Table 3 Drug effects on model parameters

From: Dopamine regulates decision thresholds in human reinforcement learning in males

RLDDM2 parameter

L-dopa effect

Haloperidol effect

 

M

[95% HDI]

P(effect < 0)

dBF (<0)

M

[95% HDI]

P(effect < 0)

dBF (<0)

α

−.114

[−.219, .001]

.977

37.462

−.125

[−.228, −.022]

.988

72.841

τ

.000

[−.033, .034]

.502

1.093

−.013

[−.039, .015]

.840

5.087

vcoeff

−.0104

[−1.057, .834]

.588

1.482

−.255

[−.969, .467]

.762

3.181

η+

−.076

[−.283, .127]

.776

3.290

−.046

[−.438, .381]

.606

1.523

η-

−.655

[−1.918, .538]

.870

6.192

−1.69

[−3.323, −.125]

.987

78.686

  1. Statistical testing was performed by directly examining the posterior distributions of group-level parameters. Mean posterior drug effects on model parameters (Mdiff) relative to the Placebo condition, for L-dopa (left) and Haloperidol (right) and posterior probabilities that the given change in a parameter is <0, P(effect<0). Directional Bayes Factors (dBF) quantify the degree of evidence for a reduction in a parameter, relative to an increase. No correction for multiple comparisons was applied for these measures99.
Back to article page