TME analyses
How do naming vs other engagement strategies affect accuracy?
Item-level
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : Model failed to converge with max|grad| = 0.00726454
## (tol = 0.001, component 1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## isRight ~ scale(kinds) + scale(simpson) + typ + avgFam + scale(NamingFreq_Study_num) +
## StudyStrat + (1 | category) + (1 | subjCode)
## Data: tme_all
##
## AIC BIC logLik deviance df.resid
## 4211.3 4267.0 -2096.7 4193.3 3591
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0725 -0.8915 0.4273 0.6776 1.9134
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.7073 0.8410
## category (Intercept) 0.2511 0.5011
## Number of obs: 3600, groups: subjCode, 80; category, 45
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.20010 1.86975 1.177 0.2393
## scale(kinds) 0.21853 0.09063 2.411 0.0159 *
## scale(simpson) -0.13094 0.09097 -1.439 0.1501
## typ -0.02666 0.01572 -1.695 0.0900 .
## avgFam -0.28964 0.40343 -0.718 0.4728
## scale(NamingFreq_Study_num) 0.14698 0.10225 1.438 0.1506
## StudyStrat 0.07964 0.09859 0.808 0.4192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(k) scl(s) typ avgFam s(NF_S
## scale(knds) 0.363
## scal(smpsn) 0.395 0.188
## typ -0.035 -0.002 0.004
## avgFam -0.995 -0.364 -0.397 -0.008
## scl(NmF_S_) -0.002 0.002 -0.001 -0.001 -0.001
## StudyStrat -0.066 0.001 -0.001 0.000 0.000 0.042
## convergence code: 0
## Model failed to converge with max|grad| = 0.00726454 (tol = 0.001, component 1)Subject-level
##
## Call:
## lm(formula = testAcc ~ NamingFreq_Study_num + StudyStrat, data = tme_subj)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36275 -0.10974 0.00796 0.12957 0.34129
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.57990 0.05833 9.942 1.87e-15 ***
## NamingFreq_Study_num 0.01980 0.01559 1.270 0.208
## StudyStrat 0.01739 0.01808 0.962 0.339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1671 on 77 degrees of freedom
## Multiple R-squared: 0.03085, Adjusted R-squared: 0.005679
## F-statistic: 1.226 on 2 and 77 DF, p-value: 0.2992Subject-level graph
How does naming during study affect accuracy?
Accuracy - Naming frequency
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : Model failed to converge with max|grad| = 0.00444033
## (tol = 0.001, component 1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## isRight ~ scale(kinds) + scale(simpson) + typ + avgFam + version *
## scale(NamingFreq_Study_num) + (1 | category) + (1 | subjCode)
## Data: tme_all
##
## AIC BIC logLik deviance df.resid
## 4206.1 4268.0 -2093.0 4186.1 3590
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0309 -0.8888 0.4264 0.6757 1.9581
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.6363 0.7977
## category (Intercept) 0.2510 0.5010
## Number of obs: 3600, groups: subjCode, 80; category, 45
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.31585 1.86846 1.239 0.21518
