rocs: Recognition memory ROCs used by Klauer & Kellen (2015)
In MPTinR: Analyze Multinomial Processing Tree Models
Description
Usage
Details
Note
Source
References
Examples
Description
Data of the meta-analysis on recognition memory ROC (receiver operating characteristic) curves reported in Klauer and Kellen (2015). In total there are 850 individual ROCs, 459 6-point ROCs and 391 8-point ROCs. Both data sets first report responses to old items and then to new item. For both item types the response categories are ordered from sure-new to sure-old. Please always cite the original authors when using this data.
Usage
1
2
Details
The source of each data set is given in the exp column of each data set. The id column gives a unique id for each data set. For the 6-point ROCs the first 12 columns contain the data, for the 8-point ROCs, the first 16 columns.
Note
Whenever using any of the data available here, please make sure to cite the original sources given in the following.
Source
The 6-point ROCs contains data from the following sources:
-
Dube_2012-P and Dube_2012-W:
Dube, C., & Rotello, C. M. (2012). Binary ROCs in perception and recognition memory are curved. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(1), 130-151. http://doi.org/10.1037/a0024957
-
heathcote_2006_e1 and heathcote_2006_e2:
Heathcote, A., Ditton, E., & Mitchell, K. (2006). Word frequency and word likeness mirror effects in episodic recognition memory. Memory & Cognition, 34(4), 826-838. http://doi.org/10.3758/BF03193430
-
Jaeger_2013:
Jaeger, A., Cox, J. C., & Dobbins, I. G. (2012). Recognition confidence under violated and confirmed memory expectations. Journal of Experimental Psychology: General, 141(2), 282-301. http://doi.org/10.1037/a0025687
-
Koen_2010_pure:
Koen, J. D., & Yonelinas, A. P. (2010). Memory variability is due to the contribution of recollection and familiarity, not to encoding variability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(6), 1536-1542. http://doi.org/10.1037/a0020448
-
Koen_2011:
Koen, J. D., & Yonelinas, A. P. (2011). From humans to rats and back again: Bridging the divide between human and animal studies of recognition memory with receiver operating characteristics. Learning & Memory, 18(8), 519-522. http://doi.org/10.1101/lm.2214511
-
Koen-2013_full and Koen-2013_immediate:
Koen, J. D., Aly, M., Wang, W.-C., & Yonelinas, A. P. (2013). Examining the causes of memory strength variability: Recollection, attention failure, or encoding variability? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(6), 1726-1741. http://doi.org/10.1037/a0033671
-
Pratte_2010:
Pratte, M. S., Rouder, J. N., & Morey, R. D. (2010). Separating mnemonic process from participant and item effects in the assessment of ROC asymmetries. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(1), 224-232. http://doi.org/10.1037/a0017682
-
Smith_2004:
Smith, D. G., & Duncan, M. J. J. (2004). Testing Theories of Recognition Memory by Predicting Performance Across Paradigms. Journal of Experimental Psychology. Learning, Memory & Cognition, 30(3), 615-625.
The 8-point ROCs contains data from the following sources:
-
Benjamin_2013:
Benjamin, A. S., Tullis, J. G., & Lee, J. H. (2013). Criterion Noise in Ratings-Based Recognition: Evidence From the Effects of Response Scale Length on Recognition Accuracy. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 1601-1608. http://doi.org/10.1037/a0031849
-
Onyper_2010-Pics and Onyper_2010-Words:
Onyper, S. V., Zhang, Y. X., & Howard, M. W. (2010). Some-or-none recollection: Evidence from item and source memory. Journal of Experimental Psychology: General, 139(2), 341-364. http://doi.org/10.1037/a0018926
References
Klauer, K. C., & Kellen, D. (2015). The flexibility of models of recognition memory: The case of confidence ratings. Journal of Mathematical Psychology, 67, 8-25. http://doi.org/10.1016/j.jmp.2015.05.002
