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Jonathan M. Fawcett

@jmfawcet.bsky.social

Associate Professor at Memorial University of Newfoundland specializing in forgetting, distinctive encoding, eyewitness memory, meta-analysis, and general academic survival.

14 Posts60 Follower136 Folgt
Daniel Lakens@lakens.bsky.social · 3mo ago

Reviewing a paper, and it is so nice that the Metacheck R package we made allows me to download all OSF Files by just typing: metacheck::osf_file_download("2w123") (not the actual repo). All files are downloaded, maintaining the folder structure, without any hassle.

Jonathan M. Fawcett@jmfawcet.bsky.social · 3mo ago

Are you interested in the weapon focus effect? How about using virtual reality to study eyewitness memory? Here's a piece about some work from our group on the topic. Also, nice to see NeuroFog lab members Mikayla, Soheil and Chloe featured in the thumbnail!

Faculty of Science, Memorial University@memorialuscience.bsky.social · 3mo ago

Weapon-focus is the study of eyewitness memory and the ability to recall details when threatened with a weapon. Dr. Jonathan Fawcett leads a multinational team of experts who research the phenomenon, including a team of @memorialu.bsky.social student researchers. gazette.mun.ca/research/vir...

Jonathan M. Fawcett@jmfawcet.bsky.social · 4mo ago

Congrats to former NeuroFog lab member @noahpevie.bsky.social - I know he's been working on this one for a long time! Good to finally see it out :)

Noah Pevie@noahpevie.bsky.social · 4mo ago

SO EXCITED to share this work (with the lovely @queersian.bsky.social, @cq-n.bsky.social, and @johnsakaluk.bsky.social) on trans-for-trans (T4T) attraction! In short, T4T is a label used by trans people to denote romantic/sexual attraction to other trans people. 📝: osf.io/preprints/ps... 🧵 (1)

Jonathan M. Fawcett@jmfawcet.bsky.social · 4mo ago

Our meta-analysis of the prevalence of infant abuse made the cover of eClinicalMedicine! Core finding: It'a more common than you think. Congrats to NeuroFog members Ada Vaziri and Chelsea Lahey, lead author Cora Keeney and co-senior author Nichole Fairbrother (and everyone, else too, of course!)

eClinicalMedicine – The Lancet Discovery Science@eclinicalmed.bsky.social · 4mo ago

📢NEW: March issue now online featuring #obesity, #endometriosis, #KawasakiDisease, #diabetes, #SpinalCordInjury, #HIV, and much more with our editorial on risks of AI-generated health advice …all available #free, #OpenAccess❗️ Read here: www.thelancet.com/issue/S2589-... Attribution: Getty #MedSky

Daniel Lakens@lakens.bsky.social · 5mo ago

I wish more people knew this. Power analysis should be based on the smallest effect size of interest. Not on a guess, or a hope. You also need to specify that effect to make your claim falsifiable, and to know when the effect is statistically significant, but practically irrelevant.

Cyrus Samii@cdsamii.bsky.social · 5mo ago

I teach power analysis in terms of “what is a minimally meaningful effect size? Are you powered for that?” A minimally meaningful effect size can be deduced from principles (eg cost-benefit), eliminating the kind of guesswork you mentioned.

Marcus Crede@marcuscrede.bsky.social · 5mo ago

We recently submitted a commentary on a very influential meta-analysis. We found that: 1) 40% of relevant literature had not been identified because of lazy search, 2) a few large N included studies did not meet stated inclusion criteria, and 3) that almost all sig. moderator findings were wrong.

Jonathan M. Fawcett@jmfawcet.bsky.social · 6mo ago

Check out this recent paper from NeuroFog and AWE lab members exploring the prevalence of obsessions relating to harming oneself or others in OCD! It's more prevalent than many people realize.

Department of Psychology - Memorial University of Newfoundland@munpsych.bsky.social · 6mo ago

Congrats to Quinn Morris, Chelsea Lahey, Olivia Bishop, Concordia co-authors & Drs. Josh Rash, Jacqueline Carter, Jonathan Fawcett & Emily Fawcett on a new meta-analysis! They found that ~70% of people with OCD report aggressive obsessions in their lifetime. shorturl.at/tJS4n

Screenshot of the article titled "The prevalence and predictors of aggressive obsessions in obsessive-compulsive disorder: A meta-analytic review"
Jonathan M. Fawcett@jmfawcet.bsky.social · 7mo ago

If you're a Clinician looking for a faculty position, or knows someone who is, have I got some great news for you! Memorial University is hiring 2 Open Rank positions! We're practically the North American gateway to Europe!

