diff --git a/R/as.textmodel2.R b/R/as.textmodel2.R
index 1567753..e82ff1e 100644
--- a/R/as.textmodel2.R
+++ b/R/as.textmodel2.R
@@ -1,12 +1,20 @@
 
 #' @rdname as.textmodel_lss
 #' @export
+#' @param spatial \[experimental\] if `FALSE`, return a probabilistic model.
+#'   See the details.
+#' @details
+#'   If `spatial = TRUE`, it return a spatial model; otherwise a probabilistic model.
+#'   While the polarity scores of words are their cosine similarity to seed words in
+#'   spatial models, they are predicted probability that the seed words to occur in
+#'   their contexts.
+#'
 #' @method as.textmodel_lss textmodel_word2vec
 as.textmodel_lss.textmodel_word2vec <- function(x, seeds,
                                                   terms = NULL,
                                                   nested_weight = TRUE,
+                                                  spatial = FALSE,
                                                   verbose = FALSE,
-                                                  spatial = TRUE,
                                                   ...) {
 
   #args <- list(terms = terms, seeds = seeds)
diff --git a/R/textmodel_lss.R b/R/textmodel_lss.R
index 1fc32ef..cfe8b78 100644
--- a/R/textmodel_lss.R
+++ b/R/textmodel_lss.R
@@ -51,11 +51,8 @@
 #'   in `seed_weighted` in the object.
 #'
 #'   When `x` is a tokens or tokens_xptr object, [wordvector::textmodel_word2vec]
-#'   is called internally with `type = "skip-gram"` and other arguments passed via `...`.
-#'   If `spatial = TRUE`, it return a spatial model; otherwise a probabilistic model.
-#'   While the polarity scores of words are their cosine similarity to seed words in
-#'   spatial models, they are predicted probability that the seed words to occur in
-#'   their contexts. The probabilistic models are still experimental, so use them with caution.
+#'   is called internally with `type = "skip-gram"` and other arguments passed via
+#'   `...`.
 #'
 #'   `nested_weight = TRUE` to limit the impact of glob patterns used in seed words.
 #'   When it is `FALSE`, the weights of the seed words are all equal being the inverse of
diff --git a/R/textmodel_lss2.R b/R/textmodel_lss2.R
index 3a70604..5a67c65 100644
--- a/R/textmodel_lss2.R
+++ b/R/textmodel_lss2.R
@@ -1,5 +1,4 @@
 #' @rdname textmodel_lss
-#' @param spatial \[experimental\] if `FALSE`, return a probabilistic model. See the details.
 #' @export
 #' @inheritParams wordvector::textmodel_word2vec
 #' @importFrom quanteda dfm dfm_group
@@ -10,7 +9,6 @@ textmodel_lss.tokens <- function(x, seeds, terms = NULL, k = 200,
                                  nested_weight = TRUE,
                                  include_data = FALSE,
                                  group_data = FALSE,
-                                 spatial = TRUE,
                                  verbose = FALSE, ...) {
 
 
@@ -25,8 +23,8 @@ textmodel_lss.tokens <- function(x, seeds, terms = NULL, k = 200,
   w2v <- wordvector::textmodel_word2vec(x, dim = k, min_count = min_count,
                                         type = "skip-gram", tolower = tolower,
                                         normalize = FALSE, verbose = verbose, ...)
-  result <- as.textmodel_lss(w2v, seeds = seeds, terms = terms, spatial = spatial,
-                             nested_weight = nested_weight, verbose = FALSE)
+  result <- as.textmodel_lss(w2v, seeds = seeds, terms = terms,
+                             nested_weight = nested_weight, verbose = FALSE, ...)
   result$type <- "word2vec"
   result$call <- try(match.call(sys.function(-1), call = sys.call(-1)), silent = TRUE)
 
diff --git a/man/as.textmodel_lss.Rd b/man/as.textmodel_lss.Rd
index f3913c6..be1f4e3 100644
--- a/man/as.textmodel_lss.Rd
+++ b/man/as.textmodel_lss.Rd
@@ -30,8 +30,8 @@ as.textmodel_lss(x, ...)
   seeds,
   terms = NULL,
   nested_weight = TRUE,
+  spatial = FALSE,
   verbose = FALSE,
-  spatial = TRUE,
   ...
 )
 }
@@ -63,7 +63,8 @@ the number of glob pattern matches.}
 
 \item{verbose}{show messages if \code{TRUE}.}
 
-\item{spatial}{if \code{TRUE}, return a spatial model. Otherwise, a probabilistic model.}
+\item{spatial}{[experimental] if \code{FALSE}, return a probabilistic model.
+See the details.}
 }
 \value{
 a dummy \link{textmodel_lss} object
@@ -79,4 +80,9 @@ if it was trained originally using SVD.
 If \code{x} is a dense matrix, it is treated as a column-oriented word vectors with which
 polarity of words are computed. If \code{x} is a named numeric vector, the values are treated
 as polarity scores of the words in the names.
+
+If \code{spatial = TRUE}, it return a spatial model; otherwise a probabilistic model.
+While the polarity scores of words are their cosine similarity to seed words in
+spatial models, they are predicted probability that the seed words to occur in
+their contexts.
 }
diff --git a/man/as.textmodel_lss.textmodel_wordvector.Rd b/man/as.textmodel_lss.textmodel_wordvector.Rd
index c351f5b..00b8d58 100644
--- a/man/as.textmodel_lss.textmodel_wordvector.Rd
+++ b/man/as.textmodel_lss.textmodel_wordvector.Rd
@@ -9,8 +9,8 @@
   seeds,
   terms = NULL,
   nested_weight = TRUE,
+  spatial = FALSE,
   verbose = FALSE,
-  spatial = TRUE,
   ...
 )
 }
diff --git a/man/textmodel_lss.Rd b/man/textmodel_lss.Rd
index cf03965..c8286db 100644
--- a/man/textmodel_lss.Rd
+++ b/man/textmodel_lss.Rd
@@ -52,7 +52,6 @@ textmodel_lss(x, ...)
   nested_weight = TRUE,
   include_data = FALSE,
   group_data = FALSE,
-  spatial = TRUE,
   verbose = FALSE,
   ...
 )
@@ -112,8 +111,6 @@ size of the corpus. Used only when \code{x} is a \code{fcm}.}
 this in \code{x} are removed before training.}
 
