Temporal Learning: IS50 prior RT

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Erica Lewis
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1 Temporal Learning: IS50 prior RT Loading required package: Matrix Jihyun Suh 1/27/2016 This data.table install has not detected OpenMP support. It will work but slower in single threaded m Attaching package: 'lmertest' The following object is masked from 'package:lme4': lmer The following object is masked from 'package:stats': step Attaching package: 'ggplot2' The following objects are masked from 'package:psych': %+%, alpha setwd("~/box Sync/Lab meeting/bugg") # data<-as.data.frame(read.csv('bugg_data_cleaned.csv', header = TRUE, sep = # ',')) raw_data <- as.data.frame(read.csv("bugg_raw_data.csv", header = TRUE, sep = ",")) raw_data$subjpcno <- NA raw_data$subjpcno[which(raw_data$subjpc == "MC")] <- 0 raw_data$subjpcno[which(raw_data$subjpc == "MI")] <- 1 # feature repetition raw_data$feature_r <- NA library(zoo) Attaching package: 'zoo' The following objects are masked from 'package:base': as.ate, as.ate.numeric raw_data$feature_r <- c(rollapply(raw_data$color, width = 2, FUN = function(x) as.numeric(!identical(x[2 x[1]))), NA) raw_data$feature_r_1 <- NA raw_data$feature_r_1[2:nrow(raw_data)] <- raw_data$feature_r[1:nrow(raw_data) - 1] raw_data$feature_r_1[which(is.na(raw_data$priorrt))] <- NA raw_data <- subset(raw_data, select = -feature_r) filtering incorrect trials 1

2 p_incorr <- (length(which(raw_data$accuracy == 0))/nrow(raw_data)) * 100 p_incorr [1] data_won <- raw_data[raw_data$accuracy!= 0, ] filtering fast/slow trials p_out_rt <- (length(which(data_won$rt >= 3000 data_won$rt <= 200))/nrow(data_won)) * 100 p_out_rt [1] data_won <- data_won[data_won$rt < 3000, ] data_won <- data_won[data_won$rt > 200, ] data_won$priorrt[which(data_won$priorrt >= 3000)] <- NA data_won$priorrt[which(data_won$priorrt <= 200)] <- NA # eliminate neutral trials data_won <- data_won[data_won$congruency!= "neutral", ] # eliminate biased trials data_won <- data_won[data_won$ispc == "IS50", ] # eliminate feature repetition data_won <- data_won[data_won$feature_r_1 == 1, ] data_won <- na.omit(data_won) # data$priorerr[is.na(data$priorerr)] <- 2 eliminate N-1 errors # data<-subset(data,priorerr!=0,select=expname:priorerr) # eliminate neutral trials data_won<-data[data$trialtype!='neutral',] remove # rows with all nas data_won <- data_won[!apply(is.na(data_won), 1, all),] # data_won<-data # transform RT data_won$t_rt <- (1/data_won$rt) * data_won$t_priorrt <- (1/data_won$priorRT) * # centering prior RT original data_won$rt_gc <- data_won$rt - mean(data_won$rt, na.rm = T) data_won$priorrt_gc <- data_won$priorrt - mean(data_won$priorrt, na.rm = T) # transformed data_won$t_rt_gc <- data_won$t_rt - mean(data_won$t_rt, na.rm = T) data_won$t_priorrt_gc <- data_won$t_priorrt - mean(data_won$t_priorrt, na.rm = T) # Normality Test Plot_RT <- as.numeric(scale(data_won$rt)) Plot_T_RT <- as.numeric(scale(data_won$t_rt)) Plot_RT <- Plot_RT[!is.na(Plot_RT)] Plot_T_RT <- Plot_T_RT[!is.na(Plot_T_RT)] 2

3 n1 <- length(plot_rt) n2 <- length(plot_t_rt) probabilities_1 = (1:n1)/(n1 + 1) probabilities_2 = (1:n2)/(n2 + 1) normal.quantiles1 = qnorm(probabilities_1, mean(plot_rt, na.rm = TRUE), sd(plot_rt, na.rm = T)) plot(sort(normal.quantiles1), sort(plot_rt), xlab = "Theoretical Quantiles from Normal istribution", ylab = "Sample Quqnatiles of RT", main = "Normal Quantile-Quantile Plot of RT", xlim = c(-7, 5), ylim = c(-7, 5)) abline(0, 1) Normal Quantile Quantile Plot of RT Sample Quqnatiles of RT Theoretical Quantiles from Normal istribution hist(plot_rt) 3

