Data for Experiment 1 - Rosenbaum, Grassie & Hartley, 2022
Compiled by Gail Rosenbaum on 1/15/2022
gailrosenbaum@gmail.com

Notes:
- The Risk-Sensitive Temporal Difference (RSTD) model is sometimes referred to as “TDRS” in variable names
- These files include AIC as model-fit metrics. We ultimately reported BIC instead of AIC in the manuscript. The analysis file computes BIC based on AIC values

Data dictionary for all data files in this directory

# BestFitParams - best fitting model parameters for Experiment 1 participants

SubjectNumber: subject number
Age: Age on participation date
TD_Alpha: best-fitting alpha value from the TD model
TD_Beta: best-fitting beta value from the TD model
TD_AIC: AIC of the TD model with best-fitting parameters for this subject
TDRS_AlphaPos: best-fitting alpha positive value from the RSTD model
TDRS_AlphaNeg: best-fitting alpha negative value from the RSTD model
TDRS_Beta: best-fitting beta value from the RSTD model
TDRS_AsymmIdx: Asymmetry Index based on TDRS_AlphaPos and TDRS_AlphaNeg
TDRS_AIC: AIC of the RSTD model with best-fitting parameters for this subject
Util_Alpha: best-fitting alpha value from the Utility model
Util_Beta: best-fitting beta value from the Utility model
Util_Rho: best-fitting rho value from the Utility model
Util_AIC: AIC of the Utility model with best-fitting parameters for this subject
FourLR_AlphaPos: best-fitting alpha positive for free choices from the FourLR model 
FourLR_AlphaNeg: best-fitting alpha negative for free choices from the FourLR model 
FourLR_Beta: best-fitting beta value for free choices from the FourLR model 
FourLR_AlphaPos2: best-fitting alpha positive for forced choices from the FourLR model 
FourLR_AlphaNeg2: best-fitting alpha negative for forced choices from the FourLR model 
FourLR_AIC: AIC of the FourLR model with best-fitting parameters for this subject

# MachineEVs - expected values of point machines

TrialType: trial type code
M1: Machine 1; A = 100% chance of 0 points; B = 100% chance of 20 points; C = 100% chance of 40 points; D = 50%/50% 0/40 points; E =  50%/50% 0/80 points
M1EV: expected value of Machine 1
M2: Machine 2; A = 100% chance of 0 points; B = 100% chance of 20 points; C = 100% chance of 40 points; D = 50%/50% 0/40 points; E =  50%/50% 0/80 points
M2EV: expected value of Machine 2
EVDiff: difference between M2EV and M1EV

# MemDF - trial-by-trial RPEs based on the RSTD model; variables below are those that differ from MemDF_Util variables

SubjectNumber: subject number
Age: Age on participation date
TrialNum: Trial number in the Risk-Sensitive Reinforcement Learning task
AlphaPos: best-fitting alpha positive value from the RSTD model
AlphaNeg: best-fitting alpha negatives value from the RSTD model
Beta_TDRS: best-fitting beta value from the RSTD model
UnshuffledResp: Choice in the Risk-Sensitive Reinforcement Learning task (machine 1 or machine 2; which specific machine was chosen can be found by merging MachineEVs file with this file, matching FullTrialType in this file to TrialType in MachineEVs)
MemResp: Response in Surprise Memory test; 5 = definitely old, 6 = maybe old, 7 = maybe new, 8 = defniitely new
RespOld: binarized version of MemResp; 1 = old, 0 = new
TrialType: Simplified trial type (vs. FullTrialType); 1 = risky, 2 = forced, 3 = test
FullTrialType: Trial type from the Risk-Sensitive Reinforcement Learning task; corresponds to TrialType in MachineEVs
Outcome: choice outcome in the Risk-Sensitive Reinforcement Learning task
RiskyResp: Did they make a risky choice? (this is only set to 1 if they made a risky choice on a “risky” trial type)
AnyRisk: Did they make a risky choice? (this variable is set to 1 if they chose a risky machine regardless of the trial type, e.g., even if they were forced to choose a risky machine)
RPE: the RPE for the trial based on the RSTD model
AbsRPE: the absolute value of RSTD RPE
PositiveRPE: the valence of RSTD RPE, 0 = negative or 0, 1 = positive
MemIdx: Trial number on the Surprise Memory test
RT: Response time in the Risk-Sensitive Reinforcement Learning task

