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rcsBasicAnalysis.Rmd
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rcsBasicAnalysis.Rmd
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---
title: "RCS Basic Analysis"
author: "Hayley Brooks"
date: "2022-10-12"
output: html_document
---
```{r setup, include=FALSE}
rm(list=ls())
library('config')
config = config::get()
library('lmerTest')
library(dplyr)
setup_source = file.path(config$path$code_files$dataSetUp) # run our set up script (which loads all the data)
source(setup_source) #, local = knitr::knit_global())
sem <- function(x) sd(x)/sqrt(length(x)); # set up sem function for later analyses
```
#### How many participants are in each condition/order?
```{r conditions, echo=FALSE}
cat("participants in natural-natural condition:",sum(rcsSubLevelWide_clean$condCode==1), "\nparticipants in natural-strategy condition:",sum(rcsSubLevelWide_clean$condCode==2), "\nparticipants in strategy-natural condition:",sum(rcsSubLevelWide_clean$condCode==3),"\nparticipants in strategy-strategy condition:", sum(rcsSubLevelWide_clean$condCode==4))
condcode1 = rcsSubLevelWide_clean$subID[rcsSubLevelWide_clean$condCode==1]
condcode2 = rcsSubLevelWide_clean$subID[rcsSubLevelWide_clean$condCode==2]
condcode3 = rcsSubLevelWide_clean$subID[rcsSubLevelWide_clean$condCode==3]
condcode4 = rcsSubLevelWide_clean$subID[rcsSubLevelWide_clean$condCode==4]
```
#### Demographic
```{r demographic-info}
# Race code: 1 = white, 2 = Black or African American, 3 = American Indian and Alaskan Native, 4 = Asian, 5 = Native Hawaiian and Other Pacific Islander, 6 = Two or more races, 7= other, 8 = Decline to answer
cat("RACE: \nWhite:", sum(rcsSubLevelWide_clean$raceCode==1),
"\nBlack or African American:",sum(rcsSubLevelWide_clean$raceCode==2),
"\nAmerican Indian and Alaskan Native:",sum(rcsSubLevelWide_clean$raceCode==3),
"\nAsian:", sum(rcsSubLevelWide_clean$raceCode==4),
"\nNative Hawaiian and Other Pacific Islander:", sum(rcsSubLevelWide_clean$raceCode==5),
"\nTwo or more races:", sum(rcsSubLevelWide_clean$raceCode==6),
"\nOther:", sum(rcsSubLevelWide_clean$raceCode==7),
"\nDecline to answer:", sum(rcsSubLevelWide_clean$raceCode==8))
# Ethnicity code: 1 = Hispanic or Latino; 2 = Not Hispanic or Latino
cat("ETHNICITY: \nHispanic or Latino:", sum(rcsSubLevelWide_clean$ethnicityCode==1),
"\nNon-Hispanic or Latino:",sum(rcsSubLevelWide_clean$ethnicityCode==2))
# Gender code: 1 = Male, 2 = Female, 3 = Trans Male, 4 = Trans Female, 5 = Gender nonconforming, 6 = other, 7 = Decline to answer
cat("GENDER: \nMale:", sum(rcsSubLevelWide_clean$genderCode==1),
"\nFemale:",sum(rcsSubLevelWide_clean$genderCode==2),
"\nTrans Male:",sum(rcsSubLevelWide_clean$genderCode==3),
"\nTrans Female:", sum(rcsSubLevelWide_clean$genderCode==4),
"\nGender nonconforming:", sum(rcsSubLevelWide_clean$genderCode==5),
"\nOther:", sum(rcsSubLevelWide_clean$genderCode==6),
"\nDecline to answer:", sum(rcsSubLevelWide_clean$genderCode==7))
# Age:
cat("AGE (years): \nmean =", mean(rcsSubLevelWide_clean$age),
"\nrange =", range(rcsSubLevelWide_clean$age),
"\nmedian =", median(rcsSubLevelWide_clean$age))
```
#### Average change in risk-taking across conditions
```{r, risk-taking-across-rounds, echo=FALSE}
# What is the overall p(gamble) in each round (not separating by condition)
# some basic pgamble stuff
summary(rcsSubLevelWide_clean$round1_pgamble)
# range: 0.007692-0.885496; median: 0.538168 mean: 0.533754
summary(rcsSubLevelWide_clean$round2_pgamble)
# range: 0.0229-0.9160 median: 0.5249 mean: 0.5343
t.test(rcsSubLevelWide_clean$round1_pgamble, rcsSubLevelWide_clean$round2_pgamble, paired = T); # no significant difference in mean pgamble across rounds
# differences in pgam from round 1 to round 2:
pgamDiff = rcsSubLevelWide_clean$round1_pgamble - rcsSubLevelWide_clean$round2_pgamble
# Risk-taking slightly decreased from round 1 to round 2, but the difference is not significant.
# What does the change in risk-taking from round 1 to round 2 look like for the different conditions?
# CONTROL- CONTROL
pgamDiffnatNat=mean(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode1)] - rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode1)]);
cat("\n\nDifference in risk-taking across rounds \nnatural(round1) - natural (round2)\ndiff p(gamble)",pgamDiffnatNat);
# Risk-taking decreases from round 1 to round 2 in people are told to act natural in both rounds.
# CONTROL- STRATEGY
pgamDiffnatStrat = mean(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode2)] - rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode2)]);
cat("\n\nDifference in risk-taking across rounds \nnatural(round1) - strategy (round2)\ndiff p(gamble)",pgamDiffnatStrat);
# Risk-taking increases from round 1 to round 2 when people go from act natural to strategy condition.
# STRATEGY-CONTROL
pgamDiffstratNat = mean(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode3)] - rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode3)]);
cat("\n\nDifference in risk-taking across rounds \nstrategy(round1) - natural (round2)\ndiff p(gamble)",pgamDiffstratNat);
# Risk-taking decreases from round 1 to round 2 when people go from strategy to natural.
# STRATEGY-STRATEGY
pgamDiffstratStrat = mean(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode4)] - rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$subID %in% as.numeric(condcode4)]);
cat("\n\nDifference in risk-taking across rounds \nstrategy(round1) - strategy (round2)\ndiff p(gamble)",pgamDiffstratStrat);
# Risk-taking increases from round 1 to round 2 when people do strategy in both rounds.
# SUMMARY: Generally, more risk-taking in the strategy condition when people do both natural and strategy (regardless of order). When people repeat act natural, they take less risks across time and when people repeat strategy, they take more risks over time.
