixed effects estimates. All trait variables are grand mean-centered. P values are two-sided. ** indicates p < 0.01, *** indicates p < 0.001
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