When moving from model 3 to 4, you are adding two variables instead of one. Not only are you adding $g + h$ but implicitly the interaction $g * h$. If there is no evidence of a correlation, there could potentially be an interaction effect between the two
KRSS measures that moderates (i.e. attenuates) the effect of the
Stop-Signal RT $(0.242 < 0.363)$ as well as the
PPT baseline $(-0.397 < -0.215)$.
However, I would first isolate each of the
KRSS measures and build two models, let's call them 4a and 4b. In 4a you add the
KRSS Thought suppresion measure to Model 3, and in 4b you instead add
KRSS HilfHoffnungslosigkeit. These two steps could rule out any individual moderation and validate your correlation analysis.
In case neither the models 4a or 4b individually moderate the
RT or the
PPT scores, it is likely a moderation-by-interaction effect.
To test this for the interaction $g * h$, compute a new variable:
KRSS Thought Suppresion * KRSS HilfHoffnungslosigkeit and include this variable in the correlation analysis. (If the variables are of different scales, you should standardize them before the multiplication.)
Also note that gender seems to play a much lesser role in Model 4 than in 3: $(-0.1 << -0.013)$, suggesting perhaps that this effect was absorbed by one of the
If you are interested in the effects of adding variables, a more detailed approach would be to extend the analysis with mediation / moderation analysis. That is also the basis for my recommendation, so the procedure is similar, but if you have a grasp of these concepts you can make a nice flow chart.
Finally, because the $F$-test for models 1 and 2 are not significant, you could exclude these variables from the final model. Naturally, you should still report the negative findings of the hierarchical analysis. You could also plot each model in a single plot and compare the slopes and the intercepts to get a sense of how the hierarchical steps affect the model. If you are using SPSS, use the Save function in the
Regression window and then create a combined line plot of the $\hat y$ values for each model.
I hope this helps.