diff --git a/README.rmd b/README.rmd
index a1bc722..107c4b8 100644
--- a/README.rmd
+++ b/README.rmd
@@ -66,7 +66,7 @@ install.packages("statConfR")
 ### 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)
@@ -79,32 +79,27 @@ 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.
+-   participant (integer): giving the subject ID.
 
-```{r}
-MaskOri$condition <- MaskOri$diffCond
-head(MaskOri)
-```
 
 ### 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
+```  {r}
  fitted_pars <- fitConfModels(MaskOri, models=c("SDT", "WEV"), .parallel = TRUE) 
 ```
 
@@ -462,6 +457,8 @@ 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.