Analysis logbook: cue-expectancy
1 About
- 1.1 Usage
2 [ beh] expectation ~ cue
3 [ beh ] outcome ~ cue
4 [ beh ] outcome ~ stimulus_intensity
5 outcome_rating ~ cue * stim
- 5.1 What is the purpose of this notebook?
- 5.2 model 03 iv-cuecontrast dv-actual
6 expect-actual ~ cue * trial
7 RT ~ cue
8 [fMRI] RT ~ cue * stim
9 cognitive RT tradeoff ~ cue * stim (withinsubject)
10 [beh] outcome_rating ~ session (“behavioral ICC”)
- 10.1 Functions
- 10.2 TODO:
11 N-1 outcome rating ~ N expectation rating
12 (N-2) shifted outcome ratings ~ (N) expectation ratings; Jayazeri (2018)
13 outcome ~ expect Jayazeri (2018)
14 [ beh ] outcome ~ cue * stim * expectrating * n-1outcomerating
15 [ beh ] outcome_demean ~ cue * stim * expectrating * n-1outcomerating
16 [beh] outcome_demean_per_run ~ cue x stim x expectrating x n-1outcomerating
17 [beh] Mediation outcome ~ cue * stim * expectrating * n-1outcomerating
18 NPS_contrast_notscaled ~ cue * stim
19 [fMRI] NPS_contrast ~ cue * stim
20 [fMRI] nps_contrast ~ cue * stim (“error, contrast not scaled”)
- 20.1 Overview
- 20.2 contrast plot
21 [fMRI] nps_contrast ~ cue * stim
- 21.1 Overview
- 21.2 For loop for all the pvc dummy codes
22 nps_dummy ~ stim
23 biomarker NPS ~ cue * stim (2022)
24 NPSdummy ~ stim * task (contrast-notscaled-error)
25 NPSdummy ~ stim * task (contrast-scaled)
- 25.1 Raincloud plots
- 25.2 Line plots
26 [ fMRI ] Pain signature ~ single trial
27 [fMRI] Vicarious signature ~ single trial
28 [fMRI] Cognitive signature ~ single trial
29 [fMRI] Single trial correlation between cue and stim
30 [fMRI] Signature effect size ~ single trial
31 VIF
32 load in the datamatrix and calculate the vif
33 vif
34 [fMRI] Cognitive signature ~ single trial
35 [fMRI] Vicarious signature ~ single trial
36 [fMRI] Pain signature ~ single trial
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behavioral_ICC

Chapter 32 load in the datamatrix and calculate the vif

Maybe I could use nltools or nilearn using the python patch