pdf(file.path(config$path$directory, config$path$shlab_figures,'pgamByRound_condition.pdf'))
par(pty="s", mfrow=c(2,2), mar = c(5,4,5,2))
plot(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==1], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==1], asp=1,col = "blue", pch=16, ylim=c(0,1), xlim=c(0,1), ylab="p(gamble round 1)", xlab="p(gamble round 2)", axes=F, cex=1.5, main=sprintf("Natural-Natural\npgam diff = %.2f",pgamDiffnatNat))
abline(a = 0, b=1, lty="dashed", col="darkgrey",lwd=5)
axis(1, lwd=6)
axis(2, lwd=6)
plot(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==2], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==2], asp=1,col = "green", pch=16, ylim=c(0,1), xlim=c(0,1), ylab="p(gamble round 1)", xlab="p(gamble round 2)", axes=F, cex=1.5, main=sprintf("Natural-Strategy\npgam diff = %.2f",pgamDiffnatStrat))
abline(a = 0, b=1, lty="dashed", col="darkgrey",lwd=5)
axis(1, lwd=6)
axis(2, lwd=6)
plot(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==3], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==3],col = "red", pch=16, ylim=c(0,1), xlim=c(0,1), ylab="p(gamble round 1)", xlab="p(gamble round 2)", asp =1, axes=F, cex=1.5, main=sprintf("Strategy-Natural\npgam diff = %.2f",pgamDiffstratNat))
abline(a = 0, b=1, lty="dashed", col="darkgrey",lwd=5)
axis(1, lwd=6)
axis(2, lwd=6)
plot(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==4], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==4], asp=1,col = "purple", pch=16, ylim=c(0,1), xlim=c(0,1), ylab="p(gamble round 1)", xlab="p(gamble round 2)", axes=F, cex=1.5, main=sprintf("Strategy-Strategy\npgam diff = %.2f",pgamDiffstratStrat))
abline(a = 0, b=1, lty="dashed", col="darkgrey",lwd=5)
axis(1, lwd=6)
axis(2, lwd=6)
mtext("Change in risk-taking", side = 3, line = -1, outer = TRUE)
dev.off();
```
Another version of plots above: Not super helpful plots with full dataset
```{r plot-basic-pgamacrossall, echo=FALSE}
par(pty="s")
plot(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==1], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==1], asp=1,col = rgb(red=0, green=0, blue=1, alpha = 0.4), pch=16, ylim=c(0,1), xlim=c(0,1), ylab="p(gamble round 1)", xlab="p(gamble round 2)", axes=F, cex=2)
abline(a = 0, b=1, lty="dashed", col="darkgrey",lwd=5)
points(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==2], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==2], col = rgb(red=0, green=1, blue=0, alpha = 0.4),asp=1, pch=16,cex=2)
points(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==3], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==3], asp=1, col = rgb(red=1, green=0, blue=0, alpha = 0.4), pch=16, cex=2)
points(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==4], rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==4], asp= 1,col = rgb(red=.5, green=0, blue=.5, alpha = 0.4), pch=16, cex=2)
axis(1, lwd=6)
axis(2, lwd=6)
legend("bottomright", legend=c("nat-nat","nat-strat", "strat-nat" ,"strat-strat"), pch=16, bty="n", col=c("blue", "green", "red", "purple"), cex=1)
plot(c(rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==2]- rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==2], rcsSubLevelWide_clean$round1_pgamble[rcsSubLevelWide_clean$condCode==3]- rcsSubLevelWide_clean$round2_pgamble[rcsSubLevelWide_clean$condCode==3]), pch=16, ylab="Risk-taking\n (round 1 - round 2)", xlab="participant", ylim=c(-.5,.5),cex= 2,col=c(rep("green",length(condcode2)), rep("red", length(condcode3))), axes=F)
axis(1, at =1:sum(rcsSubLevelWide_clean$condCode %in% c(2,3)), labels = as.numeric(c(rcsSubLevelWide_clean$subID[rcsSubLevelWide_clean$condCode==2], rcsSubLevelWide_clean$subID[rcsSubLevelWide_clean$condCode==3])), lwd=6, cex.axis=1)
axis(2, lwd=6, cex=1.5)
abline(h=0, col="darkgrey", lty="dashed", lwd=4)
legend("bottomright", legend=c("nat-strat", "strat-nat"), pch=16, bty="n", col=c("green","red"), cex=1)
```
#### Post round difficulty ratings
```{r instDifficulty-ratings}
# Participants rated how difficult the instructions were to follow on a scale from 1-100
# ACROSS CONDITIONS
summary(rcsSubLevelLong_clean$instDifficult[rcsSubLevelLong_clean$strategy==0]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 12.89 25.83 30.95 49.80 79.59
sd(rcsSubLevelLong_clean$instDifficult[rcsSubLevelLong_clean$strategy==0]); # 22.17784
summary(rcsSubLevelLong_clean$instDifficult[rcsSubLevelLong_clean$strategy==1]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 11.62 27.49 34.18 56.98 99.80
sd(rcsSubLevelLong_clean$instDifficult[rcsSubLevelLong_clean$strategy==1]); # 26.37377
# Participants rated strategy instructions as more difficult, on average, relative to control. This does not account for condition/order
summary(rcsSubLevelLong_clean$instDifficult[rcsSubLevelLong_clean$roundRDM==-1]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 11.01 25.34 30.99 50.61 99.80
summary(rcsSubLevelLong_clean$instDifficult[rcsSubLevelLong_clean$roundRDM==1]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 14.16 27.64 34.14 56.20 92.58
# NATURAL-NATURAL
natNatRound1difficulty = rcsSubLevelWide_clean$round1_instDifficult[rcsSubLevelWide_clean$condCode==1];
natNatRound2difficulty = rcsSubLevelWide_clean$round2_instDifficult[rcsSubLevelWide_clean$condCode==1];
cond1difficultydiff = mean(natNatRound1difficulty - natNatRound2difficulty);
# round 1, natural-natural
summary(natNatRound1difficulty);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 12.79 22.75 27.48 38.13 64.36
# round 2, natural-natural
summary(natNatRound2difficulty)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.758 18.457 39.746 36.489 56.494 79.590
t.test(natNatRound1difficulty, natNatRound2difficulty, paired=T); # significant difference, p = .02
