This task involves rather simple analyses. Might as well make model specifications for them to make running analyses easier and more transparent. - [ ] Detection model (`model-detection_smdl.json`) - Break trial types in auditory, motor, and visual. - Model motor using `onset` + `response_time` as onset and `tap_duration` as duration. - Do we want to include `tap_count` divided by `tap_duration` as a modulator? - Not sure if it's worth it with a block design. - Standard convolution with standard HRF. - Contrasts for `auditory > other`, `visual > other`, and `motor > other`. - [ ] Estimation model (`model-estimation_smdl.json`) - Break trial types in auditory, motor, and visual. - Model motor using `onset` + `response_time` as onset and `tap_duration` as duration. - Do we want to include `tap_count` divided by `tap_duration` as a modulator? - Not sure if parametric modulation is compatible with FIR. - Finite impulse response model. - No real need for contrasts, since we'll pull FIR estimates per condition and model those outside of fitlins.
This task involves rather simple analyses. Might as well make model specifications for them to make running analyses easier and more transparent.
model-detection_smdl.json)onset+response_timeas onset andtap_durationas duration.tap_countdivided bytap_durationas a modulator?auditory > other,visual > other, andmotor > other.model-estimation_smdl.json)onset+response_timeas onset andtap_durationas duration.tap_countdivided bytap_durationas a modulator?