ManyNumbers Exploratory Study: Contributions of Executive Functioning to Numerical Conceptual Change
RAM
2024-10-29
File: /Volumes/BL-PSY-gunderson_lab/Main/Studies/2024_manynumbers_pilot_firsthalf/scripts/mn_ef.Rmd
Databases
Heart and Flowers
Figure
Analyses
model_hf <- glmer(accuracy ~ Block* (enter_age)
+ (1|participant_id),
data = hf_dataframe, family = binomial,
control = glmerControl(optimizer = "bobyqa"))
#summary(model_hf)
tab_model(model_hf)| accuracy | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 6.04 | 6.02 – 6.06 | <0.001 |
| Block [2] | 0.00 | 0.00 – 0.00 | <0.001 |
| enter age | 1.22 | 1.21 – 1.22 | <0.001 |
| Block [2] × enter age | 4.18 | 4.16 – 4.19 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant_id | 1.32 | ||
| ICC | 0.29 | ||
| N participant_id | 19 | ||
| Observations | 456 | ||
| Marginal R2 / Conditional R2 | 0.203 / 0.431 | ||
Anova(model_hf, type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: accuracy
## Chisq Df Pr(>Chisq)
## (Intercept) 983128 1 < 0.00000000000000022 ***
## Block 16100146 1 < 0.00000000000000022 ***
## enter_age 11870 1 < 0.00000000000000022 ***
## Block:enter_age 622615 1 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1means_model_hf = emmeans(model_hf, pairwise ~ Block|enter_age, mult.name = "Block", at = list(enter_age= c(3,4,5)))
summary(means_model_hf, infer=c(TRUE,TRUE), null=0, type = "response", adjust = "none")## $emmeans
## enter_age = 3:
## Block prob SE df asymp.LCL asymp.UCL null z.ratio p.value
## 1 0.9162 0.0004410 Inf 0.9153 0.9171 0.5 416.394 <.0001
## 2 0.3564 0.0018614 Inf 0.3528 0.3601 0.5 -72.817 <.0001
##
## enter_age = 4:
## Block prob SE df asymp.LCL asymp.UCL null z.ratio p.value
## 1 0.9302 0.0004863 Inf 0.9292 0.9311 0.5 345.769 <.0001
## 2 0.7383 0.0020443 Inf 0.7343 0.7423 0.5 98.023 <.0001
##
## enter_age = 5:
## Block prob SE df asymp.LCL asymp.UCL null z.ratio p.value
## 1 0.9420 0.0005061 Inf 0.9410 0.9430 0.5 300.952 <.0001
## 2 0.9349 0.0007960 Inf 0.9334 0.9365 0.5 203.682 <.0001
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
## Tests are performed on the logit scale
##
## $contrasts
## enter_age = 3:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## Block1 / Block2 19.74 0.1131 Inf 19.52 19.96 1 520.438 <.0001
##
## enter_age = 4:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## Block1 / Block2 4.72 0.0353 Inf 4.65 4.79 1 207.775 <.0001
##
## enter_age = 5:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## Block1 / Block2 1.13 0.0104 Inf 1.11 1.15 1 13.256 <.0001
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log odds ratio scale
## Tests are performed on the log odds ratio scaleDCCS
MN032 was removed because they failed the pre-switch (shape) condition (accuracy < .80)
Figure
## , , = 3
##
##
## color shape
## failed 1 1
## passed 3 3
##
## , , = 4
##
##
## color shape
## failed 2 0
## passed 4 6
##
## , , = 5
##
##
## color shape
## failed 2 0
## passed 7 9
Analyses
#dccs_dataframe$age_factor = as.factor(as.numeric(dccs_dataframe$enter_age))
dccs_dataframe= subset(dccs_dataframe, participant!="mn032")
model_dccs <- glmer(accuracy ~ condition
* enter_age
+ (1|participant),
data = dccs_dataframe, family = binomial,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
#summary(model_dccs)
tab_model(model_dccs)| accuracy | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.45 | 0.45 – 0.45 | <0.001 |
| condition [shape] | 6.41 | 1.35 – 30.50 | 0.020 |
| enter age | 2.48 | 2.47 – 2.49 | <0.001 |
|
condition [shape] × enter age |
1.96 | 1.96 – 1.97 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 participant | 11.59 | ||
| ICC | 0.78 | ||
| N participant | 18 | ||
| Observations | 358 | ||
| Marginal R2 / Conditional R2 | 0.308 / 0.847 | ||
Anova(model_dccs, type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: accuracy
## Chisq Df Pr(>Chisq)
## (Intercept) 341816.1065 1 < 0.0000000000000002 ***
## condition 5.4555 1 0.01951 *
## enter_age 450620.1429 1 < 0.0000000000000002 ***
## condition:enter_age 249002.5868 1 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1means_model_dccs = emmeans(model_dccs, pairwise ~ condition|enter_age, mult.name = "condition", at = list(enter_age= c(3,4,5)))
summary(means_model_dccs, infer=c(TRUE,TRUE), null=0, type = "response", adjust = "none")## $emmeans
