Description Usage Details Note Source References Examples
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.
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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.
Whenever using any of the data available here, please make sure to cite the original sources given in the following.
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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 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. doi: 10.1037/a0018926
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. doi: 10.1016/j.jmp.2015.05.002
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# 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)
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