diff --git a/R/priors.R b/R/priors.R
index 8aaaaddcf..12edfbb55 100644
--- a/R/priors.R
+++ b/R/priors.R
@@ -1906,12 +1906,12 @@ eval_dirichlet <- function(prior, len = NULL, env = NULL) {
 #'   is greater \code{1}, the shape of the prior will no longer resemble
 #'   a horseshoe and it may be more appropriately called an hierarchical
 #'   shrinkage prior in this case.
-#' @param scale_slab Scale of the student-t prior of the regularization
-#'   parameter. Defaults to \code{2}. The original unregularized horseshoe
-#'   prior is obtained by setting \code{scale_slab} to infinite, which
-#'   we can approximate in practice by setting it to a very large real value.
-#' @param df_slab Degrees of freedom of the student-t prior of
-#'   the regularization parameter. Defaults to \code{4}.
+#' @param scale_slab Scale of the Student-t slab. Defaults to \code{2}. The
+#'   original unregularized horseshoe prior is obtained by setting
+#'   \code{scale_slab} to infinite, which we can approximate in practice by
+#'   setting it to a very large real value.
+#' @param df_slab Degrees of freedom of the student-t slab.
+#'   Defaults to \code{4}.
 #' @param par_ratio Ratio of the expected number of non-zero coefficients
 #'   to the expected number of zero coefficients. If specified,
 #'   \code{scale_global} is ignored and internally computed as
diff --git a/man/horseshoe.Rd b/man/horseshoe.Rd
index 7b0925b72..3cf203aa6 100644
--- a/man/horseshoe.Rd
+++ b/man/horseshoe.Rd
@@ -29,13 +29,13 @@ is greater \code{1}, the shape of the prior will no longer resemble
 a horseshoe and it may be more appropriately called an hierarchical
 shrinkage prior in this case.}
 
-\item{scale_slab}{Scale of the student-t prior of the regularization
-parameter. Defaults to \code{2}. The original unregularized horseshoe
-prior is obtained by setting \code{scale_slab} to infinite, which
-we can approximate in practice by setting it to a very large real value.}
+\item{scale_slab}{Scale of the Student-t slab. Defaults to \code{2}. The
+original unregularized horseshoe prior is obtained by setting
+\code{scale_slab} to infinite, which we can approximate in practice by
+setting it to a very large real value.}
 
-\item{df_slab}{Degrees of freedom of the student-t prior of
-the regularization parameter. Defaults to \code{4}.}
+\item{df_slab}{Degrees of freedom of the student-t slab.
+Defaults to \code{4}.}
 
 \item{par_ratio}{Ratio of the expected number of non-zero coefficients
 to the expected number of zero coefficients. If specified,