## Data files
### Part 1: New data collection
Files include:
#### 1) Behavioral data file (*vicData.csv*)
- Variables:
1) riskyGain - risky gain option ($)
2) riskyLoss - risky loss option ($)
3) alternative - guaranteed alternative ($)
4) EV - expected value of the gamble
5) groundEV - expected value level of the current trial (expected value of gamble and safe)
6) shift - shift value ($EV)
7) runlength - number of trials in the run of the current trial
8) choice - accept gamble (1), reject gamble (0)
9) outcome - outcome amount ($)
10) subjectIndex - subject ID
11) RT - reaction time
12) itiTime - variable intertrial interval (not including leftover time from response window)
#### 2) Scored SCR following outcomes (*vicSCRdataUpdatedOC.csv*)
- Variables:
1) trial number
2) amplitude (microsiemens)
3) subject ID
#### 3) Scored SCR during decision phase (*mergedVICDecision.csv*)
- Variables:
1) subject ID
2) trial number
3) amplitude (microsiemens)
#### 4) Demographic information (*VICageGender.csv*)
- Variables:
1) age (years)
2) gender identity
3) subject ID
### Part 2: Reanalysis of Sokol-Hessner et al (2015) Determinants of Propranolol’s Selective Effect on Loss Aversion, *Psychological Science, 26(7),* 1123-1130. Data also available https://osf.io/i5knh/
#### 1) Behavioral data (*alldata.csv*)
- Variables:
1) subject ID
2) session (1 or 2)
3) alternative
4) risky loss option ($)
5) risky gain option ($)
6) possible outcome - predetermined outcome if gamble accepted
7) choice (accept gamble = 1, reject gamble = 2)
8) reaction time (ms)
#### 2) Demographic data 1 (*demoData.csv*)
- Variables:
1) number of days between sessions
2) gender (male=0; female=1)
3) Body mass index (BMI)
#### 3) Demographic data 2 (*PDM_Gender+Age.csv*)
- Variables:
1) subject ID
2) gender (male=M; female=F)
3) Age (years)
#### 4) Propranolol session information (*pdmcounterbalancing.txt*)
- Variables:
1) subject ID
2) condition day 1: propranolol = 1, placebo = 2
3) condition day 2: propranolol = 1, placebo = 2
## Analysis scripts
All data cleaning and analysis scripts can be accessed on the Sokol-Hessner Lab's Github: https://github.com/sokolhessnerlab/vic
