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ManuelRausch committed Apr 12, 2024
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12 changes: 6 additions & 6 deletions DESCRIPTION
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Expand Up @@ -2,7 +2,7 @@ Package: statConfR
Type: Package
Title: Models of Decision Confidence and Metacognition
Version: 0.1.1
Date: 2024-02-06
Date: 2024-04-12
Authors@R: c(
person("Manuel", "Rausch", email="[email protected]", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-5805-5544")),
Expand All @@ -11,15 +11,15 @@ Authors@R: c(
)
Maintainer: Manuel Rausch <[email protected]>
Description: Provides fitting functions and other tools for decision confidence
and metacognition researchers, including meta-d´/d´, often considered to be
and metacognition researchers, including meta-d′/d′, often considered to be
the gold standard to measure metacognitive efficiency.
Also allows to fit several static models of decision making and confidence
to test the assumptions underlying meta-d´/d´ and which may serve as an
alternative when the assumptions of meta-d´/d´ do not hold. See also Rausch
et al. (2023) <doi:10.31234/osf.io/kdz34>.
to test the assumptions underlying meta-d′/d′ and which may serve as an
alternative when the assumptions of meta-d′/d′ do not hold. See also Rausch
et al. (2023) <doi:10.1037/met0000634>.
License: GPL(>=3)
URL: https://github.com/ManuelRausch/StatConfR
BugReports: https://github.com/ManuelRausch/StatConfR
BugReports: https://github.com/ManuelRausch/StatConfR/issues
Depends:
R (>= 4.0)
Imports:
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132 changes: 63 additions & 69 deletions README.md
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Expand Up @@ -6,8 +6,8 @@ binary discrimination tasks, as well as meta-d′/d′ (Rausch & Hellmann,
2024). The package can be used to test the assumptions underlying
meta-d′/d′. Several models provide a metacognition parameter that may
serve as an alternative when the assumptions of meta-d′/d′ assuming the
corresponding model provides a better fit to the data. Currently, the
following models are included:
corresponding model provides a better fit to the data. At this point in
time, the following models are included:

- Signal detection rating model

Expand All @@ -19,7 +19,7 @@ following models are included:

- Independent Gaussian model

- Independent truncated gaussian Model (the model underlying the
- Independent truncated Gaussian model (the model underlying the
meta-d′/d′ method, see Rausch et al., 2023)

- Lognormal noise model
Expand All @@ -28,80 +28,62 @@ following models are included:

## Installation

For the current development version, the easiest way of installation is
using `devtools` and install from GitHub:
The latest released version of the package is available on CRAN via

devtools::install_github("ManuelRausch/StatConfR")
install.packages("statConfR")

The latest released version of the package will soon be available on
CRAN via
The easiest way to install the development version is using `devtools`
and install from GitHub:

``` r
install.packages("statConfR")
```
devtools::install_github("ManuelRausch/StatConfR")

## Usage

### Data structure

The package includes a demo data set from a masked orientation
discrimination task with confidence judgments.
discrimination task with confidence judgments (Hellmann et al., 2023,
Exp. 1.

``` r
library(statConfR)
data("MaskOri")
head(MaskOri)
```

## participant stimulus correct rating condition gender age trialNo
## 1 1 0 0 1 33.3 f 23 1
## 2 1 0 0 1 16.7 f 23 2
## 3 1 90 1 1 133.3 f 23 3
## 4 1 0 1 4 133.3 f 23 4
## 5 1 90 1 2 33.3 f 23 5
## 6 1 90 0 2 16.7 f 23 6
## participant stimulus correct rating diffCond trialNo
## 1 1 0 1 0 8.3 1
## 2 1 90 0 4 133.3 2
## 3 1 0 1 0 33.3 3
## 4 1 90 0 0 16.7 4
## 5 1 0 1 3 133.3 5
## 6 1 0 1 0 16.7 6

Data should be in the form of a data.frame object columns for following
variables:

- stimulus (factor with 2 levels): The property of the stimulus which
defines which response is correct
- condition (factor): The experimental manipulation that is expected to
affect discrimination sensitivity (only used for fitting confidence
models)
- diffCond (factor): The experimental manipulation that is expected to
affect discrimination sensitivity
- correct (0-1): Indicating whether the choice was correct (1) or
incorrect(0).
- rating (factor): A discrete variable encoding the decision confidence
(high: very confident; low: less confident)
- sbj (integer): giving the subject ID.

``` r
MaskOri$condition <- MaskOri$diffCond
head(MaskOri)
```

## participant stimulus correct rating gender age trialNo
## 1 1 0 0 1 f 23 1
## 2 1 0 0 1 f 23 2
## 3 1 90 1 1 f 23 3
## 4 1 0 1 4 f 23 4
## 5 1 90 1 2 f 23 5
## 6 1 90 0 2 f 23 6
- participant (integer): giving the subject ID.

