ManyNumbers: NIE-IU - AMS Feedback Pilot

0.1 Load Database

## New names:
## • `` -> `...4`

This file combines data from two sites: Indiana University, and Nanyang Technological University. The mean age of participants was 44.46 (SD = 8.42). Number of participants by age group:

	IU	nie
2	2	0
3	2	4
4	0	6

Participants completed the full battery: give-n task, highest count task, dot comparison task, and a visual memory task.

Three participants were removed because of experimenter errors (age entered in AMS task did not match the age calculated using DOB or reported in months)

Path: Main/Studies/2023_manynumbers/scripts/mn_amstask_analyses_amsfeedback_072524.Rmd

age	N	age_months	sd	se	ci
3	8	38.37	5.112	1.807	4.274
4	6	52.58	3.061	1.250	3.212

1.1 Data Cleaning - Trial Level

Anticipatory responses

Slow responses

participant	N	rt	sd	se	ci	sd3_sup	sd3_inf
ck1	54	2.992	0.7472	0.1017	0.2040	5.234	0.7506
ck10	60	3.249	1.7333	0.2238	0.4477	8.449	-1.9509
ck12	54	2.258	0.9355	0.1273	0.2553	5.064	-0.5488
ck2	60	2.257	1.1315	0.1461	0.2923	5.652	-1.1370
ck3	54	2.789	0.8514	0.1159	0.2324	5.343	0.2345
ck4	54	3.216	3.4013	0.4629	0.9284	13.419	-6.9883
ck5	54	3.141	1.2851	0.1749	0.3508	6.997	-0.7139
ck6	60	3.348	1.7088	0.2206	0.4414	8.475	-1.7780
ck8	60	3.815	3.4236	0.4420	0.8844	14.086	-6.4557
ck9	54	2.558	2.2984	0.3128	0.6273	9.453	-4.3376
mn066	60	6.449	16.1963	2.0909	4.1840	55.038	-42.1397
mn067	60	12.647	50.3155	6.4957	12.9979	163.594	-138.2994
mn068	60	4.479	7.7186	0.9965	1.9939	27.635	-18.6764
mn070	60	4.991	5.9164	0.7638	1.5284	22.740	-12.7580

Var1	Freq
ck1	53
ck10	60
ck12	54
ck2	59
ck3	53
ck4	53
ck5	53
ck6	58
ck8	59
ck9	52
mn066	59
mn067	59
mn068	58
mn070	58

	too_slow	valid_correct	valid_incorrect	total	valid_total
ck1	1	43	10	54	53
ck10	0	25	35	60	60
ck12	0	40	14	54	54
ck2	1	42	17	60	59
ck3	1	45	8	54	53
ck4	1	37	16	54	53
ck5	1	46	7	54	53
ck6	2	31	27	60	58
ck8	1	26	33	60	59
ck9	2	25	27	54	52
mn066	1	27	32	60	59
mn067	1	30	29	60	59
mn068	2	29	29	60	58
mn070	2	35	23	60	58

## Warning: Expected 2 pieces. Additional pieces discarded in 658 rows [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, ...].

## [1] 14

2.1 General Analyses

Practice trials

Var1	Freq
ck1	6
ck10	12
ck12	6
ck2	12
ck3	6
ck4	6
ck5	6
ck6	12
ck8	12
ck9	6
mn066	12
mn067	12
mn068	12
mn070	12

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

##   age practice_block N accuracy     sd      se     ci
## 1   3         block1 6   0.3611 0.1255 0.05122 0.1317
## 2   3         block2 6   0.5278 0.1255 0.05122 0.1317
## 3   4         block1 2   0.4167 0.1179 0.08333 1.0589
## 4   4         block2 2   0.8333 0.2357 0.16667 2.1177

## 
##  Paired t-test
## 
## data:  accuracy by practice_block
## t = -4.2, df = 7, p-value = 0.004
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -0.3568 -0.1015
## sample estimates:
## mean difference 
##         -0.2292

Experimental trials

All trials

Analyses

df_acc_noout_experimental = subset(df_acc_noout, block != "practice")
df_acc_noout_experimental$age = as.factor(as.character(df_acc_noout_experimental$age))
distinct(df_acc_noout_experimental[c("participant","age")]) %>%
  {table(.$age)}

## 
## 3 4 
## 8 6

ratio_condition_model <- glmer(accuracy ~ 
                                          +     numRatio * block*age+ 
                                          + (1|participant), data = df_acc_noout_experimental, family = binomial, control = glmerControl(optimizer = "bobyqa"))
summary(ratio_condition_model)

## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
##  Family: binomial  ( logit )
## Formula: accuracy ~ +numRatio * block * age + +(1 | participant)
##    Data: df_acc_noout_experimental
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##    845.7    904.4   -409.9    819.7      659 
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -4.110 -0.956  0.440  0.794  1.457 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.112    0.335   
## Number of obs: 672, groups:  participant, 14
## 
## Fixed effects:
##                           Estimate Std. Error z value Pr(>|z|)
## (Intercept)                 0.1918     0.7634    0.25     0.80
## numRatio                   -0.0277     0.3216   -0.09     0.93
## blockblock2                 0.1193     1.0641    0.11     0.91
## blockblock3                -0.7945     1.0844   -0.73     0.46
## age4                       -0.3030     1.1931   -0.25     0.80
## numRatio:blockblock2        0.1235     0.4598    0.27     0.79
## numRatio:blockblock3        0.0784     0.4652    0.17     0.87
## numRatio:age4               0.6867     0.5910    1.16     0.25
## blockblock2:age4           -0.9363     1.7241   -0.54     0.59
## blockblock3:age4           -1.2003     1.7492   -0.69     0.49
## numRatio:blockblock2:age4   0.3709     0.9028    0.41     0.68
## numRatio:blockblock3:age4   1.1849     0.9137    1.30     0.19
## 
## Correlation of Fixed Effects:
##             (Intr) numRat blckb2 blckb3 age4   nmRt:2 nmRt:3 nmRt:4 blc2:4 blc3:4 nR:2:4
## numRatio    -0.960                                                                      
## blockblock2 -0.700  0.689                                                               
## blockblock3 -0.690  0.678  0.492                                                        
## age4        -0.640  0.614  0.449  0.441                                                 
## nmRt:blckb2  0.671 -0.700 -0.970 -0.471 -0.430                                          
## nmRt:blckb3  0.666 -0.694 -0.475 -0.971 -0.426  0.483                                   
## numRatio:g4  0.522 -0.544 -0.375 -0.369 -0.949  0.381  0.378                            
## blckblck2:4  0.432 -0.425 -0.618 -0.303 -0.678  0.600  0.293  0.658                     
## blckblck3:4  0.428 -0.421 -0.306 -0.619 -0.668  0.293  0.602  0.649  0.463              
## nmRt:blc2:4 -0.342  0.357  0.495  0.239  0.624 -0.510 -0.245 -0.657 -0.961 -0.426       
## nmRt:blc3:4 -0.339  0.354  0.243  0.494  0.617 -0.247 -0.508 -0.649 -0.428 -0.959  0.427

Anova(ratio_condition_model, type=3)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: accuracy
##                    Chisq Df Pr(>Chisq)
## (Intercept)         0.06  1       0.80
## numRatio            0.01  1       0.93
## block               0.83  2       0.66
## age                 0.06  1       0.80
## numRatio:block      0.07  2       0.96
## numRatio:age        1.35  1       0.25
## block:age           0.54  2       0.77
## numRatio:block:age  1.71  2       0.43

em=emtrends(ratio_condition_model, pairwise ~ age|block, var="numRatio", mult.name = "age")
summary(em, infer=c(TRUE,TRUE), null=0, type = "response", adjust = "none")

## $emtrends
## block = block1:
##  age numRatio.trend    SE  df asymp.LCL asymp.UCL z.ratio p.value
##  3          -0.0277 0.322 Inf   -0.6581     0.603  -0.086  0.9313
##  4           0.6589 0.496 Inf   -0.3129     1.631   1.329  0.1839
## 
## block = block2:
##  age numRatio.trend    SE  df asymp.LCL asymp.UCL z.ratio p.value
##  3           0.0957 0.329 Inf   -0.5481     0.740   0.292  0.7707
##  4           1.1532 0.596 Inf   -0.0149     2.321   1.935  0.0530
## 
## block = block3:
##  age numRatio.trend    SE  df asymp.LCL asymp.UCL z.ratio p.value
##  3           0.0507 0.335 Inf   -0.6055     0.707   0.151  0.8797
##  4           1.9223 0.610 Inf    0.7268     3.118   3.152  0.0016
## 
## Confidence level used: 0.95 
## 
## $contrasts
## block = block1:
##  contrast    estimate    SE  df asymp.LCL asymp.UCL z.ratio p.value
##  age3 - age4   -0.687 0.591 Inf     -1.84     0.472  -1.162  0.2453
## 
## block = block2:
##  contrast    estimate    SE  df asymp.LCL asymp.UCL z.ratio p.value
##  age3 - age4   -1.058 0.681 Inf     -2.39     0.277  -1.553  0.1204
## 
## block = block3:
##  contrast    estimate    SE  df asymp.LCL asymp.UCL z.ratio p.value
##  age3 - age4   -1.872 0.695 Inf     -3.23    -0.509  -2.691  0.0071
## 
## Confidence level used: 0.95

emeans=emmeans(ratio_condition_model, pairwise ~ numRatio|age, mult.name = "age", at=list(numRatio=c(1.5,2,2.5,3)))

## NOTE: Results may be misleading due to involvement in interactions

summary(emeans, infer=c(TRUE,TRUE), null=0, type = "response", adjust = "none")