# Difficulty ratings significantly higher in round 2 for people repeating natural.
# NATURAL-STRATEGY
natStratRound1difficulty = rcsSubLevelWide_clean$round1_instDifficult[rcsSubLevelWide_clean$condCode==2];
natStratRound2difficulty = rcsSubLevelWide_clean$round2_instDifficult[rcsSubLevelWide_clean$condCode==2];
cond2difficultydiff = mean(natStratRound1difficulty - natStratRound2difficulty);
summary(natStratRound1difficulty);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 11.23 29.20 31.55 50.88 77.64
summary(natStratRound2difficulty);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 2.637 16.699 33.691 41.777 66.602 92.578
t.test(natStratRound1difficulty, natStratRound2difficulty, paired=T); # trending difference, p = .09
# Difficulty higher in round 2 for people who start with natural and do strategy.
# STRATEGY-NATURAL
stratNatRound1difficulty = rcsSubLevelWide_clean$round1_instDifficult[rcsSubLevelWide_clean$condCode==3];
stratNatRound2difficulty = rcsSubLevelWide_clean$round2_instDifficult[rcsSubLevelWide_clean$condCode==3];
cond3difficultydiff = mean(stratNatRound1difficulty - stratNatRound2difficulty);
summary(stratNatRound1difficulty);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.074 8.398 24.707 31.096 51.465 99.805
summary(stratNatRound2difficulty);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 13.53 24.22 28.30 37.11 76.17
t.test(stratNatRound1difficulty, stratNatRound2difficulty, paired=T); # n.s, p = .64
# Difficulty ratings are slightly lower for natural after strategy The difference is not significant and much smaller than people who go from natural to strategy
# STRATEGY-STRATEGY
stratStratRound1difficulty = rcsSubLevelWide_clean$round1_instDifficult[rcsSubLevelWide_clean$condCode==4];
stratStratRound2difficulty = rcsSubLevelWide_clean$round2_instDifficult[rcsSubLevelWide_clean$condCode==4];
cond4difficultydiff = mean(stratStratRound1difficulty - stratStratRound2difficulty)
summary(stratStratRound1difficulty);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 13.23 30.76 33.84 57.37 87.11
summary(stratStratRound2difficulty);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 2.930 9.521 23.828 30.015 41.602 86.816
t.test(stratStratRound1difficulty, stratStratRound2difficulty, paired=T); # n.s, p = .35
# Difficulty ratings are slightly lower in round 2 for people who do strat-strat but the difference is not significant. This is not consistent with people who repeat nat-nat who found second round as more difficult.
# These ratings are odd, there isn't a clear pattern here. What about looking at strat vs nat in round 2 for people who experienced natural first and for people who experience strategy first
plot(c(1,2), c(mean(natNatRound1difficulty), mean(natNatRound2difficulty)), pch = 16, col="red", ylim=c(20,50), ylab="difficulty ratings", xlab="condition\nred = nat-nat\n blue=nat-strat", main="Difficulty ratings as fx of round 1 natural", axes=F);
points(c(1,2), c(mean(natStratRound1difficulty), mean(natStratRound2difficulty)), pch = 16, col="blue");
axis(1, at=c(1,2), label=c("round 1", "round 2"))
axis(2, at=c(20,30,40,50))
plot(c(1,2), c(mean(stratNatRound1difficulty), mean(stratNatRound2difficulty)), pch = 16, col="red", ylim=c(20,50), ylab="difficulty ratings", xlab="condition\nred = strat-nat\n blue=strat-strat", main="Difficulty ratings as fx of round 1 strategy", axes=F);
points(c(1,2), c(mean(stratStratRound1difficulty), mean(stratStratRound2difficulty)), pch = 16, col="blue");
axis(1, at=c(1,2), label=c("round 1", "round 2"))
axis(2, at=c(20,30,40,50))
# These plots suggest that if you start with natural, anything after that including natural is more difficult. If you start with strategy, anything after that is less difficult (inc. repeating strategy condition)
# Overall, these results suggests that we need to be careful about how we use the difficulty ratings because they are super variable and aren't as straightforward as we expected It is possible that mean is not the best measure.
# it might worth looking at change in ratings instead of actual ratings
rcsSubLevelLong_clean$strategyRecode = rcsSubLevelLong_clean$strategy
rcsSubLevelLong_clean$strategyRecode[rcsSubLevelLong_clean$strategyRecode==0]= -1
rcsSubLevelLong_clean$rdmRound = rep(c(-1,1), times = nSub)
diffRoundStrat = lmer(instDifficult~strategyRecode*rdmRound + (1|subID), data=rcsSubLevelLong_clean); # no effect of round or strategy, no interaction on difficulty ratings in linear mixed effects model
# What about comparing natural vs. strategy in round 1 only (N=62 per group)
natFirst = rcsSubLevelWide_clean$round1_instDifficult[rcsSubLevelWide_clean$condCode %in% c(1,2)]; # people who do nat first
summary(natFirst);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 11.23 23.58 29.51 49.61 77.64
stratFirst = rcsSubLevelWide_clean$round1_instDifficult[rcsSubLevelWide_clean$condCode %in% c(3,4)]; # people who do strat first
summary(stratFirst);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.000 9.912 26.709 32.468 55.688 99.805
t.test(natFirst, stratFirst); # ns, p= .5
# No significant difference between difficulty ratings in natural and strategy for people who do natural and strategy first.