### Fitting

It is strongly recommended that if metacognitive efficiency is to be
measured using the meta-d’/d’ method that researchers fist determine
measured using the meta-d′/d′ method that researchers fist determine
whether the Independent Truncated Gaussian Model, the confidence model
implied by the meta-d’/d’ method, is an adequate description of the
implied by the meta-d′/d′ method, is an adequate description of the
data. Using the function fitConfModel, we can fit several confidence
models to the data of each participant. The argument
`.parallel=TRUE`allows for parallelization over all but one available
core.

``` r
fitted_pars <- fitConfModels(MaskOri, models=c("SDT", "WEV"), .parallel = TRUE)
```
fitted_pars <- fitConfModels(MaskOri, models=c("SDT", "WEV"), .parallel = TRUE)

This parallelizes the fitting process over participant-model
combinations. The output is then a data frame with one row for each
Expand All @@ -113,34 +95,41 @@ These may be used for quantitative model comparison.
head(fitted_pars)
```

## model participant negLogLik N k BIC AICc AIC d1
## 1 SDT 1 989.8068 960 11 2055.150 2001.846 2001.614 0.15071384
## 2 WEV 1 913.0819 960 13 1915.434 1852.494 1852.164 0.13854199
## 3 SDT 2 1076.5072 960 11 2228.551 2175.246 2175.014 0.24277316
## 4 WEV 2 1053.6981 960 13 2196.666 2133.726 2133.396 0.37470202
## 5 SDT 3 890.3000 960 11 1856.136 1802.832 1802.600 0.01609433
## 6 WEV 3 767.7225 960 13 1624.715 1561.775 1561.445 0.00244278
## d2 d3 d4 theta w sigma cA1
## 1 1.1622992 5.866728 8.625396 0.38575591 NA NA -1.7634060
## 2 1.4499134 4.233249 5.506721 0.39877878 0.4147619 0.5123999 -0.8207672
## 3 1.4138645 4.925860 7.691836 0.02784248 NA NA -2.1061454
## 4 1.4442048 4.073919 5.971996 0.02595525 0.2332637 0.4086341 -1.4745243
## 5 0.1404938 2.947725 7.086975 -1.26647186 NA NA -2.2868067
## 6 0.4075982 3.206503 5.926012 -1.33589282 0.4360688 0.6875500 -1.9056750
## cA2 cA3 cB1 cB2 cB3
## 1 -1.1169610 -0.12542605 1.0221640 1.7691779 2.183439
## 2 -0.2423225 0.82897295 -0.1272569 0.6705370 1.086434
## 3 -1.5485703 -0.90972112 0.7944319 1.5473073 1.995515
## 4 -0.9671368 -0.38593372 0.2404484 0.9270251 1.339125
## 5 -1.7878067 -1.35670379 0.8875427 1.7188347 2.410818
## 6 -1.2341607 -0.07619421 0.2861275 1.2793275 2.018620

If the Truncated Gaussian model performs best in the model comparison,
metacognitive efficiency can be quantified using meta-d′/d′:

