hi jack
#### I have 6 different task conditions each represented by two EV (the target and the answer), I add a third EV with a dynamic 3rd column. my contrast (cA-iA)-(cB-iB) account for differences between the two different congruent/incongruent comparison. how could [0 1] do this?
>If you are performing a typical conflict monitoring task, then you will be looking at RT differences between congruent and >incongruent (and maybe neutral) trials. The difference between RT means, i.e. incongRT - congRT, is thought to represent the >amount of "conflict" in the decision. Thus, in standard conflict monitoring models, incongRT-congRT is proportional to conflict. If >you are interested in detecting conflict voxels, then you should create a regressor in which the amplitude of each BOLD response is >proportional to the amount of conflict.
>So, if congruent trials are the "baseline" condition, then assign them a value of zero. Incongruent trials have a conflict level of >incongRT-congRT and neutral trials will presumable have a conflict level of neutRT-congRT. If you think that neutral trials are the >baseline, then assign them a value of zero and make the others incongRT-neutRT and congRT-neutRT. In all cases, the conflict >activity should last until the conflict is resolved, which presumably occurs when the response is made. Thus, the regressor will >have a duration of RT_trial and a height of xRT-baselineRT, where xRT is one of the three conditions.
>It is still important to control for non-specific effects of task, so you will need a regressor that models trial-to-trial RTs. This is >modeling variance unrelated to conflict and thus doesn't change from trial to trial but is present for the duration of the trial. It will >have a height of 1 but a duration of RT_trial.
>The design matrix will only have two regressors: one for non-specific, unmodulated activity and one for conflict-modulated activity. >You are presumably interested in the [0 1] contrast.
let me see if I understood. you suggest me to replace my 6 EV (2 EV for the three cong/incong/neutral conditions) with just 2 EV.
that is, not consider the trial type but just put an EV with fixed 3rd column value (1) and one with variable values (xRT-baselineRT)
and then create one simple contrast [0 1].
if so, consider that i have two types of conflict (defined by the 2 possible "TARGET stimulus" characteristics), that is 2 cong 2 incong and 2 neutral, and one of my question is why/where they behave differently (as i see from RT). do I have to add two further regressors or just the conflict-related (with its different xRT-baselineRT) one?
and make [0 1 0]/[0 1 0 0] and [0 0 1]/[0 0 0 1] to assess the two, presumably different, conflicting voxels patterns?
since in all conditions they have to finally press a button answering to a question regarding the "CUE stimulus" characteristic (which is independent from the conflict type), should i add a further EV in order to let him explain part of the variance.
>Note: the construction of any regressor requires a number of assumptions. In this case, we assume that (1) conflict is present >until the response is made, (2) conflict is proportional to RT differences between conditions, and (3) learning does not affect the >estimate of conflict. Make sure that these assumptions are consistent with the cognitive model you are using to interpret your data.
they should be ok. subjects gets a little faster in the third session compared to the first one, but in all the conditions indifferently.
####I setup such an analysis, the feat_model warned me over the similarity among EV. of course EV1 and EV3 of each condition have same onset and duration. can i simply ignore this warning?
>No. If you ever get this warning, your model is rank deficient and will not provide meaningful results.
it was rank deficient, as my model was wrong.
very thanks Jack, I would have never thought such a model without your help.
cheers
Alberto
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