 \item{tolower}{if \code{TRUE}, lower-case all the words in the model.}
-
-\item{spatial}{[experimental] if \code{FALSE}, return a probabilistic model. See the details.}
 }
 \description{
 Latent Semantic Scaling (LSS) is a semi-supervised algorithm for document scaling based on
@@ -141,11 +138,8 @@ between seed words' computed and original scores. Weighted scores are saved
 in \code{seed_weighted} in the object.
 
 When \code{x} is a tokens or tokens_xptr object, \link[wordvector:textmodel_word2vec]{wordvector::textmodel_word2vec}
-is called internally with \code{type = "skip-gram"} and other arguments passed via \code{...}.
-If \code{spatial = TRUE}, it return a spatial model; otherwise a probabilistic model.
-While the polarity scores of words are their cosine similarity to seed words in
-spatial models, they are predicted probability that the seed words to occur in
-their contexts. The probabilistic models are still experimental, so use them with caution.
+is called internally with \code{type = "skip-gram"} and other arguments passed via
+\code{...}.
 
 \code{nested_weight = TRUE} to limit the impact of glob patterns used in seed words.
 When it is \code{FALSE}, the weights of the seed words are all equal being the inverse of
diff --git a/tests/testthat/test-as.textmodel2.R b/tests/testthat/test-as.textmodel2.R
index 2bbe729..1bbba1f 100644
--- a/tests/testthat/test-as.textmodel2.R
+++ b/tests/testthat/test-as.textmodel2.R
@@ -7,7 +7,7 @@ test_that("as.textmodel_lss works with textmodel_wordvector", {
 
   # spatial
   wdv <- readRDS("../data/word2vec.RDS")
-  lss <- as.textmodel_lss(wdv, seed)
+  lss <- as.textmodel_lss(wdv, seed, spatial = TRUE)
 
   expect_equal(lss$beta_type, "similarity")
   expect_equal(lss$embedding, t(wdv$values))
diff --git a/tests/testthat/test-textmodel_lss2.R b/tests/testthat/test-textmodel_lss2.R
index e21f6a8..c7e8d0c 100644
--- a/tests/testthat/test-textmodel_lss2.R
+++ b/tests/testthat/test-textmodel_lss2.R
@@ -14,7 +14,7 @@ test_that("textmodel_lss works when spatial = TRUE", {
   skip_on_cran()
 
   # without data
-  lss1 <- textmodel_lss(toks_test, seed_test, k = 10)
+  lss1 <- textmodel_lss(toks_test, seed_test, k = 10, spatial = TRUE)
 
   expect_s3_class(lss1, "textmodel_lss")
   expect_equal(lss1$k, 10)
@@ -30,7 +30,8 @@ test_that("textmodel_lss works when spatial = TRUE", {
   )
 
   # with data
-  lss2 <- textmodel_lss(toks_test, seed_test, k = 10, include_data = TRUE)
+  lss2 <- textmodel_lss(toks_test, seed_test, k = 10, include_data = TRUE,
+                        spatial = TRUE)
 
   expect_s3_class(lss2, "textmodel_lss")
   expect_equal(lss2$concatenator, concatenator(toks_test))
@@ -42,7 +43,7 @@ test_that("textmodel_lss works when spatial = TRUE", {
 
   # with terms
   lss3 <- textmodel_lss(toks_test, seed_test, k = 10, terms = feat_test,
-                         include_data = TRUE, group_data = TRUE)
+                        include_data = TRUE, group_data = TRUE, spatial = TRUE)
 
   expect_s3_class(lss3, "textmodel_lss")
   expect_true(all(names(lss3$beta) %in% feat_test))
@@ -53,7 +54,7 @@ test_that("textmodel_lss works when spatial = TRUE", {
 
   # with tokens_xptr
   lss4 <- textmodel_lss(as.tokens_xptr(toks_test), seed_test, k = 10,
-                         include_data = TRUE)
+                        include_data = TRUE, spatial = TRUE)
 
   expect_s3_class(lss4, "textmodel_lss")
   expect_equal(docnames(lss4$data), docnames(toks_test))
@@ -61,7 +62,7 @@ test_that("textmodel_lss works when spatial = TRUE", {
   # warning
   expect_warning(
     textmodel_lss(toks_test, seed_test, k = 10,
-                   include_data = FALSE, group_data = TRUE),
+                  include_data = FALSE, group_data = TRUE, spatial = TRUE),
     "group_data is ignored when include_data = FALSE"
   )
 
@@ -99,7 +100,8 @@ test_that("textmodel_lss works when spatial = FALSE", {
   )
 
   # with data
-  lss2 <- textmodel_lss(toks_test, seed_test, k = 10, include_data = TRUE, spatial = FALSE)
+  lss2 <- textmodel_lss(toks_test, seed_test, k = 10, include_data = TRUE,
+                        spatial = FALSE)
 
   expect_s3_class(lss2, "textmodel_lss")
   expect_equal(lss2$concatenator, concatenator(toks_test))