``` r
MetaDs <- fitMetaDprime(MaskOri, model="ML", .parallel = TRUE)
```
## model participant negLogLik N k BIC AICc AIC d_1 d_2
## 1 SDT 1 2721.256 1620 14 5545.975 5470.739 5470.513 0.0428 0.4593
## 2 WEV 1 2621.110 1620 16 5360.464 5274.520 5274.221 0.2027 0.6142
## 3 SDT 2 1946.258 1620 14 3995.979 3920.743 3920.517 0.0000 0.0950
## 4 WEV 2 1827.221 1620 16 3772.684 3686.741 3686.441 0.0512 0.1920
## 5 SDT 3 1706.178 1620 14 3515.818 3440.582 3440.356 0.2708 0.4673
## 6 WEV 3 1661.617 1620 16 3441.476 3355.533 3355.233 0.4146 0.8561
## d_3 d_4 d_5 c theta_minus.4 theta_minus.3 theta_minus.2
## 1 1.0526 3.6806 4.7779 -0.2723 -1.5467 -1.0333 -0.6336
## 2 1.0797 3.4746 4.0799 -0.2957 -2.0665 -1.2485 -0.4152
## 3 0.8601 6.1410 8.0556 -0.1394 -2.0092 -1.9193 -1.4097
## 4 1.0412 4.1423 5.2886 -0.1475 -2.0441 -1.9500 -1.3982
## 5 1.9117 6.4257 7.5755 -1.1510 -1.9938 -1.6372 -1.2600
## 6 2.7115 6.9164 7.9863 -1.3743 -2.7625 -1.9192 -0.3724
## theta_minus.1 theta_plus.1 theta_plus.2 theta_plus.3 theta_plus.4 sigma
## 1 -0.4543 -0.0944 0.2152 0.9850 1.5735 NA
## 2 0.1296 -0.6196 0.1544 1.3976 2.1879 1.0105
## 3 -0.9580 0.7857 1.3781 2.0879 2.2369 NA
## 4 -0.9030 0.8201 1.4484 2.2447 2.4030 0.6391
## 5 -1.1668 -1.1143 -0.7344 0.2961 0.9314 NA
## 6 0.9328 -2.7695 -1.1313 0.7714 1.7520 1.3289
## w wAIC wAICc wBIC
## 1 NA 0 0 0
## 2 0.5361 1 1 1
## 3 NA 0 0 0
## 4 0.5020 1 1 1
## 5 NA 0 0 0
## 6 0.3818 1 1 1

If the Independent Truncated Gaussian model provides a decent account of
the data (which is not the case though in the demo dataset), it is
legitimate to quantify metacognitive efficiency with meta-d′/d′:

MetaDs <- fitMetaDprime(subset(MaskOri, diffCond == "33.3"),
model="ML", .parallel = TRUE)

## Contact

Expand All @@ -150,6 +139,11 @@ or [submit an issue](https://github.com/ManuelRausch/StatConfR/issues).

## References

Hellmann, S., Zehetleitner, M., & Rausch, M. (2023). Simultaneous
modeling of choice, confidence, and response time in visual perception.
Psychological Review. 130(6), 1521–1543.
[doi:10.1037/rev0000411](https://doi.org/10.1037/rev0000411)

Rausch, M., Hellmann, S. & Zehetleitner, M. (2023). Measures of
metacognitive efficiency across cognitive models of decision confidence.
Psychological Methods.
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25 changes: 13 additions & 12 deletions README.rmd
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Expand Up @@ -24,8 +24,7 @@ binary discrimination tasks, as well as meta-d′/d′ (Rausch & Hellmann,
2024). The package can be used to test the assumptions underlying
meta-d′/d′. Several models provide a metacognition parameter that may
serve as an alternative when the assumptions of meta-d′/d′ assuming the
corresponding model provides a better fit to the data. Currently, the
following models are included:
corresponding model provides a better fit to the data. At this point in time, the following models are included:

- Signal detection rating model

Expand All @@ -37,7 +36,7 @@ following models are included:

- Independent Gaussian model

- Independent truncated gaussian Model (the model underlying the
- Independent truncated Gaussian model (the model underlying the
meta-d′/d′ method, see Rausch et al., 2023)

- Lognormal noise model
Expand All @@ -46,19 +45,21 @@ following models are included:

## Installation

For the current development version, the easiest way of installation is
The latest released version of the package is available on
CRAN via

```
install.packages("statConfR")
```


The easiest way to install the development version is
using `devtools` and install from GitHub:

```
devtools::install_github("ManuelRausch/StatConfR")
```

The latest released version of the package will soon be available on
CRAN via

``` r
install.packages("statConfR")
```

## Usage

Expand Down Expand Up @@ -98,7 +99,7 @@ models to the data of each participant. The argument
`.parallel=TRUE`allows for parallelization over all but one available
core.

```{r}
```
fitted_pars <- fitConfModels(MaskOri, models=c("SDT", "WEV"), .parallel = TRUE)
```

Expand Down Expand Up @@ -242,7 +243,7 @@ head(fitted_pars)

If the Independent Truncated Gaussian model provides a decent account of the data (which is not the case though in the demo dataset), it is legitimate to quantify metacognitive efficiency with meta-d′/d′:

``` {r}
```
MetaDs <- fitMetaDprime(subset(MaskOri, diffCond == "33.3"),
model="ML", .parallel = TRUE)
```
Expand Down

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