## scale(kinds) 0.21847 0.09061 2.411 0.01590
## scale(simpson) -0.13088 0.09095 -1.439 0.15014
## typ -0.02671 0.01573 -1.698 0.08948
## avgFam -0.28974 0.40335 -0.718 0.47256
## version -0.15137 0.20151 -0.751 0.45256
## scale(NamingFreq_Study_num) 0.45449 0.15760 2.884 0.00393
## version:scale(NamingFreq_Study_num) -0.55955 0.20453 -2.736 0.00622
##
## (Intercept)
## scale(kinds) *
## scale(simpson)
## typ .
## avgFam
## version
## scale(NamingFreq_Study_num) **
## version:scale(NamingFreq_Study_num) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(k) scl(s) typ avgFam versin s(NF_S
## scale(knds) 0.363
## scal(smpsn) 0.395 0.188
## typ -0.035 -0.002 0.005
## avgFam -0.995 -0.364 -0.397 -0.008
## version -0.059 -0.001 0.001 0.004 0.000
## scl(NmF_S_) -0.018 0.004 -0.002 -0.002 -0.001 0.185
## vrs:(NF_S_) 0.014 -0.004 0.002 0.003 0.001 -0.053 -0.771
## convergence code: 0
## Model failed to converge with max|grad| = 0.00444033 (tol = 0.001, component 1)Accuracy - Mentioned naming
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## isRight ~ scale(kinds) + scale(simpson) + scale(studyAcc) + version *
## StudyStrat_naming + (1 | category) + (1 | subjCode)
## Data: tme_all
##
## AIC BIC logLik deviance df.resid
## 4197.1 4252.8 -2089.6 4179.1 3591
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8976 -0.8893 0.4289 0.6761 2.0097
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.5432 0.7371
## category (Intercept) 0.2529 0.5029
## Number of obs: 3600, groups: subjCode, 80; category, 45
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.91754 0.18075 5.076 3.85e-07 ***
## scale(kinds) 0.19430 0.08465 2.295 0.0217 *
## scale(simpson) -0.15678 0.08372 -1.873 0.0611 .
## scale(studyAcc) 0.39967 0.09171 4.358 1.31e-05 ***
## version -0.16339 0.21189 -0.771 0.4406
## StudyStrat_naming 0.17825 0.29413 0.606 0.5445
## version:StudyStrat_naming -0.53108 0.46047 -1.153 0.2488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(k) scl(s) scl(A) versin StdyS_
## scale(knds) 0.008
## scal(smpsn) 0.001 0.051
## scl(stdyAc) 0.102 0.006 -0.005
## version -0.703 -0.001 0.001 -0.082
## StdyStrt_nm -0.512 0.001 -0.001 -0.124 0.438
## vrsn:StdyS_ 0.323 -0.001 0.001 0.039 -0.461 -0.634How does naming during study affect typicality shift?
Typicality shift - Naming Frequency
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: typ_adv_choice ~ scale(avgFam) * scale(simpson) * typ + kinds +
## version * scale(NamingFreq_Study_num) + (1 | category) +
## (1 | subjCode)
## Data: filter(tme_all, isRight == 0)
##
## REML criterion at convergence: 5648.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5809 -0.6966 0.1868 0.7883 2.1769