# Plot round 1 vs round 2 difficulty ratings for each condition:
pdf(file.path(config$path$directory, config$path$shlab_figures,'InstDifficultyRatingPlots.pdf'))
par(pty="s", mfrow= c(2,2), mar = c(5,4,5,2))
plot(natNatRound1difficulty, natNatRound2difficulty, ylim=c(0,100), xlim=c(0,100), ylab="Act natural Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="blue", main=sprintf("Natural-Natural\ndifficulty diff = %.2f",cond1difficultydiff))
abline(a=0, b=1, col="grey")
plot(natStratRound1difficulty, natStratRound2difficulty, ylim=c(0,100), xlim=c(0,100), ylab="Strategy Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="green",main=sprintf("Natural-Strategy\ndifficulty diff = %.2f",cond2difficultydiff))
abline(a=0, b=1, col="grey")
plot(stratNatRound1difficulty, stratNatRound2difficulty, ylim=c(0,100), xlim=c(0,100), ylab="Act Natural Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="red", main=sprintf("Strategy-Natural\ndifficulty diff = %.2f",cond3difficultydiff))
abline(a=0, b=1, col="grey")
plot(stratStratRound1difficulty, stratStratRound2difficulty, ylim=c(0,100), xlim=c(0,100), ylab="Strategy Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="purple", main=sprintf("Strategy-Strategy\ndifficulty diff = %.2f",cond4difficultydiff))
abline(a=0, b=1, col="grey")
mtext("Instruction Difficulty Ratings", side = 3, line = -1, outer = TRUE)
dev.off()
```
#### Post round how often/frequency ratings
```{r instFrequency-ratings}
# Participants reported how often they were able to follow instructions in each round of the rdm task
# FREQUENCY RATINGS FOR INSTRUCTION TYPE OVERALL (not accounting for order/condition)
summary(rcsSubLevelLong_clean$instHowOften[rcsSubLevelLong_clean$strategy==0]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 18.36 63.40 75.39 75.17 91.46 99.90
sd(rcsSubLevelLong_clean$instHowOften[rcsSubLevelLong_clean$strategy==0]); # 18.71106
summary(rcsSubLevelLong_clean$instHowOften[rcsSubLevelLong_clean$strategy==1]);
sd(rcsSubLevelLong_clean$instHowOften[rcsSubLevelLong_clean$strategy==1]); # 19.90221
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 22.17 63.45 79.30 75.06 90.72 99.90
# Participants were able to follow instructions about the same across instructions
#FREQUENCY RATINGS FOR INSTRUCTIONS ACROSS ROUNDS
summary(rcsSubLevelWide_clean$round1_instHowOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 18.36 67.31 79.98 76.66 91.46 99.71
summary(rcsSubLevelWide_clean$round2_instHowOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 22.17 60.50 75.20 73.57 90.87 99.90
# participants reported following instructions less in round 2 but difference is small
# NATURAL-NATURAL
natNatRound1howOften = rcsSubLevelWide_clean$round1_instHowOften[rcsSubLevelWide_clean$condCode==1];
natNatRound2howOften = rcsSubLevelWide_clean$round2_instHowOften[rcsSubLevelWide_clean$condCode==1];
cond1freqdiff = mean(natNatRound1howOften-natNatRound2howOften);
summary(natNatRound1howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 18.36 66.70 76.66 73.10 90.09 98.73
summary(natNatRound2howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 38.18 58.40 70.90 72.31 90.38 97.66
t.test(natNatRound1howOften,natNatRound2howOften, paired= T); #not significant p =.8
# Participants followed instructions about the same amount of time in both natural rounds.
# NATURAL-STRATEGY
natStratRound1howOften = rcsSubLevelWide_clean$round1_instHowOften[rcsSubLevelWide_clean$condCode==2];
natStratRound2howOften = rcsSubLevelWide_clean$round2_instHowOften[rcsSubLevelWide_clean$condCode==2];
cond2freqdiff = mean(natStratRound1howOften-natStratRound2howOften);
summary(natStratRound1howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 48.44 65.14 75.49 76.87 90.82 99.71
summary(natStratRound2howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 22.17 57.03 72.75 69.22 84.81 99.90
t.test(natStratRound1howOften,natStratRound2howOften, paired= T); #significant difference p =.04
# Participants reporting following instructions less in strategy condition relative to first round of natural.
# STRATEGY-NATURAL
stratNatRound1howOften = rcsSubLevelWide_clean$round1_instHowOften[rcsSubLevelWide_clean$condCode==3];
stratNatRound2howOften = rcsSubLevelWide_clean$round2_instHowOften[rcsSubLevelWide_clean$condCode==3];
cond3freqdiff = mean(stratNatRound1howOften-stratNatRound2howOften);
summary(stratNatRound1howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 31.05 67.92 80.66 76.97 90.87 99.12
summary(stratNatRound2howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 38.77 64.31 81.54 78.40 97.61 99.90
t.test(stratNatRound1howOften,stratNatRound2howOften, paired= T); #no significant difference, p = .7
# Participants reported no significant change in difficulty when going from strategy to natural (although mean difference is in expected direction with frequency higher in natural)
# STRATEGY-STRATEGY
stratStratRound1howOften = rcsSubLevelWide_clean$round1_instHowOften[rcsSubLevelWide_clean$condCode==4];
stratStratRound2howOften = rcsSubLevelWide_clean$round2_instHowOften[rcsSubLevelWide_clean$condCode==4];
cond4freqdiff = mean(stratStratRound1howOften-stratStratRound2howOften);
summary(stratStratRound1howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 35.84 72.61 80.96 79.69 95.90 98.54
summary(stratStratRound2howOften);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 25.78 60.35 79.79 74.36 94.14 99.02
t.test(stratStratRound1howOften,stratStratRound2howOften, paired= T); #not significant difference, p = .15
# Participants reported following instructions less often in second round when repeating strategy but difference is not significant.
# Overall, participants reported following instructions between 70-80% of the time. When participants repeat conditions, frequency ratings are similar. When participants switch conditions, frequency ratings are higher for natural relative to strategy condition but this difference is only significant when going from natural to strategy.
freqRoundStrat = lmer(instHowOften~strategyRecode*rdmRound + (1|subID), data=rcsSubLevelLong_clean); # no effect of round or strategy, no interaction on difficulty ratings in linear mixed effects model
summary(freqRoundStrat); # no effect of strategy on frequency, potential effect of round (trending at p=.08), no interaction where frequency is lower in round 2 consistent with results above
# What about comparing strat vs nat in round 1 only:
natFirstFreq = rcsSubLevelWide_clean$round1_instHowOften[rcsSubLevelWide_clean$condCode %in% c(1,2)]; # people who do nat first
summary(natFirstFreq);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 18.36 64.84 76.07 74.99 91.02 99.71
stratFirstFreq = rcsSubLevelWide_clean$round1_instHowOften[rcsSubLevelWide_clean$condCode %in% c(3,4)]; # people who do strat first
summary(stratFirstFreq);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 31.05 68.48 80.81 78.33 93.46 99.12