4 Histogram of Plot_RT Frequency Plot_RT normal.quantiles2 = qnorm(probabilities_2, mean(plot_t_rt, na.rm = TRUE), sd(plot_t_rt, na.rm = T)) plot(sort(normal.quantiles2), sort(plot_t_rt), xlab = "Theoretical Quantiles from Normal istribution", ylab = "Sample Quqnatiles of Transformed RT", main = "Normal Quantile-Quantile Plot of Transfromed_R xlim = c(-7, 5), ylim = c(-7, 5)) abline(0, 1) 4

5 Normal Quantile Quantile Plot of Transfromed_RT Sample Quqnatiles of Transformed RT Theoretical Quantiles from Normal istribution hist(plot_t_rt) Histogram of Plot_T_RT Frequency Mixed Model # exclude NAs data_won <- na.omit(data_won) Plot_T_RT #Linear 5

6 # Set model optimizer cl <- lmercontrol(optimizer = "optimx", optctrl = list(method = "nlminb", maxiter = )) Transformed RT 1) PC x trialtype Fit1a <- lmer(t_rt ~ subjpcno + trialtype + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = data_won, na.action = na.exclude, control = cl) summary(fit1a) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: t_rt ~ subjpcno + trialtype + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: data_won Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype subjpcno:trialtype Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** subjpcno:trialtype *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp 6

7 subjpcno trialtype sbjpcn:trlt # fixef(fit1a) av1 <- vcov(fit1a) [1] ) PC x trialtype + prior RT new_data <- data_won[data_won$prior.acc!= 0, ] Fit1b <- lmer(t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude, control = cl) summary(fit1b) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype t_priorrt_gc subjpcno:trialtype Pr(> t ) (Intercept) < *** subjpcno ** 7

8 trialtype *** t_priorrt_gc < *** subjpcno:trialtype *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp t_prt_ subjpcno trialtype t_prirrt_gc sbjpcn:trlt # fixef(fit1b) vcov(fit1b) av1 <- vcov(fit1b) [1] ) PCtrialtypepriorRT - 3way interaction Fit1c <- lmer(t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + subjpcno:t_priorrt_gc + trialtype:t_priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude, control = cl) summary(fit1c) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + subjpcno:t_priorrt_gc + trialtype:t_priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: 8

9 Estimate Std. Error df t value (Intercept) subjpcno trialtype t_priorrt_gc subjpcno:trialtype subjpcno:t_priorrt_gc trialtype:t_priorrt_gc Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** t_priorrt_gc < *** subjpcno:trialtype *** subjpcno:t_priorrt_gc trialtype:t_priorrt_gc *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp t_prt_ sbjpc: spc:_r subjpcno trialtype t_prirrt_gc sbjpcn:trlt sbjpcn:_rt_ trltyp:_rt_ # fixef(fit1c) vcov(fit1c) av1 <- vcov(fit1c) [1] ) PC + trialtype + PriorRT + PC x trialtype + trialtype x priort (Schmidt s) Fit1d <- lmer(t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + trialtype:t_priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude, control = cl) summary(fit1d) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + trialtype:t_priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data Control: cl REML criterion at convergence:

10 Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype t_priorrt_gc subjpcno:trialtype trialtype:t_priorrt_gc Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** t_priorrt_gc < *** subjpcno:trialtype *** trialtype:t_priorrt_gc *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp t_prt_ sbjpc: subjpcno trialtype t_prirrt_gc sbjpcn:trlt trltyp:_rt_ # fixef(fit1d) vcov(fit1d) av1 <- vcov(fit1d) [1] ) three-way interaction Fit1e <- lmer(t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + subjpcno:t_priorrt_gc + trialtype:t_priorrt_gc + subjpcno:trialtype:t_priorrt_gc + 10

11 (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude, control = cl) summary(fit1e) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: t_rt ~ subjpcno + trialtype + t_priorrt_gc + subjpcno:trialtype + subjpcno:t_priorrt_gc + trialtype:t_priorrt_gc + subjpcno:trialtype:t_priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype t_priorrt_gc subjpcno:trialtype subjpcno:t_priorrt_gc trialtype:t_priorrt_gc subjpcno:trialtype:t_priorrt_gc Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** t_priorrt_gc *** subjpcno:trialtype *** subjpcno:t_priorrt_gc trialtype:t_priorrt_gc *** subjpcno:trialtype:t_priorrt_gc Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp t_prt_ sbjpc: spc:_r t:_rt_ subjpcno trialtype