## enter_age = 3:
## condition prob SE df asymp.LCL asymp.UCL null z.ratio p.value
## color 0.87373 0.000472109 Inf 0.87280 0.87465 0.5 452.037 <.0001
## shape 0.99703 0.002353719 Inf 0.98604 0.99937 0.5 7.312 <.0001
##
## enter_age = 4:
## condition prob SE df asymp.LCL asymp.UCL null z.ratio p.value
## color 0.94494 0.000290288 Inf 0.94437 0.94551 0.5 509.502 <.0001
## shape 0.99939 0.000485538 Inf 0.99710 0.99987 0.5 9.302 <.0001
##
## enter_age = 5:
## condition prob SE df asymp.LCL asymp.UCL null z.ratio p.value
## color 0.97705 0.000154745 Inf 0.97674 0.97735 0.5 543.634 <.0001
## shape 0.99987 0.000099785 Inf 0.99940 0.99997 0.5 11.291 <.0001
##
## Confidence level used: 0.95
## Intervals are back-transformed from the logit scale
## Tests are performed on the logit scale
##
## $contrasts
## enter_age = 3:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## color / shape 0.02059 0.01638 Inf 0.00433 0.0979 1 -4.881 <.0001
##
## enter_age = 4:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## color / shape 0.01049 0.00834 Inf 0.00221 0.0499 1 -5.729 <.0001
##
## enter_age = 5:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## color / shape 0.00534 0.00425 Inf 0.00112 0.0254 1 -6.577 <.0001
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log odds ratio scale
## Tests are performed on the log odds ratio scale#plot(ggpredict(model_dccs, terms=c("enter_age","condition")))Given-N Task
given_data2 = given_hc_dataframe
given_data2 = given_data2[,c(57,24:47)]
na_strings <- c("21", "22")
given_data3 = given_data2 %>%
replace_with_na_all(condition = ~.x %in% na_strings)
given_data3_gather = gather(given_data3, "trial", "response", -participant)
given_data3_gather = given_data3_gather %>%
left_join(given_trials, by = "trial")
given_data3_gather$accuracy = ifelse(given_data3_gather$number == given_data3_gather$response, 1,0)
given_data3_gather$subject_id = given_data3_gather$participant
given_data3_gather$demographics_pid = given_data3_gather$participant
given_data3_gather$Query = given_data3_gather$number
given_data3_gather$Response = given_data3_gather$response
given_data3_gather_overall = aggregate(accuracy ~ participant, given_data3_gather, mean)Correlations
## hf_congruent hf_incongruent Pre-Switch\nShape
## hf_congruent 1.00 0.26 0.04
## hf_incongruent 0.26 1.00 0.06
## Pre-Switch\nShape 0.04 0.06 1.00
## Post-Switch\nColor 0.08 0.34 -0.01
## demographics_age 0.20 0.73 0.04
## highestcount_score 0.07 0.37 0.23
## given_accuracy 0.34 0.56 0.24
## Post-Switch\nColor demographics_age highestcount_score
## hf_congruent 0.08 0.20 0.07
## hf_incongruent 0.34 0.73 0.37
## Pre-Switch\nShape -0.01 0.04 0.23
## Post-Switch\nColor 1.00 0.27 0.31
## demographics_age 0.27 1.00 0.77
## highestcount_score 0.31 0.77 1.00
## given_accuracy 0.37 0.79 0.70
## given_accuracy
## hf_congruent 0.34
## hf_incongruent 0.56
## Pre-Switch\nShape 0.24
## Post-Switch\nColor 0.37
## demographics_age 0.79
## highestcount_score 0.70
## given_accuracy 1.00
##
## n
## hf_congruent hf_incongruent Pre-Switch\nShape
## hf_congruent 19 19 18
## hf_incongruent 19 19 18
## Pre-Switch\nShape 18 18 18
## Post-Switch\nColor 18 18 18
## demographics_age 19 19 18
## highestcount_score 19 19 18
## given_accuracy 19 19 18
## Post-Switch\nColor demographics_age highestcount_score
## hf_congruent 18 19 19
## hf_incongruent 18 19 19
## Pre-Switch\nShape 18 18 18
## Post-Switch\nColor 18 18 18
## demographics_age 18 19 19
## highestcount_score 18 19 19
## given_accuracy 18 19 19
## given_accuracy
## hf_congruent 19
## hf_incongruent 19
## Pre-Switch\nShape 18
## Post-Switch\nColor 18
## demographics_age 19
## highestcount_score 19
## given_accuracy 19
##
## P
## hf_congruent hf_incongruent Pre-Switch\nShape
## hf_congruent 0.2750 0.8867
## hf_incongruent 0.2750 0.8211
## Pre-Switch\nShape 0.8867 0.8211
## Post-Switch\nColor 0.7432 0.1691 0.9651
## demographics_age 0.4121 0.0004 0.8658
## highestcount_score 0.7863 0.1141 0.3571
## given_accuracy 0.1520 0.0132 0.3339
## Post-Switch\nColor demographics_age highestcount_score
## hf_congruent 0.7432 0.4121 0.7863
## hf_incongruent 0.1691 0.0004 0.1141
## Pre-Switch\nShape 0.9651 0.8658 0.3571
## Post-Switch\nColor 0.2764 0.2039
## demographics_age 0.2764 0.0001
## highestcount_score 0.2039 0.0001
## given_accuracy 0.1359 0.0000 0.0008
## given_accuracy
## hf_congruent 0.1520
## hf_incongruent 0.0132
## Pre-Switch\nShape 0.3339
## Post-Switch\nColor 0.1359
## demographics_age 0.0000
## highestcount_score 0.0008
## given_accuracy