i have very rapidly become incapable of thinking about anything that isn’t this video

I try to teach my kids this all the time. In order to become good at something, you have to be willing to be bad at it first. I worry that our kids will forget the messy process of learning things. AI promises "woah, I know kung fu," but minds and bodies don't work that way.

We are looking for a new colleague!🧠🇨🇭🦩
A new post doc position is available in our lab - check the link for more details!

www.unige.ch/fapse/womcog...

I worry about putting out a vibe against my express wishes to receive questions/feedback from students and mentees. Yet, when an undergrad student includes "fuck" in his question after class and an RA tells me I'd be "diabolical" to do an exam before spring break...maybe signals that I'm doing OK 😂

a little girl is making a funny face while wearing a blue shirt with a leopard print . ALT: a little girl is making a funny face while wearing a blue shirt with a leopard print .

We are excited to announce that we will host WMS2026!
The tentative dates are July 14th-17th, and we are currently looking for a postdoc to join the WMS2026 organizer team.
If you are interested, please submit your application using the link below (Deadline: March 29th)

forms.gle/noqsuEja2tB8...

Real-world Objects Scaffold Visual Working Memory for Features: Increased Neural Engagement When Colors Are Remembered as Part of Meaningful Objects Abstract. Visual working memory is a core cognitive function that allows active storage of task-relevant visual information. Contrary to the common assumption that the capacity of this system is fixed...

New paper with @timbrady.bsky.social and @violastoermer.bsky.social now out in JoCN! "Real-world Objects Scaffold Visual Working Memory for Features: Increased Neural Engagement When Colors Are Remembered as Part of Meaningful Objects" doi.org/10.1162/JOCN...

Our paper is now available online! Check out the Kudos summary here:
link.growkudos.com/1ed9uhpo8w0

OpenWMData A collection of publicly available working memory datasets

Do you have an open working memory dataset and want it to be findable and reused? You can now add it to the Open WM Data Hub: williamngiam.github.io/OpenWMData! The collection of datasets tagged with useful metadata is steadily growing thanks to a small team of volunteers!

Transparent and comprehensive statistical reporting is critical for ensuring the credibility, reproducibility, and interpretability of psychological research. This paper offers a structured set of guidelines for reporting statistical analyses in quantitative psychology, emphasizing clarity at both the planning and results stages. Drawing on established recommendations and emerging best practices, we outline key decisions related to hypothesis formulation, sample size justification, preregistration, outlier and missing data handling, statistical model specification, and the interpretation of inferential outcomes. We address considerations across frequentist and Bayesian frameworks and fixed as well as sequential research designs, including guidance on effect size reporting, equivalence testing, and the appropriate treatment of null results. To facilitate implementation of these recommendations, we provide the Transparent Statistical Reporting in Psychology (TSRP) Checklist that researchers can use to systematically evaluate and improve their statistical reporting practices (https://osf.io/t2zpq/). In addition, we provide a curated list of freely available tools, packages, and functions that researchers can use to implement transparent reporting practices in their own analyses to bridge the gap between theory and practice. To illustrate the practical application of these principles, we provide a side-by-side comparison of insufficient versus best-practice reporting using a hypothetical cognitive psychology study. By adopting transparent reporting standards, researchers can improve the robustness of individual studies and facilitate cumulative scientific progress through more reliable meta-analyses and research syntheses.

Our paper on improving statistical reporting in psychology is now online 🎉

As a part of this paper, we also created the Transparent Statistical Reporting in Psychology checklist, which researchers can use to improve their statistical reporting practices

www.nature.com/articles/s44...

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

Here is your chance to become an associate editor of a fantastic journal! The incoming editor of @jcgntn.bsky.social, @davidecrepaldi.bsky.social, is looking for associate editors. Check out this cool opportunity to self-nominate or nominate someone you know!

www.escop.eu/about-us/jou...

Dariia Mykhailyshyna - Workshops for Ukraine Feedback on the past workshops (if you want to learn how to make wordclouds, check out Text Data Analysis workshop below)

I’ll be giving a 'Workshops for Ukraine' session on Building and Customising Statistical Models with Stan and R: An Introduction to Bayesian Inference — online on Nov 13.

