Mattan S. Ben-Shachar
@mattansb.msbstats.info
Statistics lecturer | Freelance statistical consultant & research analyst | #rstats dev @easystats.github.io
home.msbstats.info
(He/Him)
home.msbstats.info
(He/Him)
So I learned that 2-yellow is "good", but in the last step I was shown 2-red and 3-yellow, which to choose?
If I choose 2-red, I focused on the numerosity dimension!
This produced a very reliable measure that was ALSO related to better acuity in quantity discrimination - just like in children!
If I choose 2-red, I focused on the numerosity dimension!
This produced a very reliable measure that was ALSO related to better acuity in quantity discrimination - just like in children!
January 11, 2026 at 4:30 PM
So I learned that 2-yellow is "good", but in the last step I was shown 2-red and 3-yellow, which to choose?
If I choose 2-red, I focused on the numerosity dimension!
This produced a very reliable measure that was ALSO related to better acuity in quantity discrimination - just like in children!
If I choose 2-red, I focused on the numerosity dimension!
This produced a very reliable measure that was ALSO related to better acuity in quantity discrimination - just like in children!
Subjects learned which of two easily distinguishable stimuli earn them points: easily distinguishable on two dimensions: color (e.g., red vs yellow) and numerosity (e.g., 3 vs 2 dot-arrays). Very easy!
Then, in the last step, the dimensions are *switched* w/o warning and their choice is recorded.
Then, in the last step, the dimensions are *switched* w/o warning and their choice is recorded.
January 11, 2026 at 4:30 PM
Subjects learned which of two easily distinguishable stimuli earn them points: easily distinguishable on two dimensions: color (e.g., red vs yellow) and numerosity (e.g., 3 vs 2 dot-arrays). Very easy!
Then, in the last step, the dimensions are *switched* w/o warning and their choice is recorded.
Then, in the last step, the dimensions are *switched* w/o warning and their choice is recorded.
The grad student I was working with was studying Spontaneous Focusing on Numerosity (SFON) in children - attending to the number aspect of objects w/o being prompted - and had found that it was related to better acuity in quantity discrimination.
But how can we measure SFON in adults?
But how can we measure SFON in adults?
January 11, 2026 at 4:30 PM
The grad student I was working with was studying Spontaneous Focusing on Numerosity (SFON) in children - attending to the number aspect of objects w/o being prompted - and had found that it was related to better acuity in quantity discrimination.
But how can we measure SFON in adults?
But how can we measure SFON in adults?
▰▰▰▰▰▰▰▰ 100%
December 21, 2025 at 4:18 PM
▰▰▰▰▰▰▰▰ 100%
▰▰▰▰▰▰▱▱ 75%
Please, no more fried food.
Okay, maybe one more sfenj 🤤
Please, no more fried food.
Okay, maybe one more sfenj 🤤
December 19, 2025 at 4:49 PM
▰▰▰▰▰▰▱▱ 75%
Please, no more fried food.
Okay, maybe one more sfenj 🤤
Please, no more fried food.
Okay, maybe one more sfenj 🤤
▰▰▰▰▱▱▱▱ 50%
Now with latkas! 🤤
Now with latkas! 🤤
December 17, 2025 at 4:34 PM
▰▰▰▰▱▱▱▱ 50%
Now with latkas! 🤤
Now with latkas! 🤤
Happy holidays!
A Menora is really just a Hanukkah progress bar:
▰▰▰▱▱▱▱▱ 37.5%
A Menora is really just a Hanukkah progress bar:
▰▰▰▱▱▱▱▱ 37.5%
December 16, 2025 at 4:13 PM
Happy holidays!
A Menora is really just a Hanukkah progress bar:
▰▰▰▱▱▱▱▱ 37.5%
A Menora is really just a Hanukkah progress bar:
▰▰▰▱▱▱▱▱ 37.5%
You can catch up / re create the experience!
December 13, 2025 at 9:06 AM
You can catch up / re create the experience!
For within person effects, @ianhussey.mmmdata.io has this "delicious" benchmark:
easystats.github.io/effectsize/r...
easystats.github.io/effectsize/r...
December 9, 2025 at 5:45 PM
For within person effects, @ianhussey.mmmdata.io has this "delicious" benchmark:
easystats.github.io/effectsize/r...
easystats.github.io/effectsize/r...
December 3, 2025 at 8:29 PM
I'd say I need to get a hobby... but this is my hobby! 💙🩵
![library(atrrr)
library(dplyr)
library(tidytext)
library(wordcloud)
# authetication via app specific password not shown
# auth()
me <- get_skeets_authored_by(actor = "mattansb.msbstats.info", limit = 6700)
common_words <- me |>
select(text) |>
unnest_tokens(word, text) |>
anti_join(stop_words, by = "word") |>
count(word, sort = TRUE) |>
filter(n > 1) |>
filter(!grepl("^[0-9]+$", word))
wordcloud(
words = common_words$word,
freq = common_words$n,
max.words = 100,
random.order = FALSE,
colors = RColorBrewer::brewer.pal(8, "BuPu")
)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:kwjddjickma3syepdsfufyvi/bafkreibvh5axqt5vwaldbdzuwwe4yz6mj4griyjrtbq35oiseavj6jelj4@jpeg)
November 29, 2025 at 8:38 PM
I'd say I need to get a hobby... but this is my hobby! 💙🩵
IDK about this one
November 27, 2025 at 2:34 PM
IDK about this one
I was actually surprised how well a lognormal-lognormal mixture model captured the data!
November 27, 2025 at 9:18 AM
I was actually surprised how well a lognormal-lognormal mixture model captured the data!
Build a an example for next week's class of using #shiny in @quarto.org with results from a #brms mixture model.
I think it turned out pretty cool 😎
I think it turned out pretty cool 😎
November 27, 2025 at 9:18 AM
Build a an example for next week's class of using #shiny in @quarto.org with results from a #brms mixture model.
I think it turned out pretty cool 😎
I think it turned out pretty cool 😎
MORE DATA + MORA ALGORITHM = MORE MONEY
November 19, 2025 at 4:53 PM
MORE DATA + MORA ALGORITHM = MORE MONEY
What a little gemstone from {tidytext} to order axes *within* facets!
#rstats
juliasilge.github.io/tidytext/ref...
#rstats
juliasilge.github.io/tidytext/ref...
November 19, 2025 at 10:36 AM
What a little gemstone from {tidytext} to order axes *within* facets!
#rstats
juliasilge.github.io/tidytext/ref...
#rstats
juliasilge.github.io/tidytext/ref...
Seems not inconsequential IMO. It's weird though, because it's pulling very strongly in opposite directions - seems to result in a strange platykurtic shape? Compare to a flat prior (3rd image)
November 18, 2025 at 9:58 PM
Seems not inconsequential IMO. It's weird though, because it's pulling very strongly in opposite directions - seems to result in a strange platykurtic shape? Compare to a flat prior (3rd image)
The default prior for the intercept in both {rstanarm} and {brms} are very wide.
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
November 18, 2025 at 2:01 PM
The default prior for the intercept in both {rstanarm} and {brms} are very wide.
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
Counterintuitively - being on the logit scale, this is actually translates to a **strong** prior that p(y=1) is near 1 or near 0.
Always check your priors!
#rstats
oh yes, very nice!
November 16, 2025 at 1:36 PM
oh yes, very nice!