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.1048 0.3237
## category (Intercept) 0.5376 0.7332
## Residual 5.7224 2.3922
## Number of obs: 1211, groups: subjCode, 80; category, 45
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -3.93392 0.54404 51.88061
## scale(avgFam) -0.21517 0.21370 157.96764
## scale(simpson) 0.50405 0.21541 206.40063
## typ 1.02822 0.03082 1164.28318
## kinds -0.14223 0.20765 41.39459
## version 0.31437 0.16317 64.17866
## scale(NamingFreq_Study_num) -0.13595 0.13027 65.37645
## scale(avgFam):scale(simpson) 0.18409 0.20806 194.23694
## scale(avgFam):typ -0.01991 0.03088 1150.91221
## scale(simpson):typ -0.03264 0.03092 1164.65951
## version:scale(NamingFreq_Study_num) 0.30700 0.16538 64.05728
## scale(avgFam):scale(simpson):typ -0.01895 0.03078 1178.92261
## t value Pr(>|t|)
## (Intercept) -7.231 2.15e-09 ***
## scale(avgFam) -1.007 0.3155
## scale(simpson) 2.340 0.0202 *
## typ 33.367 < 2e-16 ***
## kinds -0.685 0.4972
## version 1.927 0.0585 .
## scale(NamingFreq_Study_num) -1.044 0.3005
## scale(avgFam):scale(simpson) 0.885 0.3774
## scale(avgFam):typ -0.645 0.5191
## scale(simpson):typ -1.056 0.2914
## version:scale(NamingFreq_Study_num) 1.856 0.0680 .
## scale(avgFam):scale(simpson):typ -0.616 0.5381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(F) scl(s) typ kinds versin s(NF_S sc(F):()
## scale(vgFm) 0.233
## scal(smpsn) -0.125 -0.385
## typ -0.297 -0.024 0.036
## kinds -0.899 -0.244 0.118 -0.002
## version -0.176 -0.026 0.022 0.029 -0.005
## scl(NmF_S_) -0.030 0.027 -0.005 0.010 -0.008 0.117
## scl(vgF):() -0.206 0.001 0.210 0.291 0.062 0.017 -0.001
## scl(vgFm):t -0.014 -0.719 0.283 0.006 -0.003 0.039 -0.031 0.025
## scl(smpsn): 0.010 0.277 -0.759 -0.017 0.009 -0.021 -0.005 -0.164
## vrs:(NF_S_) 0.029 0.000 -0.007 -0.013 0.002 -0.049 -0.786 -0.007
## scl(vF):(): 0.118 0.022 -0.162 -0.366 -0.002 -0.019 -0.002 -0.752
## sc(F): scl(): v:(NF_
## scale(vgFm)
## scal(smpsn)
## typ
## kinds
## version
## scl(NmF_S_)
## scl(vgF):()
## scl(vgFm):t
## scl(smpsn): -0.363
## vrs:(NF_S_) 0.005 0.007
## scl(vF):(): -0.017 0.168 0.009Typicality shift - Mentioned naming
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## typ_adv_choice ~ scale(kinds) + typ * scale(avgFam) * scale(simpson) +
## version * StudyStrat_naming + (1 | category) + (1 | subjCode)
## Data: filter(tme_all, isRight == 0)
##
## REML criterion at convergence: 5648.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5100 -0.7109 0.1925 0.8008 2.1781
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.1254 0.3541
## category (Intercept) 0.5381 0.7336
## Residual 5.7200 2.3917
## Number of obs: 1211, groups: subjCode, 80; category, 45
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -4.28971 0.25234 230.84871 -17.000
## scale(kinds) -0.09376 0.13864 41.35775 -0.676
## typ 1.02974 0.03083 1163.14240 33.400
## scale(avgFam) -0.23045 0.21383 157.80913 -1.078
## scale(simpson) 0.51095 0.21549 206.10126 2.371
## version 0.26568 0.18926 71.41650 1.404
## StudyStrat_naming 0.01021 0.27260 69.84648 0.037
## typ:scale(avgFam) -0.01688 0.03095 1163.43969 -0.546
## typ:scale(simpson) -0.03337 0.03093 1163.11955 -1.079
## scale(avgFam):scale(simpson) 0.19395 0.20820 194.07179 0.932
## version:StudyStrat_naming 0.33309 0.40723 66.01543 0.818
## typ:scale(avgFam):scale(simpson) -0.02050 0.03081 1176.46526 -0.665
## Pr(>|t|)
## (Intercept) <2e-16 ***
## scale(kinds) 0.5026
## typ <2e-16 ***
## scale(avgFam) 0.2828
## scale(simpson) 0.0187 *
## version 0.1647
## StudyStrat_naming 0.9702
## typ:scale(avgFam) 0.5855
## typ:scale(simpson) 0.2808
## scale(avgFam):scale(simpson) 0.3527
## version:StudyStrat_naming 0.4163
## typ:scale(avgFam):scale(simpson) 0.5060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(k) typ scl(F) scl(s) versin StdyS_ ty:(F) typ:()
## scale(knds) -0.056
## typ -0.648 -0.002
## scale(vgFm) 0.042 -0.244 -0.024
## scal(smpsn) -0.046 0.118 0.036 -0.385
## version -0.475 -0.007 0.026 -0.017 0.018
## StdyStrt_nm -0.322 -0.005 0.012 0.010 -0.001 0.421
## typ:scl(vF) -0.037 -0.002 0.007 -0.720 0.283 0.025 -0.003
## typ:scl(sm) 0.035 0.009 -0.017 0.278 -0.759 -0.013 0.010 -0.363
## scl(vgF):() -0.332 0.062 0.292 0.001 0.210 0.015 0.009 0.026 -0.164
## vrsn:StdyS_ 0.207 0.009 0.004 -0.026 0.007 -0.462 -0.669 0.041 -0.010
## typ:s(F):() 0.256 -0.002 -0.366 0.023 -0.162 -0.019 -0.020 -0.019 0.168
## s(F):( vr:SS_
## scale(knds)
## typ
## scale(vgFm)
## scal(smpsn)
## version
## StdyStrt_nm
## typ:scl(vF)
## typ:scl(sm)
## scl(vgF):()
## vrsn:StdyS_ 0.009
## typ:s(F):() -0.752 -0.004Look at typicality shift in Study 1 and Study 2
Study 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## typ_adv_choice ~ kinds + avgFam + typ + simpson + (1 | subjCode) +
## (1 | category)
## Data: filter(tme1, isRight == 0)
##
## REML criterion at convergence: 2463.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2050 -0.7110 0.1930 0.7912 2.1150