## $emmeans
## age = 3:
##  numRatio  prob     SE  df asymp.LCL asymp.UCL null z.ratio p.value
##       1.5 0.506 0.0531 Inf     0.404     0.609  0.5   0.123  0.9020
##       2.0 0.511 0.0413 Inf     0.431     0.592  0.5   0.278  0.7813
##       2.5 0.516 0.0414 Inf     0.436     0.596  0.5   0.396  0.6918
##       3.0 0.521 0.0532 Inf     0.418     0.623  0.5   0.401  0.6883
## 
## age = 4:
##  numRatio  prob     SE  df asymp.LCL asymp.UCL null z.ratio p.value
##       1.5 0.694 0.0438 Inf     0.602     0.773  0.5   3.972  0.0001
##       2.0 0.809 0.0352 Inf     0.730     0.868  0.5   6.335  <.0001
##       2.5 0.887 0.0339 Inf     0.802     0.939  0.5   6.085  <.0001
##       3.0 0.936 0.0288 Inf     0.851     0.974  0.5   5.573  <.0001
## 
## Results are averaged over the levels of: block 
## Confidence level used: 0.95 
## Intervals are back-transformed from the logit scale 
## Tests are performed on the logit scale 
## 
## $contrasts
## age = 3:
##  contrast                  odds.ratio     SE  df asymp.LCL asymp.UCL null z.ratio p.value
##  numRatio1.5 / numRatio2        0.980 0.0926 Inf    0.8147     1.180    1  -0.209  0.8341
##  numRatio1.5 / numRatio2.5      0.961 0.1816 Inf    0.6638     1.392    1  -0.209  0.8341
##  numRatio1.5 / numRatio3        0.942 0.2670 Inf    0.5408     1.642    1  -0.209  0.8341
##  numRatio2 / numRatio2.5        0.980 0.0926 Inf    0.8147     1.180    1  -0.209  0.8341
##  numRatio2 / numRatio3          0.961 0.1816 Inf    0.6638     1.392    1  -0.209  0.8341
##  numRatio2.5 / numRatio3        0.980 0.0926 Inf    0.8147     1.180    1  -0.209  0.8341
## 
## age = 4:
##  contrast                  odds.ratio     SE  df asymp.LCL asymp.UCL null z.ratio p.value
##  numRatio1.5 / numRatio2        0.537 0.0882 Inf    0.3889     0.741    1  -3.787  0.0002
##  numRatio1.5 / numRatio2.5      0.288 0.0947 Inf    0.1512     0.548    1  -3.787  0.0002
##  numRatio1.5 / numRatio3        0.155 0.0762 Inf    0.0588     0.406    1  -3.787  0.0002
##  numRatio2 / numRatio2.5        0.537 0.0882 Inf    0.3889     0.741    1  -3.787  0.0002
##  numRatio2 / numRatio3          0.288 0.0947 Inf    0.1512     0.548    1  -3.787  0.0002
##  numRatio2.5 / numRatio3        0.537 0.0882 Inf    0.3889     0.741    1  -3.787  0.0002
## 
## Results are averaged over the levels of: block 
## Confidence level used: 0.95 
## Intervals are back-transformed from the log odds ratio scale 
## Tests are performed on the log odds ratio scale

#plot(ggpredict(ratio_condition_model, terms=c("numRatio","block")))

Figure

age	numRatio	N	accuracy	sd	se	ci
3	3.5	8	0.5208	0.2588	0.0915	0.2163
3	4.0	8	0.5625	0.2663	0.0942	0.2227
4	3.5	6	0.9167	0.2041	0.0833	0.2142
4	4.0	6	0.7222	0.2277	0.0930	0.2390

age	numRatio	N	accuracy	sd	se	ci
3	1.50	8	0.5208	0.1527	0.0540	0.1276
3	2.00	8	0.4688	0.1938	0.0685	0.1621
3	2.50	8	0.5625	0.1527	0.0540	0.1276
3	3.00	8	0.5000	0.1992	0.0704	0.1665
4	1.25	6	0.6250	0.1149	0.0469	0.1205
4	1.50	6	0.6944	0.1009	0.0412	0.1059
4	2.00	6	0.7778	0.1459	0.0596	0.1531
4	2.50	6	0.8889	0.2337	0.0954	0.2453

Age Effects

Figure

##   age N accuracy      sd      se      ci
## 1   3 8   0.5130 0.09956 0.03520 0.08324
## 2   4 6   0.7465 0.10657 0.04351 0.11184

##   age N accuracy      sd      se      ci
## 1   3 8   0.4948 0.07195 0.02544 0.06015
## 2   4 6   0.7361 0.09742 0.03977 0.10224

## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.

ManyNumbers: NIE-IU - AMS Feedback Pilot

2024-09-23

0.1 Load Database

1.1 Data Cleaning - Trial Level

Anticipatory responses

Slow responses

2.1 General Analyses

Practice trials

Experimental trials

All trials

Analyses

Figure

Age Effects

Figure