t.test(natFirstFreq, stratFirstFreq); # ns, p= .32
# No significant difference between natural and strategy frequency ratings for people who do natural and strategy first.
pdf(file.path(config$path$directory, config$path$shlab_figures,'InstFrequencyRatingPlots.pdf'))
par(pty="s", mfrow= c(2,2),mar = c(5,4,5,2))
plot(natNatRound1howOften, natNatRound2howOften, ylim=c(0,100), xlim=c(0,100), ylab="Act natural Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="blue", main=sprintf("Natural-Natural\nfrequency diff = %.2f",cond1freqdiff))
abline(a=0, b=1, col="grey")
plot(natStratRound1howOften, natStratRound2howOften, ylim=c(0,100), xlim=c(0,100), ylab="Strategy Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="green", main=sprintf("Natural-Strategy\nfrequency diff = %.2f",cond2freqdiff))
abline(a=0, b=1, col="grey")
plot(stratNatRound1howOften, stratNatRound2howOften, ylim=c(0,100), xlim=c(0,100), ylab="Act Natural Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="red", main=sprintf("Strategy-Natural\nfrequency diff = %.2f",cond3freqdiff))
abline(a=0, b=1, col="grey")
plot(stratStratRound1howOften, stratStratRound2howOften, ylim=c(0,100), xlim=c(0,100), ylab="Strategy Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="purple", main=sprintf("Strategy-Strategy\nfrequency diff = %.2f",cond4freqdiff))
abline(a=0, b=1, col="grey")
mtext("Instruction Frequency Ratings", side = 3, line = -1, outer = TRUE)
dev.off()
```
#### Reaction time
```{r RT-mean-of-means}
# using mean reaction time with two versions - all trials vs remove fast trials
# RTmean = mean with all trials
# RTmeanClean = mean removing fast trials
# First, ran this analysis removing trials faster than 1s. There were 7600 trials across both rounds with RTs less than 1s and all 124 participants had trials with fast RTs ranging from 1 trial to 176 trials across both rounds. There were more fast trials in round 2 relative to round 1 but the range of number of missed trials per participant in round 2 was smaller. There were 3291 fast trials in round 1 and all participants have at least one fast trial and 4309 fast trials in round 2 and 121/124 participants have at least one fast trial
# Next, we did fast trials as those with RTs <500ms. This way, we are looking at trials where participants really couldn't have responded meaningfully in that period of time (still possible to respond meaningfully with RT = 1s).
fastTrialIndround1 = which(rdmDFclean$RT<.5 & rdmDFclean$roundRDM==1); # fast trials in round 1
fastTrialIndround2 = which(rdmDFclean$RT<.5 & rdmDFclean$roundRDM==2); # fast trials in round 2
subIDsFastTrialsround1 = rdmDFclean$subIDnum[fastTrialIndround1]; # subid of fast trials in round 1
subIDsFastTrialsround2 = rdmDFclean$subIDnum[fastTrialIndround2]; # subid of fast trials in round 2
length(unique(subIDsFastTrialsround1)); # 14 fast trials in round 1 and all participants have at least one fast trial
length(unique(subIDsFastTrialsround2)); # 7 fast trials in round 2 and 121/124 participants have at least one fast trial
fastTriPerSubround1 = vector();
fastTriPerSubround2 = vector();
#
#How many trials per participant were fast?:
for (s in 1:nSub) {
fastTriPerSubround1[s]= sum(subIDsFastTrialsround1==as.numeric(subIDchar[s]))
fastTriPerSubround2[s]= sum(subIDsFastTrialsround2==as.numeric(subIDchar[s]))
};
#summary(fastTriPerSubround1);
# RTs less than .5s:
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.0000 0.0000 0.0000 0.1129 0.0000 4.0000
# RTs less than 1s:
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 7.00 20.50 26.54 40.00 101.00
#summary(fastTriPerSubround2);
# RTs less than .5s:
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.00000 0.00000 0.00000 0.05645 0.00000 2.00000
# RTs less than 1s:
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.00 14.00 28.50 34.75 54.00 84.00
# ACROSS INSTRUCTION TYPE
# all trials
summary(rcsSubLevelLong_clean$RTmean[rcsSubLevelLong_clean$strategy==0]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9459 1.1345 1.3046 1.3399 1.4845 2.3205
summary(rcsSubLevelLong_clean$RTmean[rcsSubLevelLong_clean$strategy==1]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.1834 1.3807 1.3889 1.5570 2.2312
# remove fast trials
summary(rcsSubLevelLong_clean$RTmeanClean[rcsSubLevelLong_clean$strategy==0]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9459 1.1345 1.3046 1.3403 1.4845 2.3205
summary(rcsSubLevelLong_clean$RTmeanClean[rcsSubLevelLong_clean$strategy==1]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.1834 1.3807 1.3896 1.5570 2.2312
t.test(rcsSubLevelLong_clean$RTmean[rcsSubLevelLong_clean$strategy==0],rcsSubLevelLong_clean$RTmean[rcsSubLevelLong_clean$strategy==1]); #ns, p =.15
t.test(rcsSubLevelLong_clean$RTmeanClean[rcsSubLevelLong_clean$strategy==0],rcsSubLevelLong_clean$RTmeanClean[rcsSubLevelLong_clean$strategy==1]); #ns, p =.15
# Reaction times are not significantly different from each other across instruction type
# ACROSS ROUNDS
# all trials
summary(rcsSubLevelWide_clean$round1_RTmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.2170 1.4111 1.4171 1.6038 2.3205
summary(rcsSubLevelWide_clean$round2_RTmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9488 1.1198 1.3155 1.3118 1.4146 2.2312
# remove fast trials
summary(rcsSubLevelWide_clean$round1_RTmeanClean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.2170 1.4111 1.4177 1.6038 2.3205
summary(rcsSubLevelWide_clean$round2_RTmeanClean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9488 1.1198 1.3155 1.3121 1.4146 2.2312
t.test(rcsSubLevelWide_clean$round1_RTmean, rcsSubLevelWide_clean$round2_RTmean); # p = 0.001895
t.test(rcsSubLevelWide_clean$round1_RTmeanClean, rcsSubLevelWide_clean$round2_RTmeanClean); # p = 0.001823
# Reaction times significantly faster in round 2
# NATURAL-NATURAL
# All trials
natNatRound1rtmean = rcsSubLevelWide_clean$round1_RTmean[rcsSubLevelWide_clean$condCode==1];
natNatRound2rtmean = rcsSubLevelWide_clean$round2_RTmean[rcsSubLevelWide_clean$condCode==1];
cond1rtdiffmean = mean(natNatRound1rtmean- natNatRound2rtmean);
summary(natNatRound1rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9459 1.1830 1.2996 1.3499 1.5016 1.9204
summary(natNatRound2rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9861 1.0695 1.1347 1.2174 1.3385 1.7769
t.test(natNatRound1rtmean,natNatRound2rtmean, paired=T); # significant difference, p = .0001
# remove fast trials
natNatRound1rtmeanclean = rcsSubLevelWide_clean$round1_RTmeanClean[rcsSubLevelWide_clean$condCode==1];
natNatRound2rtmeanclean = rcsSubLevelWide_clean$round2_RTmeanClean[rcsSubLevelWide_clean$condCode==1];
cond1rtdiffmeanclean = mean(natNatRound1rtmeanclean- natNatRound2rtmeanclean);
summary(natNatRound1rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9459 1.1830 1.2996 1.3504 1.5016 1.9204
summary(natNatRound2rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9861 1.0735 1.1347 1.2182 1.3469 1.7769
t.test(natNatRound1rtmeanclean,natNatRound2rtmeanclean, paired=T); # significant difference, p = 0.0001261