# MemDF_Util - trial-by-trial RPEs based on the utility model; variables below are those that differ from MemDF variables

Alpha_Util: best-fitting alpha value from the Utility model
Beta_Util: best-fitting beta value from the Utility model
Rho_Util: best-fitting rho value from the Utility model
RPE: the RPE for the trial based on the Utility model
AbsRPE: the absolute value of Utility RPE
PositiveRPE: the valence of Utility RPE, 0 = negative or 0, 1 = positive

# ModelRecov data - model recovery; one file for each model (TD, RSTD, Util, FourLR)

These files each contain model metrics for 10,000 simulated subjects. Subjects in ModelRecov_TD were generated using the TD model, subjects in ModelRecov_RSTD were generated using the RSTD model, and so on. Variables common to all files:
Array: A counter variable I used 
SimSub: Simulated subject number for this model
AIC_TD: AIC value when simulated data were fit by the TD model, regardless of the generating model
AIC_TDRS: AIC value when simulated data were fit by the RSTD model, regardless of the generating model
AIC_Util: AIC value when simulated data were fit by the Utility model, regardless of the generating model
AIC_FourLR: AIC value when simulated data were fit by the FourLR model, regardless of the generating model

Each of the files also contains variables that begin with “Real” and variables that begin with “Rec.” “Real” variables (e.g., “RealAlpha”) are the model parameters used to generate the data. “Rec” variables (e.g., “RecAlpha”) are the best-fitting model parameters when the simulated data were fit by the same model used to generate the data. E.g., for subject 1 in  ModelRecov_TD, data were generated by the TD model with alpha = .457 and beta = 9.688. When we fit the simulated data for this subject with the TD model, the best-fitting parameters were alpha = .421 and beta = 7.666.

For the FourLR model, AlphaPos and AlphaNeg refer to parameters for free choices, while AlphaPos2 and AlphaNeg2 are for forced choices. 

# widedf - summary variables for decision making, memory and questionnaires

SubjectNumber: subject number
Age: Age on participation date
WASI: WASI score
Hit: proportion of hits in the surprise memory test
FA: proportion of false alarms in the surprise memory test
dPrime: d-prime in the surprise memory test
MeanRiskAll: mean risk taking on all “risk” trials (both equal and unequal EV) for the full task
MeanRiskBlockEqEV1: mean risk taking on all equal-EV “risk” trials within the first 1/3 of the task
MeanRiskBlockEqEV2: mean risk taking on all equal-EV “risk” trials within the second 1/3 of the task
MeanRiskBlockEqEV3: mean risk taking on all equal-EV “risk” trials within the third 1/3 of the task
MeanRiskOverallEqEV: mean risk taking on all equal-EV “risk” trials for the full task
MeanRiskBlockUnEqEV1: mean risk taking on all unequal-EV “risk” trials within the first 1/3 of the task
MeanRiskBlockUnEqEV2: mean risk taking on all unequal-EV “risk” trials within the second 1/3 of the task
MeanRiskBlockUnEqEV3: mean risk taking on all unequal-EV “risk” trials within the third 1/3 of the task
MeanRiskOverallUnEqEV: mean risk taking on all unequal-EV “risk” trials for the full task
MeanTestBlock1: mean accuracy on “test” trials within the first 1/3 of the task
MeanTestBlock2: mean accuracy on “test” trials within the second 1/3 of the task
MeanTestBlock3: mean accuracy on “test” trials within the third 1/3 of the task
MeanTestBlocks2and3: mean accuracy on “test” trials for the last 2/3 of the task 
UsableData: was MeanTestBlocks2and3 > .6? this is 1 for everyone in this dataset (this was an inclusion criterion)
Tanner: Tanner score (Udry & Morris, 1980) (not reported in manuscript); self reported in participants age 10, and older reported by parents in participants under age 10
DOSPERT: score on age-appropriate version of the Domain-Specific Risk Taking Scale (DOSPERT) scale (Blais & Weber, 2006; Figner, Garrote, Kotabe, & Weber, 2010)
BIS11: score on a a modified version of the Barratt Impulsiveness Scale, Version 11 (BIS-11; Patton et al., 1995; Steinberg, et al., 2008) (not reported in manuscript)
BAS_Drive: Drive subscale score on Behavioral Activation Scale (Carver and White, 1994) (not reported in manuscript)
BAS_Fun_Seeking: Fun seeking subscale score on Behavioral Activation Scale (Carver and White, 1994) (not reported in manuscript)
BAS_Reward_Response: Reward responsiveness subscale score on Behavioral Activation Scale (Carver and White, 1994) (not reported in manuscript)
BAS_total: total score on Behavioral Activation Scale (Carver and White, 1994) (not reported in manuscript)
BIS: total score on Behavioral Activation Scale (Carver and White, 1994) (not reported in manuscript)
Gender: self-reported gender
Reward: number of “points” earned in the decision-making task
RiskySafeAcc: mean accuracy for explicit learning Question 1: Did this machine always give you the same number of points, or did it sometimes give 0 points and sometimes give you more points?
ValueAcc: mean accuracy for explicit learning Question 2: How many points did this machine give you each time you chose it? (if the participant responded that it was safe in Question 1) OR How many points did this machine give you when it did not give 0 points?