12 t_prirrt_gc sbjpcn:trlt sbjpcn:_rt_ trltyp:_rt_ sbjpc::_rt_ # fixef(fit1e) vcov(fit1e) av1 <- vcov(fit1e) [1] untransformed RT 1) PC x trialtype Fit2a <- lmer(rt ~ subjpcno + trialtype + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = data_won, na.action = na.exclude, control = cl) summary(fit2a) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: rt ~ subjpcno + trialtype + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: data_won Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype subjpcno:trialtype

13 Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** subjpcno:trialtype *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp subjpcno trialtype sbjpcn:trlt # fixef(fit2a) vcov(fit2a) av1 <- vcov(fit2a) [1] ) PC x trialtype + prior RT Fit2b <- lmer(rt ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude, control = cl) summary(fit2b) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: rt ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value 13

14 (Intercept) subjpcno trialtype priorrt_gc subjpcno:trialtype Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** priorrt_gc < *** subjpcno:trialtype *** --- Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp prrrt_ subjpcno trialtype priorrt_gc sbjpcn:trlt # fixef(fit2b) vcov(fit2b) av1 <- vcov(fit2b) [1] ) PCtrialtypepriorRT - 3way interaction Fit2c <- lmer(rt_gc ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + subjpcno:priorrt_gc + trialtype:priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude, control = cl) summary(fit2c) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: rt_gc ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + subjpcno:priorrt_gc + trialtype:priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept)

15 stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype priorrt_gc subjpcno:trialtype subjpcno:priorrt_gc trialtype:priorrt_gc Pr(> t ) (Intercept) *** subjpcno ** trialtype *** priorrt_gc *** subjpcno:trialtype *** subjpcno:priorrt_gc trialtype:priorrt_gc Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp prrrt_ sbjpc: spc:rt subjpcno trialtype priorrt_gc sbjpcn:trlt sbjpcn:prt_ trltyp:prt_ # fixef(fit2c) vcov(fit2c) av1 <- vcov(fit2c) [1] ) PC + trialtype + PriorRT + PC x trialtype + trialtype x priort (Schmidt s) Fit2d <- lmer(rt ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + trialtype:priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude) summary(fit2d) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: rt ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + 15

16 trialtype:priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype priorrt_gc subjpcno:trialtype trialtype:priorrt_gc Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** priorrt_gc *** subjpcno:trialtype *** trialtype:priorrt_gc Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) sbjpcn trltyp prrrt_ sbjpc: subjpcno trialtype priorrt_gc sbjpcn:trlt trltyp:prt_ # fixef(fit2d) vcov(fit2d) av1 <- vcov(fit2d) [1]

17 5) three-way interaction Fit2e <- lmer(rt ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + subjpcno:priorrt_gc + trialtype:priorrt_gc + subjpcno:trialtype:priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment), data = new_data, na.action = na.exclude, control = cl) summary(fit2e) Linear mixed model fit by REML t-tests use Satterthwaite approximations to degrees of freedom [lmermod] Formula: rt ~ subjpcno + trialtype + priorrt_gc + subjpcno:trialtype + subjpcno:priorrt_gc + trialtype:priorrt_gc + subjpcno:trialtype:priorrt_gc + (1 unique_subjno) + (1 stimuli) + (1 experiment) ata: new_data Control: cl REML criterion at convergence: Scaled residuals: Min 1Q Median 3Q Max Random effects: Groups Name Variance Std.ev. unique_subjno (Intercept) stimuli (Intercept) experiment (Intercept) Residual Number of obs: 5785, groups: unique_subjno, 72; stimuli, 32; experiment, 2 Fixed effects: Estimate Std. Error df t value (Intercept) subjpcno trialtype priorrt_gc subjpcno:trialtype subjpcno:priorrt_gc trialtype:priorrt_gc subjpcno:trialtype:priorrt_gc Pr(> t ) (Intercept) < *** subjpcno ** trialtype *** priorrt_gc *** subjpcno:trialtype *** subjpcno:priorrt_gc trialtype:priorrt_gc subjpcno:trialtype:priorrt_gc Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: 17

18 (Intr) sbjpcn trltyp prrrt_ sbjpc: spc:rt tr:rt_ subjpcno trialtype priorrt_gc sbjpcn:trlt sbjpcn:prt_ trltyp:prt_ sbjpcn::rt_ # fixef(fit2e) vcov(fit2e) av1 <- vcov(fit2e) [1]

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