# Participants are significantly faster in round 2 when doing natural both rounds.
# NATURAL-STRATEGY
# all trials
natStratRound1rtmean = rcsSubLevelWide_clean$round1_RTmean[rcsSubLevelWide_clean$condCode==2];
natStratRound2rtmean = rcsSubLevelWide_clean$round2_RTmean[rcsSubLevelWide_clean$condCode==2];
cond2rtdiffmean = mean(natStratRound1rtmean- natStratRound2rtmean);
summary(natStratRound1rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.008 1.235 1.418 1.471 1.625 2.320
summary(natStratRound2rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9968 1.1516 1.3546 1.3779 1.4246 2.2312
t.test(natStratRound1rtmean,natStratRound2rtmean, paired=T); # significant difference, p = .009
# remove fast trials
natStratRound1rtmeanclean = rcsSubLevelWide_clean$round1_RTmeanClean[rcsSubLevelWide_clean$condCode==2];
natStratRound2rtmeanclean = rcsSubLevelWide_clean$round2_RTmeanClean[rcsSubLevelWide_clean$condCode==2];
cond2rtdiffmeanclean = mean(natStratRound1rtmeanclean- natStratRound2rtmeanclean);
summary(natStratRound1rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.012 1.235 1.418 1.472 1.625 2.320
summary(natStratRound2rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9968 1.1516 1.3546 1.3784 1.4246 2.2312
t.test(natStratRound1rtmeanclean,natStratRound2rtmeanclean, paired=T); # significant p = 0.009351
# Participants are significantly faster in round 2 when doing natural then strategy.
# STRATEGY-NATURAL
# All trials
stratNatRound1rtmean = rcsSubLevelWide_clean$round1_RTmean[rcsSubLevelWide_clean$condCode==3];
stratNatRound2rtmean = rcsSubLevelWide_clean$round2_RTmean[rcsSubLevelWide_clean$condCode==3];
cond3rtdiffmean = mean(stratNatRound1rtmean- stratNatRound2rtmean);
summary(stratNatRound1rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.2494 1.4350 1.4397 1.6361 1.9239
summary(stratNatRound2rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9585 1.1504 1.3378 1.3208 1.4315 1.8003
t.test(stratNatRound1rtmean,stratNatRound2rtmean, paired=T); # significant difference, p =.0005
# remove fast trials
stratNatRound1rtmeanclean = rcsSubLevelWide_clean$round1_RTmeanClean[rcsSubLevelWide_clean$condCode==3];
stratNatRound2rtmeanclean = rcsSubLevelWide_clean$round2_RTmeanClean[rcsSubLevelWide_clean$condCode==3];
cond3rtdiffmeanclean = mean(stratNatRound1rtmeanclean- stratNatRound2rtmeanclean);
summary(stratNatRound1rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.2494 1.4350 1.4408 1.6361 1.9239
summary(stratNatRound2rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9585 1.1504 1.3378 1.3208 1.4315 1.8003
t.test(stratNatRound1rtmeanclean,stratNatRound2rtmeanclean, paired=T); # significant difference, p =0.000453
# participants significantly faster in round 2 when doing strategy then natural
# STRATEGY-STRATEGY
# All trials
stratStratRound1rtmean = rcsSubLevelWide_clean$round1_RTmean[rcsSubLevelWide_clean$condCode==4];
stratStratRound2rtmean = rcsSubLevelWide_clean$round2_RTmean[rcsSubLevelWide_clean$condCode==4];
cond4rtdiffmean = mean(stratStratRound1rtmean- stratStratRound2rtmean);
summary(stratStratRound1rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9161 1.2033 1.4671 1.4071 1.5740 2.0312
summary(stratStratRound2rtmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9488 1.1278 1.3546 1.3309 1.4426 2.1852
t.test(stratStratRound1rtmean,stratStratRound2rtmean, paired=T); # not significant, p =.174
# Remove fast trials
stratStratRound1rtmeanclean = rcsSubLevelWide_clean$round1_RTmeanClean[rcsSubLevelWide_clean$condCode==4];
stratStratRound2rtmeanclean = rcsSubLevelWide_clean$round2_RTmeanClean[rcsSubLevelWide_clean$condCode==4];
cond4rtdiffmeanclean = mean(stratStratRound1rtmeanclean- stratStratRound2rtmeanclean);
summary(stratStratRound1rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9196 1.2033 1.4671 1.4081 1.5775 2.0312
summary(stratStratRound2rtmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9488 1.1278 1.3546 1.3311 1.4426 2.1852
t.test(stratStratRound1rtmeanclean,stratStratRound2rtmeanclean, paired=T); # not significant, p =.17
# Faster RTs in round 2 when repeating strategy, but differences are not significant
# Overall, people are faster in round 2, regardless of condition/instructions. Results are also consistent across fast trial thresholds (1s, .5s).
# What about comparing strat vs nat in round 1 only:
# ALL TRIALS
natFirstRTmean = rcsSubLevelWide_clean$round1_RTmean[rcsSubLevelWide_clean$condCode %in% c(1,2)]; # people who do nat first
summary(natFirstRTmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9459 1.2087 1.3528 1.4107 1.5822 2.3205
stratFirstRTmean = rcsSubLevelWide_clean$round1_RTmean[rcsSubLevelWide_clean$condCode %in% c(3,4)]; # people who do strat first
summary(stratFirstRTmean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.2418 1.4595 1.4234 1.6064 2.0312