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
## Not run:
# This example shows only how to fit the 6-point ROCs
data("roc6")
# 2HTM (2-high threshold model)
htm <- "
(1-Do)*(1-g)*(1-gn1)*(1-gn2)
(1-Do)*(1-g)*(1-gn1)*gn2
(1-Do)*(1-g)*gn1
Do*(1-do1)*(1-do2) + (1-Do)*g*go1
Do*do1 + (1-Do)*g*(1-go1)*go2
Do*(1-do1)*do2 + (1-Do)*g*(1-go1)*(1-go2)
Dn*(1-dn1)*dn2 + (1-Dn)*(1-g)*(1-gn1)*(1-gn2)
Dn*dn1 + (1-Dn)*(1-g)*(1-gn1)*gn2
Dn*(1-dn1)*(1-dn2) + (1-Dn)*(1-g)*gn1
(1-Dn)*g*go1
(1-Dn)*g*(1-go1)*go2
(1-Dn)*g*(1-go1)*(1-go2)
"
# full 2HTM is over-parametereized:
check.mpt(textConnection(htm))
# apply some symmetric response mapping restrictions for D and g:
check.mpt(textConnection(htm), list("dn2 = do2", "gn2 = go2"))
# UVSD (unequal variance signal detection model)
uvsd <- "
pnorm(cr1, mu, sigma)
pnorm(cr1+cr2, mu, sigma) - pnorm(cr1, mu, sigma)
pnorm(cr3+cr2+cr1, mu, sigma) - pnorm(cr2+cr1, mu, sigma)
pnorm(cr4+cr3+cr2+cr1, mu, sigma) - pnorm(cr3+cr2+cr1, mu, sigma)
pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma) - pnorm(cr4+cr3+cr2+cr1, mu, sigma)
1 - pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma)
pnorm(cr1)
pnorm(cr2+cr1) - pnorm(cr1)
pnorm(cr3+cr2+cr1) - pnorm(cr2+cr1)
pnorm(cr4+cr3+cr2+cr1) - pnorm(cr3+cr2+cr1)
pnorm(cr5+cr4+cr3+cr2+cr1) - pnorm(cr4+cr3+cr2+cr1)
1 - pnorm(cr5+cr4+cr3+cr2+cr1)
"
# confidence criteria are parameterized as increments:
check.mpt(textConnection(uvsd))
# cr1 = [-Inf, Inf]
# cr2, cr3, cr4, cr5 = [0, Inf]
# mu = [-Inf, Inf]
# sigma = [0, Inf]
# MSD (mixture signal detection model):
# NOTE: To follow CRAN rules restricting examples to a width of 100 characters,
# the following example is splitted into multiple strings concatenated by paste().
# To view the full model use: cat(msd)
msd <- paste(c("
l*(pnorm(cr1-mu)) + (1 - l) * (pnorm(cr1-mu2))
l*(pnorm(cr1+cr2-mu) - pnorm(cr1-mu)) + (1 - l)*(pnorm(cr1+cr2-mu2)-pnorm(cr1-mu2))
l*(pnorm(cr1+cr2+cr3-mu)-pnorm(cr1+cr2-mu)) + (1-l)*(pnorm(cr1+cr2+cr3-mu2)-pnorm(cr1+cr2-mu2))
",
"l*(pnorm(cr1+cr2+cr3+cr4-mu) - pnorm(cr1+cr2+cr3-mu)) + ",
"(1 - l)*(pnorm(cr1+cr2+cr3+cr4-mu2)-pnorm(cr1+cr2+cr3-mu2))",
"
l*(pnorm(cr1+cr2+cr3+cr4+cr5-mu)-pnorm(cr1+cr2+cr3+cr4-mu)) + ",
"(1 - l)*(pnorm(cr1+cr2+cr3+cr4+cr5-mu2)-pnorm(cr1+cr2+cr3+cr4-mu2))",
"
l * (1-pnorm(cr1+cr2+cr3+cr4+cr5-mu)) + (1 - l)*(1-pnorm(cr1+cr2+cr3+cr4+cr5-mu2))
pnorm(cr1)
pnorm(cr1+cr2) - pnorm(cr1)
pnorm(cr1+cr2+cr3) - pnorm(cr1+cr2)
pnorm(cr1+cr2+cr3+cr4) - pnorm(cr1+cr2+cr3)
pnorm(cr1+cr2+cr3+cr4+cr5) - pnorm(cr1+cr2+cr3+cr4)
1-pnorm(cr1+cr2+cr3+cr4+cr5)
"), collapse = "")
cat(msd)
# confidence criteria are again parameterized as increments:
check.mpt(textConnection(msd))
# cr1 = [-Inf, Inf]
# cr2, cr3, cr4, cr5 = [0, Inf]
# lambda = [0, 1]
# mu, mu2 = [-Inf, Inf]
# DPSD (dual-process signal detection model)
dpsd <- "
(1-R)*pnorm(cr1- mu)
(1-R)*(pnorm(cr1 + cr2 - mu) - pnorm(cr1 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 - mu) - pnorm(cr1 + cr2 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 + cr4 - mu) - pnorm(cr1 + cr2 + cr3 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 + cr4 + cr5 - mu) - pnorm(cr1 + cr2 + cr3 + cr4 - mu))
R + (1-R)*(1 - pnorm(cr1 + cr2 + cr3 + cr4 + cr5 - mu))
pnorm(cr1)
pnorm(cr1 + cr2) - pnorm(cr1)
pnorm(cr1 + cr2 + cr3) - pnorm(cr1 + cr2)
pnorm(cr1 + cr2 + cr3 + cr4) - pnorm(cr1 + cr2 + cr3)
pnorm(cr1 + cr2 + cr3 + cr4 + cr5) - pnorm(cr1 + cr2 + cr3 + cr4)
1 - pnorm(cr1 + cr2 + cr3 + cr4 + cr5)
"
uvsd_fit <- fit.model(roc6[,1:12], textConnection(uvsd),
lower.bound=c(-Inf, rep(0, 5), 0.001), upper.bound=Inf)
msd_fit <- fit.model(roc6[,1:12], textConnection(msd),
lower.bound=c(-Inf, rep(0, 7)), upper.bound=c(rep(Inf, 5), 1, Inf, Inf))