##
## Random effects:
## Groups Name Variance Std.Dev.
## category (Intercept) 0.7304 0.8547
## subjCode (Intercept) 0.2506 0.5006
## Residual 5.8642 2.4216
## Number of obs: 523, groups: category, 45; subjCode, 37
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.90363 3.41972 36.20891 0.557 0.5812
## kinds -0.05667 0.26786 38.42335 -0.212 0.8336
## avgFam -1.43003 0.80663 36.20277 -1.773 0.0847 .
## typ 1.04093 0.04509 491.25072 23.088 <2e-16 ***
## simpson 0.85737 0.57723 39.52533 1.485 0.1454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) kinds avgFam typ
## kinds 0.196
## avgFam -0.976 -0.364
## typ -0.004 -0.035 -0.057
## simpson 0.272 0.192 -0.385 0.023Study 2
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## typ_adv_choice ~ kinds + avgFam + typ + simpson + (1 | subjCode) +
## (1 | category)
## Data: filter(tme2, isRight == 0)
##
## REML criterion at convergence: 3554.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4726 -0.7527 0.1963 0.7863 1.7803
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.1227 0.3503
## category (Intercept) 0.3332 0.5773
## Residual 5.8186 2.4122
## Number of obs: 763, groups: subjCode, 45; category, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.0889 2.5515 39.1237 0.819 0.41792
## kinds -0.1159 0.1977 39.7494 -0.586 0.56109
## avgFam -1.4612 0.5976 38.0614 -2.445 0.01922 *
## typ 1.0550 0.0362 731.0402 29.143 < 2e-16 ***
## simpson 1.1640 0.4183 38.0154 2.783 0.00835 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) kinds avgFam typ
## kinds 0.186
## avgFam -0.976 -0.357
## typ -0.080 0.013 0.004
## simpson 0.274 0.170 -0.381 0.012Density plots for typicality shift depending on studied typ
Absolute value
Raw value
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 6 rows containing missing values (geom_bar).
Histograms for typicality shift depending on studied typ
Absolute value
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 6 rows containing missing values (geom_bar).
Raw value
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 6 rows containing missing values (geom_bar).
TME2 analyses: remember/know
Accuracy
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl =
## control$checkConv, : Model failed to converge with max|grad| = 0.065224
## (tol = 0.001, component 1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## isRight ~ kinds + avgFam + typ + simpson * judgment * NamingFreq_Study_num +
## judgment * StudyStrat + (1 | category) + (1 | subjCode)
## Data: tme2_full
##
## AIC BIC logLik deviance df.resid
## 2421.5 2505.7 -1195.8 2391.5 2010
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9499 -0.8489 0.4149 0.6792 3.3429