t.test(natFirstRTmean, stratFirstRTmean); # ns, p= .7966
# No significant difference between natural and strategy RTs for people who do natural and strategy first.
# REMOVE FAST TRIALS
natFirstRTmeanclean = rcsSubLevelWide_clean$round1_RTmeanClean[rcsSubLevelWide_clean$condCode %in% c(1,2)]; # people who do nat first
summary(natFirstRTmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9459 1.2087 1.3528 1.4110 1.5822 2.3205
stratFirstRTmeanclean = rcsSubLevelWide_clean$round1_RTmeanClean[rcsSubLevelWide_clean$condCode %in% c(3,4)]; # people who do strat first
summary(stratFirstRTmeanclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8911 1.2418 1.4595 1.4245 1.6064 2.0312
t.test(natFirstRTmeanclean, stratFirstRTmeanclean); # ns, p= 0.7845
# No significant difference between natural and strategy RTs for people who do natural and strategy first.
# Plot round 1 vs round 2 RT for each condition for all trials
# All trials
pdf(file.path(config$path$directory, config$path$shlab_figures,'meanRTAcrossRndsStratPlotsAlltrials.pdf'))
par(pty="s", mfrow= c(2,2), mar = c(5,4,5,2))
plot(natNatRound1rtmean, natNatRound2rtmean, ylim=c(0,3), xlim=c(0,3), ylab="Act natural Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="blue", main=sprintf("Natural-Natural\n RT mean diff = %.2f",cond1rtdiffmean))
abline(a=0, b=1, col="grey")
plot(natStratRound1rtmean, natStratRound2rtmean, ylim=c(0,3), xlim=c(0,3), ylab="Strategy Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="green", main=sprintf("Natural-Strategy\n RT mean diff = %.2f",cond2rtdiffmean))
abline(a=0, b=1, col="grey")
plot(stratStratRound1rtmean, stratStratRound2rtmean, ylim=c(0,3), xlim=c(0,3), ylab="Act Natural Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="red", main=sprintf("Strategy-Natural\n RT mean diff = %.2f",cond3rtdiffmean))
abline(a=0, b=1, col="grey")
plot(stratStratRound1rtmean, stratStratRound2rtmean, ylim=c(0,3), xlim=c(0,3), ylab="Strategy Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="purple", main=sprintf("Strategy-Strategy\n RT mean diff = %.2f",cond4rtdiffmean))
abline(a=0, b=1, col="grey")
mtext("Average Reaction Time (all trials)", side = 3, line = - 1, outer = TRUE)
dev.off()
# Remove fast trials
pdf(file.path(config$path$directory, config$path$shlab_figures,'meanRTAcrossRndsStratPlotsNoFastTrials.pdf'))
par(pty="s", mfrow= c(2,2),mar = c(5,4,5,2))
plot(natNatRound1rtmeanclean, natNatRound2rtmeanclean, ylim=c(0,3), xlim=c(0,3), ylab="Act natural Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="blue", main=sprintf("Natural-Natural\n RT mean diff = %.2f",cond1rtdiffmeanclean))
abline(a=0, b=1, col="grey")
plot(natStratRound1rtmeanclean, natStratRound2rtmeanclean, ylim=c(0,3), xlim=c(0,3), ylab="Strategy Round 2", xlab="Act natural Round 1", pch=16, cex = 1.5, col="green", main=sprintf("Natural-Strategy\n RT mean diff = %.2f",cond2rtdiffmeanclean))
abline(a=0, b=1, col="grey")
plot(stratStratRound1rtmeanclean, stratStratRound2rtmeanclean, ylim=c(0,3), xlim=c(0,3), ylab="Act Natural Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="red", main=sprintf("Strategy-Natural\n RT mean diff = %.2f",cond3rtdiffmeanclean))
abline(a=0, b=1, col="grey")
plot(stratStratRound1rtmeanclean, stratStratRound2rtmeanclean, ylim=c(0,3), xlim=c(0,3), ylab="Strategy Round 2", xlab="Strategy Round 1", pch=16, cex = 1.5, col="purple", main=sprintf("Strategy-Strategy\n RT mean diff = %.2f",cond4rtdiffmeanclean))
abline(a=0, b=1, col="grey")
mtext("Average Reaction Time (removed fast trials)", side = 3, line = - 1, outer = TRUE)
dev.off()
# Effect of speeding across rounds might be practice effects within round 1. Plot mean RT on each trial across round 1 and round 2
round1RTmeanxtri = rdmDFclean[rdmDFclean$roundRDM==1,] %>% group_by(trial) %>%
summarise(mean=mean(RT)); # get the mean for each trial
round2RTmeanxtri = rdmDFclean[rdmDFclean$roundRDM==2,] %>% group_by(trial) %>%
summarise(mean=mean(RT)); # get the mean for each trial
# plot RT across round
png(filename = file.path(config$path$directory, config$path$shlab_figures, "meanRTacrossrounds.png"))
par(mfrow=c(1,2), pty="s")
plot(round1RTmeanxtri$mean, ylim=c(1,2), pch=16, col = "blue", main = "Mean RT round 1", ylab="RT(sec)", xlab="trial", axes=F)
axis(1, at = c(1,65,131))
axis(2, at = c(1, 1.5,2))
plot(round2RTmeanxtri$mean, ylim=c(1,2), pch = 16, col = "red", main = "Mean RT round 2", ylab=" ", xlab="trial", axes=F)
axis(1, at = c(1,65,131))
axis(2, at = c(1, 1.5,2))