dpsd_fit <- fit.model(roc6[,1:12], textConnection(dpsd),
lower.bound=c(-Inf, rep(0, 6)), upper.bound=c(rep(Inf, 6), 1))
htm_fit <- fit.mpt(roc6[,1:12], textConnection(htm),
list("dn2 = do2", "gn2 = go2"))
select.mpt(list(uvsd_fit, dpsd_fit, msd_fit, htm_fit))
# Note that the AIC and BIC results do not adequately take model flexibility into account.
## model n.parameters G.Squared.sum df.sum p.sum p.smaller.05
## 1 uvsd_fit 7 1820.568 1377 0 50
## 2 dpsd_fit 7 2074.188 1377 0 64
## 3 msd_fit 8 1345.595 918 0 51
## 4 htm_fit 9 1994.217 459 0 138
## delta.AIC.sum wAIC.sum AIC.best delta.BIC.sum wBIC.sum BIC.best
## 1 0.0000 1 230 0.0000 1 273
## 2 253.6197 0 161 253.6197 0 183
## 3 443.0270 0 16 4996.8517 0 3
## 4 2009.6489 0 56 11117.2982 0 4
## End(Not run)
MPTinR documentation built on May 2, 2019, 5:48 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Details Note Source References Examples
Description
Data of the meta-analysis on recognition memory ROC (receiver operating characteristic) curves reported in Klauer and Kellen (2015). In total there are 850 individual ROCs, 459 6-point ROCs and 391 8-point ROCs. Both data sets first report responses to old items and then to new item. For both item types the response categories are ordered from sure-new to sure-old. Please always cite the original authors when using this data.
Usage
1 2 |
Details
The source of each data set is given in the exp column of each data set. The id column gives a unique id for each data set. For the 6-point ROCs the first 12 columns contain the data, for the 8-point ROCs, the first 16 columns.
Note
Whenever using any of the data available here, please make sure to cite the original sources given in the following.
Source
The 6-point ROCs contains data from the following sources:
-
Dube_2012-PandDube_2012-W:
Dube, C., & Rotello, C. M. (2012). Binary ROCs in perception and recognition memory are curved. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(1), 130-151. http://doi.org/10.1037/a0024957 -
heathcote_2006_e1andheathcote_2006_e2:
Heathcote, A., Ditton, E., & Mitchell, K. (2006). Word frequency and word likeness mirror effects in episodic recognition memory.Memory & Cognition, 34(4), 826-838. http://doi.org/10.3758/BF03193430 -
Jaeger_2013:
Jaeger, A., Cox, J. C., & Dobbins, I. G. (2012). Recognition confidence under violated and confirmed memory expectations. Journal of Experimental Psychology: General, 141(2), 282-301. http://doi.org/10.1037/a0025687 -
Koen_2010_pure:
Koen, J. D., & Yonelinas, A. P. (2010). Memory variability is due to the contribution of recollection and familiarity, not to encoding variability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(6), 1536-1542. http://doi.org/10.1037/a0020448 -
Koen_2011:
Koen, J. D., & Yonelinas, A. P. (2011). From humans to rats and back again: Bridging the divide between human and animal studies of recognition memory with receiver operating characteristics. Learning & Memory, 18(8), 519-522. http://doi.org/10.1101/lm.2214511 -
Koen-2013_fullandKoen-2013_immediate:
Koen, J. D., Aly, M., Wang, W.-C., & Yonelinas, A. P. (2013). Examining the causes of memory strength variability: Recollection, attention failure, or encoding variability? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(6), 1726-1741. http://doi.org/10.1037/a0033671 -
Pratte_2010:
Pratte, M. S., Rouder, J. N., & Morey, R. D. (2010). Separating mnemonic process from participant and item effects in the assessment of ROC asymmetries. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(1), 224-232. http://doi.org/10.1037/a0017682 -
Smith_2004:
Smith, D. G., & Duncan, M. J. J. (2004). Testing Theories of Recognition Memory by Predicting Performance Across Paradigms. Journal of Experimental Psychology. Learning, Memory & Cognition, 30(3), 615-625.