##
## Random effects:
## Groups Name Variance Std.Dev.
## category (Intercept) 0.1543 0.3928
## subjCode (Intercept) 0.8412 0.9172
## Number of obs: 2025, groups: category, 45; subjCode, 45
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -0.05356 1.69715 -0.032
## kinds 0.28763 0.12294 2.340
## avgFam -0.21100 0.37292 -0.566
## typ -0.01147 0.02081 -0.551
## simpson 0.76851 0.64270 1.196
## judgmentr 2.33958 0.63949 3.659
## NamingFreq_Study_num 0.33654 0.18179 1.851
## StudyStrat -0.10196 0.16575 -0.615
## simpson:judgmentr -1.01244 0.84108 -1.204
## simpson:NamingFreq_Study_num -0.35470 0.19198 -1.848
## judgmentr:NamingFreq_Study_num -0.57209 0.19302 -2.964
## judgmentr:StudyStrat 0.01176 0.11542 0.102
## simpson:judgmentr:NamingFreq_Study_num 0.30162 0.26469 1.140
## Pr(>|z|)
## (Intercept) 0.974823
## kinds 0.019308 *
## avgFam 0.571537
## typ 0.581596
## simpson 0.231794
## judgmentr 0.000254 ***
## NamingFreq_Study_num 0.064138 .
## StudyStrat 0.538467
## simpson:judgmentr 0.228690
## simpson:NamingFreq_Study_num 0.064659 .
## judgmentr:NamingFreq_Study_num 0.003038 **
## judgmentr:StudyStrat 0.918862
## simpson:judgmentr:NamingFreq_Study_num 0.254490
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## convergence code: 0
## Model failed to converge with max|grad| = 0.065224 (tol = 0.001, component 1)Typicality shift (raw)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: typ_adv_choice ~ kinds + avgFam + typ * judgment * simpson +
## (1 | subjCode) + (1 | category)
## Data: filter(tme2_full, isRight == 0)
##
## REML criterion at convergence: 3555.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5099 -0.7432 0.1876 0.7795 1.7247
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.1273 0.3568
## category (Intercept) 0.3136 0.5600
## Residual 5.8117 2.4107
## Number of obs: 763, groups: subjCode, 45; category, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.61442 2.57780 43.09420 0.626 0.5344
## kinds -0.13896 0.19559 40.36268 -0.710 0.4815
## avgFam -1.56975 0.59130 38.76351 -2.655 0.0115 *
## typ 1.22470 0.11511 744.66120 10.639 <2e-16 ***
## judgmentr 0.91086 0.96206 742.35917 0.947 0.3441
## simpson 2.27650 0.97179 452.97055 2.343 0.0196 *
## typ:judgmentr -0.12291 0.17042 715.78270 -0.721 0.4710
## typ:simpson -0.16120 0.15864 743.91927 -1.016 0.3099
## judgmentr:simpson -0.40441 1.31195 742.27241 -0.308 0.7580
## typ:judgmentr:simpson -0.02824 0.23422 722.62540 -0.121 0.9040
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) kinds avgFam typ jdgmnt simpsn typ:jd typ:sm jdgmn:
## kinds 0.186
## avgFam -0.949 -0.354
## typ -0.210 -0.029 -0.021
## judgmentr -0.157 -0.022 -0.026 0.652
## simpson -0.065 0.069 -0.185 0.785 0.618
## typ:jdgmntr 0.144 0.030 0.017 -0.702 -0.903 -0.552
## typ:simpson 0.202 0.013 0.009 -0.901 -0.587 -0.862 0.631
## jdgmntr:smp 0.152 -0.014 0.020 -0.595 -0.899 -0.681 0.817 0.654
## typ:jdgmnt: -0.156 0.001 0.005 0.633 0.810 0.604 -0.897 -0.701 -0.904Typicality shift (absolute value)
## singular fit## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: abs_typ_adv_choice ~ kinds + avgFam + typ * judgment + simpson *
## judgment + (1 | subjCode) + (1 | category)
## Data: filter(tme2_full, isRight == 0)
##
## REML criterion at convergence: 3153.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7731 -0.7522 -0.1492 0.6331 2.6102
##
## Random effects:
## Groups Name Variance Std.Dev.
## subjCode (Intercept) 0.07401 0.272
## category (Intercept) 0.00000 0.000
## Residual 3.53952 1.881
## Number of obs: 763, groups: subjCode, 45; category, 45
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.43574 1.42449 749.67982 1.008 0.3138
## kinds -0.20759 0.11088 745.39782 -1.872 0.0616 .
## avgFam 0.21932 0.33151 745.67669 0.662 0.5085
## typ 0.25365 0.03828 745.70398 6.626 6.6e-11 ***
## judgmentr 0.11063 0.43111 737.44814 0.257 0.7975
## simpson -0.03540 0.31048 753.64510 -0.114 0.9093
## typ:judgmentr 0.01121 0.05684 752.33578 0.197 0.8438
## judgmentr:simpson -0.40389 0.43170 754.12888 -0.936 0.3498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) kinds avgFam typ jdgmnt simpsn typ:jd
## kinds 0.174
## avgFam -0.965 -0.338
## typ -0.084 -0.050 -0.044
## judgmentr -0.060 -0.050 -0.071 0.468
## simpson 0.178 0.136 -0.326 0.035 0.457
## typ:jdgmntr 0.000 0.093 0.075 -0.683 -0.671 -0.037
## jdgmntr:smp 0.024 -0.045 0.076 -0.020 -0.669 -0.669 0.020
## convergence code: 0
## singular fitInteraction between self-reported strategy, naming frequency, and judgment
Each person contributes 6 data points: their mean accuracy for typ = 2, 5, or 8, and judgment = remember or know
No strategy (N=18)
Naming (N=5)
Non-naming strategy (N=21)
Both strategies (N=1)
Are people any good at recognizing how often they’re naming?
Density plots for typicality shift depending on studied typ and judgment (Study 2)
Absolute value
Raw value
Histograms for typicality shift depending on studied typ and judgment (Study 2)
Absolute value
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 12 rows containing missing values (geom_bar).
Raw value
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 12 rows containing missing values (geom_bar).
Simpson vs. naming frequency - graph