# Plot shows a big decrease in RT in round 1 and a slight decrease across round 2 but the biggest change is in round 1.
dev.off()
```
```{r RT-mean-of-medians}
# using MEANS OF THE MEDIAN reaction timse with two versions - all trials vs remove fast trials
# RTmedian = median with all trials
# RTmedianClean = mean removing fast trials (<1s)
#summary(fastTriPerSubround1);
# fast trials <1s:
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1.00 7.00 20.50 26.54 40.00 101.00
# fast trials <.5s:
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.0000 0.0000 0.0000 0.1129 0.0000 4.0000
#summary(fastTriPerSubround2);
# fast trials <1s
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.00 14.00 28.50 34.75 54.00 84.00
# fast trials <.5s
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.00000 0.00000 0.00000 0.05645 0.00000 2.00000
# ACROSS INSTRUCTION TYPE
# all trials
summary(rcsSubLevelLong_clean$RTmedian[rcsSubLevelLong_clean$strategy==0]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8944 1.0831 1.1989 1.2481 1.3834 2.2637
summary(rcsSubLevelLong_clean$RTmedian[rcsSubLevelLong_clean$strategy==1]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8293 1.1291 1.2684 1.2994 1.4408 2.1901
# remove fast trials
summary(rcsSubLevelLong_clean$RTmedianClean[rcsSubLevelLong_clean$strategy==0]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8944 1.0831 1.1989 1.2482 1.3834 2.2637
summary(rcsSubLevelLong_clean$RTmedianClean[rcsSubLevelLong_clean$strategy==1]);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8293 1.1291 1.2684 1.2998 1.4408 2.1901
t.test(rcsSubLevelLong_clean$RTmedian[rcsSubLevelLong_clean$strategy==0],rcsSubLevelLong_clean$RTmedian[rcsSubLevelLong_clean$strategy==1]); #p=.1
t.test(rcsSubLevelLong_clean$RTmedianClean[rcsSubLevelLong_clean$strategy==0],rcsSubLevelLong_clean$RTmedianClean[rcsSubLevelLong_clean$strategy==1]); #p=.1
# Reaction times are not significantly different from each other across instruction type but direction is that people are slower in strategy
# ACROSS ROUNDS
# all trials
summary(rcsSubLevelWide_clean$round1_RTmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8293 1.1479 1.3061 1.3209 1.4776 2.2637
summary(rcsSubLevelWide_clean$round2_RTmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9176 1.0485 1.2050 1.2266 1.3312 2.1901
# remove fast trials
summary(rcsSubLevelWide_clean$round1_RTmedianClean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8293 1.1479 1.3061 1.3212 1.4776 2.2637
summary(rcsSubLevelWide_clean$round2_RTmedianClean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9183 1.0485 1.2050 1.2268 1.3312 2.1901
t.test(rcsSubLevelWide_clean$round1_RTmedian, rcsSubLevelWide_clean$round2_RTmedian); # p = .003
t.test(rcsSubLevelWide_clean$round1_RTmedianClean, rcsSubLevelWide_clean$round2_RTmedianClean); # p = .003
# Reaction times significantly faster in round 2 relative to round 1
# NATURAL-NATURAL
# All trials
natNatRound1rtmedian = rcsSubLevelWide_clean$round1_RTmedian[rcsSubLevelWide_clean$condCode==1];
natNatRound2rtmedian = rcsSubLevelWide_clean$round2_RTmedian[rcsSubLevelWide_clean$condCode==1];
cond1rtdiffmedian = mean(natNatRound1rtmedian- natNatRound2rtmedian);
summary(natNatRound1rtmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8944 1.1328 1.1876 1.2538 1.4286 1.7661
summary(natNatRound2rtmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9634 1.0189 1.0903 1.1465 1.2487 1.6210
t.test(natNatRound1rtmedian,natNatRound2rtmedian, paired=T); # significant difference, p = .0003
# remove fast trials
natNatRound1rtmedianclean= rcsSubLevelWide_clean$round1_RTmedianClean[rcsSubLevelWide_clean$condCode==1];
natNatRound2rtmedianclean = rcsSubLevelWide_clean$round2_RTmedianClean[rcsSubLevelWide_clean$condCode==1];
cond1rtdiffmedianclean = mean(natNatRound1rtmedianclean- natNatRound2rtmedianclean);
summary(natNatRound1rtmedianclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8944 1.1328 1.1876 1.2540 1.4286 1.7661
summary(natNatRound2rtmedianclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9634 1.0189 1.0903 1.1466 1.2487 1.6210
t.test(natNatRound1rtmedianclean,natNatRound2rtmedianclean, paired=T); # trending difference, p = 0.0003205
# Participants are faster in round 2 when repeating natural condition
# NATURAL-STRATEGY
# all trials
natStratRound1rtmedian = rcsSubLevelWide_clean$round1_RTmedian[rcsSubLevelWide_clean$condCode==2];
natStratRound2rtmedian = rcsSubLevelWide_clean$round2_RTmedian[rcsSubLevelWide_clean$condCode==2];
cond2rtdiffmedian = mean(natStratRound1rtmedian- natStratRound2rtmedian);
summary(natStratRound1rtmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.961 1.167 1.302 1.368 1.482 2.264
summary(natStratRound2rtmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9176 1.0782 1.2473 1.2804 1.3047 2.1901
t.test(natStratRound1rtmedian,natStratRound2rtmedian, paired=T); # significant difference, p = .005
# remove fast trials
natStratRound1rtmedianclean = rcsSubLevelWide_clean$round1_RTmedianClean[rcsSubLevelWide_clean$condCode==2];
natStratRound2rtmedianclean = rcsSubLevelWide_clean$round2_RTmedianClean[rcsSubLevelWide_clean$condCode==2];
cond2rtdiffmedianclean = mean(natStratRound1rtmedianclean- natStratRound2rtmedianclean);
summary(natStratRound1rtmedianclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9613 1.1668 1.3015 1.3680 1.4822 2.2637
summary(natStratRound2rtmedianclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.9189 1.0782 1.2473 1.2804 1.3047 2.1901
t.test(natStratRound1rtmedianclean,natStratRound2rtmedianclean, paired=T); # trending p =0.004802
# Participants are faster in round 2 relative to round 1
# STRATEGY-NATURAL
# All trials
stratNatRound1rtmedian = rcsSubLevelWide_clean$round1_RTmedian[rcsSubLevelWide_clean$condCode==3];
stratNatRound2rtmedian = rcsSubLevelWide_clean$round2_RTmedian[rcsSubLevelWide_clean$condCode==3];
cond3rtdiffmedian = mean(stratNatRound1rtmedian- stratNatRound2rtmedian);
summary(stratNatRound1rtmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8293 1.1792 1.3711 1.3480 1.5731 1.7637
summary(stratNatRound2rtmedian);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.926 1.089 1.208 1.224 1.344 1.590
t.test(stratNatRound1rtmedian,stratNatRound2rtmedian, paired=T); # significant difference, p =3.72x10-5
# remove fast trials
stratNatRound1rtmedianclean = rcsSubLevelWide_clean$round1_RTmedianClean[rcsSubLevelWide_clean$condCode==3];
stratNatRound2rtmedianclean = rcsSubLevelWide_clean$round2_RTmedianClean[rcsSubLevelWide_clean$condCode==3];
cond3rtdiffmedianclean = mean(stratNatRound1rtmedianclean- stratNatRound2rtmedianclean);
summary(stratNatRound1rtmedianclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.8293 1.1796 1.3711 1.3485 1.5731 1.7637
summary(stratNatRound2rtmedianclean);
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.926 1.089 1.208 1.224 1.344 1.590
t.test(stratNatRound1rtmedianclean,stratNatRound2rtmedianclean, paired=T); # significant difference, p =3.562e-05