The 8-point ROCs contains data from the following sources:
-
Benjamin_2013:
Benjamin, A. S., Tullis, J. G., & Lee, J. H. (2013). Criterion Noise in Ratings-Based Recognition: Evidence From the Effects of Response Scale Length on Recognition Accuracy. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 1601-1608. http://doi.org/10.1037/a0031849 -
Onyper_2010-PicsandOnyper_2010-Words:
Onyper, S. V., Zhang, Y. X., & Howard, M. W. (2010). Some-or-none recollection: Evidence from item and source memory. Journal of Experimental Psychology: General, 139(2), 341-364. http://doi.org/10.1037/a0018926
References
Klauer, K. C., & Kellen, D. (2015). The flexibility of models of recognition memory: The case of confidence ratings. Journal of Mathematical Psychology, 67, 8-25. http://doi.org/10.1016/j.jmp.2015.05.002
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | ## Not run:
# This example shows only how to fit the 6-point ROCs
data("roc6")
# 2HTM (2-high threshold model)
htm <- "
(1-Do)*(1-g)*(1-gn1)*(1-gn2)
(1-Do)*(1-g)*(1-gn1)*gn2
(1-Do)*(1-g)*gn1
Do*(1-do1)*(1-do2) + (1-Do)*g*go1
Do*do1 + (1-Do)*g*(1-go1)*go2
Do*(1-do1)*do2 + (1-Do)*g*(1-go1)*(1-go2)
Dn*(1-dn1)*dn2 + (1-Dn)*(1-g)*(1-gn1)*(1-gn2)
Dn*dn1 + (1-Dn)*(1-g)*(1-gn1)*gn2
Dn*(1-dn1)*(1-dn2) + (1-Dn)*(1-g)*gn1
(1-Dn)*g*go1
(1-Dn)*g*(1-go1)*go2
(1-Dn)*g*(1-go1)*(1-go2)
"
# full 2HTM is over-parametereized:
check.mpt(textConnection(htm))
# apply some symmetric response mapping restrictions for D and g:
check.mpt(textConnection(htm), list("dn2 = do2", "gn2 = go2"))
# UVSD (unequal variance signal detection model)
uvsd <- "
pnorm(cr1, mu, sigma)
pnorm(cr1+cr2, mu, sigma) - pnorm(cr1, mu, sigma)
pnorm(cr3+cr2+cr1, mu, sigma) - pnorm(cr2+cr1, mu, sigma)
pnorm(cr4+cr3+cr2+cr1, mu, sigma) - pnorm(cr3+cr2+cr1, mu, sigma)
pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma) - pnorm(cr4+cr3+cr2+cr1, mu, sigma)
1 - pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma)
pnorm(cr1)
pnorm(cr2+cr1) - pnorm(cr1)
pnorm(cr3+cr2+cr1) - pnorm(cr2+cr1)
pnorm(cr4+cr3+cr2+cr1) - pnorm(cr3+cr2+cr1)
pnorm(cr5+cr4+cr3+cr2+cr1) - pnorm(cr4+cr3+cr2+cr1)
1 - pnorm(cr5+cr4+cr3+cr2+cr1)
"
# confidence criteria are parameterized as increments:
check.mpt(textConnection(uvsd))
