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Hi, thanks for your replies. The trial structure is Fixation (500ms) Cue (1000ms) <Interval> Outcome (750ms) < ITI> The cue give the participant the probabilities of various outcomes. There are 4 cues, 2 of which are associated with outcomes A and B (probabilities 75/25% and 25/75% respectively), and the other 2 are associated with outcomes A and C (75/25 and 25/75 again). The relationship between the cue and outcome is not therefore orthogonal so I think we would need to be able to separate the responses to the cue from the responses to the outcome. I think, as Helmut says, we would want the interval between cue and outcome to be shorter than the ITI to maintain the integrity of the trial. Given scanning time we could really do with an average trial length of 10s max, which is difficult to achieve with two intervals. Perhaps 2 - 4 for the interval and 3 - 7 for the outcome? Or would that be too short to distinguish the responses to cue and outcome. I've seen papers where the ITI is jittered using a non-uniform distribution (e.g. the number of trials with each of the ITI lengths is different). For example Ziaei et al (2014) 'Brain systems underlying attentional control...' use an ITI distribution of: 42% 1.5 s, 28% 3 s, 14% 4.5 s, 12% 6 s, and 4% 7.5 s). There is an earlier paper (Hagberg et al (2001) 'Improved detection of event-related....') which seems to suggest using simulated data that Geometric or exponential distributions of ITI, biased towards shorter ITI (As per Ziaei et al) are better than uniform distributions at Detection and Parameter estimation, as long as the range of ITIs in the distribution is quite wide (i.e. a small number of long ITIs of say 15s). I can't find any more recent work on this. Is this a common method? Are these distributions what optseq produces? - I couldn't get it to work on my windows machine. Thanks Rob