Department of Psychology - Memorial University of Newfoundland@munpsych.bsky.social · 7mo ago

Memorial University's Department of Psychology is hiring! Be a part of a vibrant and engaged academic community in beautiful St. John's, NL. 🌊 Details below! www.mun.ca/academic-car...

Richard McElreath 🐈‍⬛@rmcelreath.bsky.social · 7mo ago

Getting my new slides in shape. I have it on good authority that the typeface I have chosen is "woke" (aka sans serif)

title slide for Statistical Rethinking 2026 Lecture 1: Introduction to Bayesian workflow. The typeface is Atkinson Hyperlegible, a very readable sans serif typeface.
Richard McElreath 🐈‍⬛@rmcelreath.bsky.social · 8mo ago

I'm teaching Statistical Rethinking again starting Jan 2026. This time with live lectures, divided into Beginner and Experienced sections. Will be a lot more work for me, but I hope much better for students. I will record lectures & all will be found at this link: github.com/rmcelreath/s...

course schedule as a table. Available at the link in the post.
Richard McElreath 🐈‍⬛@rmcelreath.bsky.social · 7mo ago

Including the old black cat adoptions survival analysis example as a case study in the forthcoming Bayesian Workflow book. This is presented as a whole incremental workflow with simulation, validation, and model comparison. Just now went through code and extra-commented and cleaned. Getting close!

Incremental development and testing: Black cat adoptions Even when we know the final statistical model that we want to use for inference, we should not try to write it directly. It is better to develop simpler, incremental models and test each with synthetic data. This helps us to avoid the frustration of trying to debug a complex model. Large models can and usually do fail in multiple ways, due to a poison salad\subjindex{poison salad} of coding errors, misspecification, and estimation challenges. % Being smart means working smart. By starting with a simple, minimal model and adding one feature at a time, we have a better chance of knowing which portion of the model code is responsible for an error, misspecification, or poor convergence. This case study builds the target statistical model in several steps, while using synthetic data simulation to help construct and test each incremental model. This helps us construct the model, notice and evaluate alternative implementations, and better understand how the model performs. This case study also provides an example of survival analysis with censoring. This kind of problem is commonplace---there are observations that are only partially observed, and we need to use all the information, even if only partial. Bayesian implementation provides two different ways to implement censored observations, by using cumulative distributions corresponding to the ordinary data model or by treating each censored value as partially observed and imputing it using the data model. Neither approach is always superior, and each helps us understand the model better. We'll show you both. Another benefit of this kind of example is the generative model of the sample and the statistical model necessarily differ. We often say Bayesian models are generative, they can be used to simulate observations. And that's true. But it isn't always true of every aspect of the model. In the case of censored values, the censoring is part of the observation m…Prior predictive distribution of waiting times for the first adoption model (without censoring). Each curve is a survival plot for an individual prior simulation. Black curves correspond to black cats. Orange curves correspond to all other cat colors.Posterior predictive distributions of waiting times for the first adoption model (without censoring). Each curve is a survival plot for an individual posterior simulation. Black curves correspond to black cats. Orange curves correspond to all other cat colors.
Jonathan M. Fawcett@jmfawcet.bsky.social · 8mo ago

This. I often justify sample size based on resources (participants are rare here). Every review demands a priori power analysis. Then, most papers I read include incorrectly conducted and interpreted power analyses. Editors often don't care they are incorrect if told. Statistical rituals at work.

Mattan S. Ben-Shachar@mattansb.msbstats.info · 8mo ago

Examples: ❗Statistical power has become the *only* way to justify sample sizes, so papers are filled with BS section on an a priori power analysis that they didn't do, instead of being ---honest--- (budget, time, informal past experiences with similar Ns). 8/

Jonathan M. Fawcett@jmfawcet.bsky.social · 9mo ago

Did you know your pupils get bigger when you're thinking harder? Are you interested in understanding why reading something aloud helps you remember it better? Check out this recent paper by multiple current and former NeuroFoggies using pupil dilation to study the production effect!

Department of Psychology - Memorial University of Newfoundland@munpsych.bsky.social · 9mo ago

New research from Drs. Jonathan Fawcett and Kathleen Hourihan, alumni Hannah Willoughby and Jenny Tiller, and team found that reading aloud triggers larger pupil dilation than reading silently, suggesting that speaking aloud enhances attention and encoding. See full article: shorturl.at/MRQRA

Screenshot of the article