# cr1 = [-Inf, Inf]
# cr2, cr3, cr4, cr5 = [0, Inf]
# mu = [-Inf, Inf]
# sigma = [0, Inf]
# MSD (mixture signal detection model):
# NOTE: To follow CRAN rules restricting examples to a width of 100 characters,
# the following example is splitted into multiple strings concatenated by paste().
# To view the full model use: cat(msd)
msd <- paste(c("
l*(pnorm(cr1-mu)) + (1 - l) * (pnorm(cr1-mu2))
l*(pnorm(cr1+cr2-mu) - pnorm(cr1-mu)) + (1 - l)*(pnorm(cr1+cr2-mu2)-pnorm(cr1-mu2))
l*(pnorm(cr1+cr2+cr3-mu)-pnorm(cr1+cr2-mu)) + (1-l)*(pnorm(cr1+cr2+cr3-mu2)-pnorm(cr1+cr2-mu2))
",
"l*(pnorm(cr1+cr2+cr3+cr4-mu) - pnorm(cr1+cr2+cr3-mu)) + ",
"(1 - l)*(pnorm(cr1+cr2+cr3+cr4-mu2)-pnorm(cr1+cr2+cr3-mu2))",
"
l*(pnorm(cr1+cr2+cr3+cr4+cr5-mu)-pnorm(cr1+cr2+cr3+cr4-mu)) + ",
"(1 - l)*(pnorm(cr1+cr2+cr3+cr4+cr5-mu2)-pnorm(cr1+cr2+cr3+cr4-mu2))",
"
l * (1-pnorm(cr1+cr2+cr3+cr4+cr5-mu)) + (1 - l)*(1-pnorm(cr1+cr2+cr3+cr4+cr5-mu2))
pnorm(cr1)
pnorm(cr1+cr2) - pnorm(cr1)
pnorm(cr1+cr2+cr3) - pnorm(cr1+cr2)
pnorm(cr1+cr2+cr3+cr4) - pnorm(cr1+cr2+cr3)
pnorm(cr1+cr2+cr3+cr4+cr5) - pnorm(cr1+cr2+cr3+cr4)
1-pnorm(cr1+cr2+cr3+cr4+cr5)
"), collapse = "")
cat(msd)
# confidence criteria are again parameterized as increments:
check.mpt(textConnection(msd))
# cr1 = [-Inf, Inf]
# cr2, cr3, cr4, cr5 = [0, Inf]
# lambda = [0, 1]
# mu, mu2 = [-Inf, Inf]
# DPSD (dual-process signal detection model)
dpsd <- "
(1-R)*pnorm(cr1- mu)
(1-R)*(pnorm(cr1 + cr2 - mu) - pnorm(cr1 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 - mu) - pnorm(cr1 + cr2 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 + cr4 - mu) - pnorm(cr1 + cr2 + cr3 - mu))
(1-R)*(pnorm(cr1 + cr2 + cr3 + cr4 + cr5 - mu) - pnorm(cr1 + cr2 + cr3 + cr4 - mu))
R + (1-R)*(1 - pnorm(cr1 + cr2 + cr3 + cr4 + cr5 - mu))
pnorm(cr1)
pnorm(cr1 + cr2) - pnorm(cr1)
pnorm(cr1 + cr2 + cr3) - pnorm(cr1 + cr2)
pnorm(cr1 + cr2 + cr3 + cr4) - pnorm(cr1 + cr2 + cr3)
pnorm(cr1 + cr2 + cr3 + cr4 + cr5) - pnorm(cr1 + cr2 + cr3 + cr4)
1 - pnorm(cr1 + cr2 + cr3 + cr4 + cr5)
"
uvsd_fit <- fit.model(roc6[,1:12], textConnection(uvsd),
lower.bound=c(-Inf, rep(0, 5), 0.001), upper.bound=Inf)
msd_fit <- fit.model(roc6[,1:12], textConnection(msd),
lower.bound=c(-Inf, rep(0, 7)), upper.bound=c(rep(Inf, 5), 1, Inf, Inf))
dpsd_fit <- fit.model(roc6[,1:12], textConnection(dpsd),
lower.bound=c(-Inf, rep(0, 6)), upper.bound=c(rep(Inf, 6), 1))
htm_fit <- fit.mpt(roc6[,1:12], textConnection(htm),
list("dn2 = do2", "gn2 = go2"))
select.mpt(list(uvsd_fit, dpsd_fit, msd_fit, htm_fit))
# Note that the AIC and BIC results do not adequately take model flexibility into account.
## model n.parameters G.Squared.sum df.sum p.sum p.smaller.05
## 1 uvsd_fit 7 1820.568 1377 0 50
## 2 dpsd_fit 7 2074.188 1377 0 64
## 3 msd_fit 8 1345.595 918 0 51
## 4 htm_fit 9 1994.217 459 0 138
## delta.AIC.sum wAIC.sum AIC.best delta.BIC.sum wBIC.sum BIC.best
## 1 0.0000 1 230 0.0000 1 273
## 2 253.6197 0 161 253.6197 0 183
## 3 443.0270 0 16 4996.8517 0 3
## 4 2009.6489 0 56 11117.2982 0 4
## End(Not run)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.