Analysis logbook: cue-expectancy
1
About
1.1
Usage
2
[ beh] expectation ~ cue
2.1
Pain
2.2
Vicarious
2.3
Cognitive
2.4
Individual difference analysis
3
[ beh ] outcome ~ cue
3.1
Pain
3.2
Vicarious
3.3
Cognitive
3.4
Individual differences analysis: random cue effects
3.5
Individual differences analysis 2: random intercept + random slopes of cue effect
4
[ beh ] outcome ~ stimulus_intensity
4.1
Pain
4.2
Vicarious
4.3
Cognitive
4.4
for loop
4.5
Lineplot
4.6
individual differences in outcome rating cue effect
5
outcome_rating ~ cue * stim
5.1
What is the purpose of this notebook?
5.2
model 03 iv-cuecontrast dv-actual
5.2.1
model 03 3-2. individual difference
5.2.2
model 04 iv-cue-stim dv-actual
5.2.3
model 04 4-2 individual differences in cue effects
5.2.4
model 04 4-3 scatter plot
5.2.5
model 04 4-4 lineplot
6
expect-actual ~ cue * trial
6.1
Overview
6.2
plot 1 - one run, average across participants
6.3
plot 2 - average across participant, but spread all 6 runs in one x axis
6.4
Do current expectation ratings predict outcome ratings?
6.5
Additional analysis
7
RT ~ cue
7.0.1
parameters
7.0.2
1) plot RT data
7.0.3
plot RT distribution per participant
7.0.4
exclude participants with RT of 5 seconds
7.1
model 1:
7.2
model 1-1
7.3
model 1-2:
7.4
model 2:
8
[fMRI] RT ~ cue * stim
8.1
Overview model 05 iv-cue dv-RT summary
8.2
Prepare data and preprocess
8.3
model 1:
8.4
model 1-1
8.5
model 1-2:
8.6
model 2: Log transformation
8.7
Conclusion across model 1 and 2
9
cognitive RT tradeoff ~ cue * stim (withinsubject)
9.1
Overview
9.2
Why use multilevel models?
9.3
Terminology
9.4
Model versions
9.5
Method 1 one-sample t
9.6
Method 1-1 aov
9.7
Method 1-2 aov contrast-coding
9.8
Method 1 effectsize
9.9
Method 2 matlab
9.10
Method 3 multilevel modeling
Conclusion: Method 1 vs Method 3
9.11
References
9.12
Other links
10
[beh] outcome_rating ~ session (“behavioral ICC”)
10.1
Functions
10.2
TODO:
11
N-1 outcome rating ~ N expectation rating
11.0.1
DONE
11.1
Overview
11.2
Do previous outcome ratings predict current expectation ratings?
11.3
Additional analysis
11.4
Let’s demean the ratings.
11.5
DEMEAN AND THEN DISCRETIZE
12
(N-2) shifted outcome ratings ~ (N) expectation ratings; Jayazeri (2018)
12.1
Do previous outcome ratings predict current expectation ratings?
12.2
Do these models differ as a function of cue?
12.3
Demean and discretize
13
outcome ~ expect Jayazeri (2018)
13.1
Overview
13.2
Do expectation ratings predict current outcome ratings? Does this differ as a function of cue?
13.3
task-pain, HLM modeling
13.4
Fig. Expectation ratings predict outcome ratings
13.5
binned expectation ratings per task
13.5.1
Pain: binned expectation ratings
13.5.2
Vicarious: binned expectation ratings
13.5.3
Cognitive: binned expectation ratings
13.6
not splitting into cue groups
14
[ beh ] outcome ~ cue * stim * expectrating * n-1outcomerating
14.1
Original motivation:
