Table 3.

Trans-diagnostic factors and model-based learning.

DOI: http://dx.doi.org/10.7554/eLife.11305.008

Construct	β (SE)	z-value	p-value
Independent Models
‘Anxious-Depression’ (Factor 1)	-0.001(0.01)	0.10	0.920
‘Compulsive Behavior and Intrusive Thought’ (Factor 2)	-0.046(0.01)	-4.06	<0.001 ***
‘Social Withdrawal’ (Factor 3)	0.013(0.01)	1.18	0.238
Covariate Model
‘Anxious-Depression’ (Factor 1)	0.003(0.01)	0.28	0.781
‘Compulsive Behavior and Intrusive Thought’ (Factor 2)	-0.058(0.01)	-4.71	<0.001 ***
‘Social Withdrawal’ (Factor 3)	0.031(0.01)	2.45	0.014*

*p<0.05; **p<0.01; ***p<0.001.

SE=standard error.

Top panel shows results from Independent Models. Bottom panel shows results from Covariate Model, where trans-diagnostic factors were entered together into the same model: glmer(Stay ~ Reward * Transition * (Factor1z + Factor2z + Factor3z + IQz + Agez + Gender) + (Reward * Transition + 1 | Subject)). Statistics refer to the interaction between scores on each factor and Reward x Transition, i.e. the extent to which that score is associated with changes in model-based learning. Positive β values indicate that the symptom score is associated with greater model-based learning, while negative β values indicate that the symptom score is associated with reduced model-based learning.