Open to all, with donations supporting Ukrainian organisations.

👉 sites.google.com/view/dariia-...

#stats #rstats #statssky

The final part of my PhD work is now published in JEP:LMC 🤩 Special thanks to my wonderful PhD supervisors @evievergauwe.bsky.social and @nlangerock.bsky.social 🤗 psycnet.apa.org/fulltext/202...

These results suggest that the benefits of prior knowledge, such as topic expertise, reflect contributions from long-term memory (LTM) to WM, rather than an increased efficiency of WM functions like bindings. I'd love to replicate with younger experts (could not get them!) and other WM functions! 🤩

A figure of the main results from Blocks 1 and 2 of the study.

Results: Expertise didn't moderate age-related slowing to establish bindings in WM, regardless of the relevance of the information (intact vs scrambled birds; Block 1). Instead, older experts showed a benefit to binding memory that disappeared when long-term memory was unreliable (high PI; Block 2).

A correlation matrix of the objective and subjective expertise measures on the left panel, and how the expertise groups were defined (kmeans clustering that is used in the paper) versus treating expertise as a continuous score.

How did we identify participants for these three groups? Subjective ratings clearly correlated with a perceptual discrimination task, which itself signaled a separate kmeans cluster of older experts. These experts were far superior at identifying birds vs the novices, validating the separate groups.

The main design of the Loaiza et al. birding study: Two blocks that both entailed a WM task that presented several bird-word pairs to participants followed by an immediate test, wherein the bird image was presented with three recall options (the correct target word from the trial, a lure word that was presented in the trial but not with that bird, and a new-to-block word).

Why does information seem so much easier to process when it coheres with our prior knowledge? We investigated this question by recruiting three groups (younger novices, older novices, and older experts in birding) to take part in a working memory (WM) study, the critical parts pictured below.

I am pleased to share that "the bird study" is now accepted at Psychology and Aging! A great collaboration with visiting intern Kishen Senziani, @leabartsch.bsky.social & @edamizrak.bsky.social 😀 Check out the pre-print below and a short thread on the study design and main takeaways 🧵👇

Congrats to @lauraklatt.bsky.social for being the #WomWoM research fairy of 2025! She is both a star researcher & incredibly generous and collegial with all in the field, especially the juniors! Thank you to everyone who sent in nominations & to @scannedfruits.bsky.social for making Laura's wand 🪄🧚‍♀️

Hey #memory folks, check out this new preprint by @joschadutli.bsky.social , @koberauer.bsky.social, and @leabartsch.bsky.social showing that elaboration benefits are likely driven by aiding in establishing efficient retrieval cues.

Yes, this was a really great experience! Thanks for @cvonbastian.bsky.social and @vmloaiza1.bsky.social for asking us to do this as part of #ESCOP2025! And if you are interested in having a similar workshop for your PhD Programm or similar, feel free to reach out!

Leeds WoMCog group at the #ESCoP2025 conference in Sheffield!

I’m going to present at the Blitz Talk on Friday, 6pm, at #ESCOP2025 @escop.bsky.social. The very last session, yet I’m sure with a lot of excitement! Come to listen whether engaging in your hobby predict your cognitive abilities. 🧠

Here is the paper that @mollyadelooze.bsky.social presented this morning at #escop2025 showing source amnesia for recently presented verbal information.

It’s been an excellent few days in Sheffield for #ESCoP2025! I have enjoyed so many talks, posters, and chats, and have come away feeling inspired and excited about research ideas and topics happening across labs 🤩

maps.app.goo.gl/ZyeuVoiPSrxW...
I've saved some of my faves after over a year of living here!

Tomorrow afternoon I'll be presenting my symposium talk at #ESCoP2025 titled "Meaningful and familiar stimuli support visual working memory for simple features"! See you there!

Sure do!! 😁

It's all starting soon 🥳 we can't wait to welcome you all to Sheffield for #ESCoP2025 🤩 @cvonbastian.bsky.social @escop.bsky.social