14.2
Pain
14.2.1
pain plot parameters
14.2.2
loess
14.3
Vicarious
14.4
Cognitive
14.4.1
cognitive parameters
15
[ beh ] outcome_demean ~ cue * stim * expectrating * n-1outcomerating
15.1
linear model
15.2
Q. Are those overestimating for high cues also underestimators for low cues?
15.3
pain run, collapsed across stimulus intensity
15.4
vicarious
15.5
cognitive
15.6
across tasks (PVC), is the slope for (highvslow cue) the same?Tor question
16
[beh] outcome_demean_per_run ~ cue x stim x expectrating x n-1outcomerating
16.1
Pain
16.2
Linear model with three factors: cue X stim X expectation rating
16.3
Pain run, collapsed across stimulus intensity
16.4
Vicarious
16.5
Cognitive
16.6
across tasks (PVC), is the slope for (highvslow cue) the same?Tor question
17
[beh] Mediation outcome ~ cue * stim * expectrating * n-1outcomerating
17.1
mediation
17.2
mediation 2
17.3
mediation 3: Test same model using mediation() from MBESS
17.4
mediation 4: Test library mediation
18
NPS_contrast_notscaled ~ cue * stim
18.1
Overview
18.2
regressors and contrasts
18.3
main effect: stim-linear high > low
18.4
main_effect: stim-quadratic med > high&low
18.5
interaction: cue X stim-linear
18.6
interaction: cue X stim-quadratic
19
[fMRI] NPS_contrast ~ cue * stim
19.1
Overview
19.2
regressors and contrasts
19.3
main effect: stim-linear high > low
19.4
main_effect: stim-quadratic med > high&low
19.5
interaction: cue X stim-linear
19.6
interaction: cue X stim-quadratic
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
22.1
TODO
22.2
regressors and contrasts
22.3
Functions
22.4
Pain
22.5
Vicarious
22.6
Cognitive
23
biomarker NPS ~ cue * stim (2022)
23.1
load libraries
23.1.1
NPS cue effect
23.2
NPS stim effect
23.3
VPS
23.4
VPS cue effect
23.5
VPS stim effect
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
26.1
PVC all task comparison
26.2
Pain only Stim x cue interaction
26.2.1
2x3 stimulus intensity * cue
26.2.2
Linear model
26.2.3
NPS stimulus intensity Cohen’s d = 1.287
26.2.4
NPS stimulus & cue effect size: stim_d = 1.16, cue_d = 0.45
26.2.5
Lineplots
26.2.6
Session 1: 2x3 stimulus intensity * cue
26.2.7
Session 3: 2x3 stimulus intensity * cue
26.2.8
Session 4: 2x3 stimulus intensity * cue
26.3
Pain only: Outcome ratings & NPS
26.3.1
outcome ratings * cue
26.3.2
outcome ratings * stim * cue
26.4
Pain only: Expectation ratings & NPS
27
[fMRI] Vicarious signature ~ single trial
27.1
PVC all task comparison
27.2
Vicarious only Stim x cue interaction
27.2.1
2x3 stimulus intensity * cue
27.2.2
Linear model
27.2.3
VPS stimulus intensity Cohen’s d = 0.2131521
27.2.4
VPS stimulus & cue effect size: stim_d = 0.217, cue_d = 0.013
27.2.5
Lineplots
27.2.6
Linear model with Stim x Cue x Expectation rating
27.2.7
Session 1: 2x3 stimulus intensity * cue
27.2.8
Session 3: 2x3 stimulus intensity * cue
27.2.9
Session 4: 2x3 stimulus intensity * cue
27.3
Vicarious only: Outcome ratings & VPS
27.3.1
outcome ratings * cue
27.4
Vicarious only: Expectation ratings & VPS
28
[fMRI] Cognitive signature ~ single trial
28.1
PVC all task comparison
28.2
Cognitive only Stim x cue interaction
28.2.1
2x3 stimulus intensity * cue
28.2.2
Linear model
28.2.3
Cog stimulus intensity Cohen’s d = 0.72
28.2.4
Cognitive stimulus & cue effect size: stim_d = 0.73, cue_d = 0.069
28.2.5
Lineplots
28.2.6
Linear model with Stim x Cue x Expectation rating
28.2.7
Session 1: 2x3 stimulus intensity * cue
28.2.8
Session 3: 2x3 stimulus intensity * cue
28.2.9
Session 4: 2x3 stimulus intensity * cue
28.3
Cognitive only: Outcome ratings & Kragel 2018
28.3.1
outcome ratings * cue
28.4
Cognitive only: Expectation ratings & NPS
29
[fMRI] Single trial correlation between cue and stim
29.1
Stack data
29.2
plot correlation (one-sample-t)
29.3
Lineplot
30
[fMRI] Signature effect size ~ single trial
30.1
effeect size
30.2
contrastt (stim intensity)
30.3
layer in metadata
31
VIF
32
load in the datamatrix and calculate the vif
33
vif
34
[fMRI] Cognitive signature ~ single trial
34.1
load behavioral data
34.2
dfmerge stats
34.3
expect & NPS as a function of cue
34.4
2x3 stim*cue
34.5
lineplot
35
[fMRI] Vicarious signature ~ single trial
35.1
load behavioral data
35.2
expect & NPS as a function of cue
35.3
2x3 stim*cue
36
[fMRI] Pain signature ~ single trial
36.1
load behavioral data
36.2
expect & NPS as a function of cue
36.3
2x3 stim*cue
36.4
lineplots
Published with bookdown
behavioral_ICC
Chapter 31
VIF
title: "VIF" author: "Heejung Jung" date: "2023-02-08"