How does one execute a first-level (subject-level) analysis with a continuous covariate?
For example, in an event-related experiment presenting pictures of scenes, I have two conditions: environment (indoors/outdoors) and openness (a continuous rating obtained independently). I am interested in the environment x openness interaction, so I need to model them together.
The parametric modulation paradigm file seems close to what I need, but in the example, there is no discrete variable to consider. Is there a way to model both discrete and continuous variables together?
If so, can this method also be used to model out nuisance regressors, like trial performance?
Thank you,
Emily
Hi Emily, I think the parametric modulation should work. In the example, there is a ShockOffset and ShockSlope as conditions 1 and 2. In your case, you would have 4 conditions. 1. IndoorsOffset 2. Indoors-OpennessSlope 3. OutdoorsOffset 4. Outdoors-OpennessSlope You would then create a contrast of 2 vs 4 to test the interaction. doug
Emily Ward wrote:
How does one execute a first-level (subject-level) analysis with a continuous covariate?
For example, in an event-related experiment presenting pictures of scenes, I have two conditions: environment (indoors/outdoors) and openness (a continuous rating obtained independently). I am interested in the environment x openness interaction, so I need to model them together.
The parametric modulation paradigm file seems close to what I need, but in the example, there is no discrete variable to consider. Is there a way to model both discrete and continuous variables together?
If so, can this method also be used to model out nuisance regressors, like trial performance?
Thank you,
Emily
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Emily Ward