# PPC_RSTD - Posterior predictive check data for the RSTD model

For each real participant, we simulated 100 fake participants with the participant’s best-fitting parameters from the RSTD model. This file is a summary of those 100 fake participants for each real participant.

SubjectNumber: subject number
Age: Age on participation date
AlphaPos: best-fitting alpha positive from the RSTD model for this real participant
AlphaNeg: best-fitting alpha negative from the RSTD model for this real participant
Beta: best-fitting beta from the RSTD model for this real participant
AsymmIdx: asymmetry index based on AlphaPos and AlphaNeg
MeanRiskAll_sim: for all of the mean risk taking on all “risk” trials (both equal and unequal EV) for the full task - mean across 100 simulated subjects
MeanRiskBlockEqEV1_sim: mean risk taking on all equal-EV “risk” trials within the first 1/3 of the task - mean across 100 simulated subjects
MeanRiskBlockEqEV2_sim: mean risk taking on all equal-EV “risk” trials within the second 1/3 of the task - mean across 100 simulated subjects
MeanRiskBlockEqEV3_sim: mean risk taking on all equal-EV “risk” trials within the third 1/3 of the task - mean across 100 simulated subjects
MeanRiskOverallEqEV_sim: mean risk taking on all equal-EV “risk” trials for the full task - mean across 100 simulated subjects
MeanRiskBlockUnEqEV1_sim: mean risk taking on all unequal-EV “risk” trials within the first 1/3 of the task - mean across 100 simulated subjects
MeanRiskBlockUnEqEV2_sim: mean risk taking on all unequal-EV “risk” trials within the second 1/3 of the task - mean across 100 simulated subjects
MeanRiskBlockUnEqEV3_sim: mean risk taking on all unequal-EV “risk” trials within the third 1/3 of the task - mean across 100 simulated subjects
MeanRiskOverallUnEqEV_sim: mean risk taking on all unequal-EV “risk” trials for the full task - mean across 100 simulated subjects
MeanTestBlock1_sim: mean accuracy on “test” trials within the first 1/3 of the task - mean across 100 simulated subjects
MeanTestBlock2_sim: mean accuracy on “test” trials within the second 1/3 of the task - mean across 100 simulated subjects
MeanTestBlock3_sim: mean accuracy on “test” trials within the third 1/3 of the task - mean across 100 simulated subjects
MeanTestBlocks2and3_sim: mean accuracy on “test” trials for the last 2/3 of the task  - mean across 100 simulated subjects
UsableData_sim: was MeanTestBlocks2and3 > .6? - mean across 100 simulated subjects
MeanRiskEq040_sim: mean risk taking on choices between safe 20 point and risky 0/40 point machines - mean across 100 simulated subjects
MeanRiskEq080_sim: mean risk taking on choices between safe 40 point and risky 0/80 point machines - mean across 100 simulated subjects

# PPC_Util - Posterior predictive check data for the RSTD model

For each real participant, we simulated 100 fake participants with the participant’s best-fitting parameters from the Utility model. This file is a summary of those 100 fake participants for each real participant.

The variables in this file are the same as those in PPC_RSTD, except they’re based on the Utility-simulated subjects instead of the RSTD-simulated subjects. Alpha, Beta and Rho indicate the best fitting parameters for the real participant in the utility model