Babies Learning Language

As I’m discussing paper writing with more folks in the age of Claude Code and other coding assistants, we’re having more discussions about how to use these tools in writing.

[This post is joint with Mika Braginsky, a long-time collaborator on data-sharing and data-viz.]

Everyone seems to be consumed with AI anxiety.

My hot take on college-level teaching is that reading responses are mostly a terrible assignment, and they're even worse in the age of AI.

I'm normally an avid fiction reader, but this summer I've been on a non-fiction kick.

After a long stint primarily reading fiction, I've been on a non-fiction kick recently and just read two books that I would definitely recommend!

One of the saddest things in academic research is an abandoned project.

[repost from Bluesky] Three ManyBabies projects - big collaborative replications of infancy phenomena - wrapped up this year.

Data sharing is a critical part of ensuring a reproducible and robust research literature.

[repost from twitter] How do we compare the scale of language learning input for large language models vs. humans?

[reposted from twitter] My lab held a hackathon yesterday to play with places where large language models could help us with our research in cognitive science.

I tried rendering this post in Quarto, which is not blogger-compatible, but I'm including the link here: rpubs.com/mcfrank/hybrid.

(tl;dr: "Ugh, can't we just get along?!"

(tl;dr: I wrote an opinion piece a couple of years ago - now rejected - on the connection between ethics and open science.

Many academic conferences and journals have limits on the number of references you can cite.

(Several people I work with have recently asked me for reviewing advice, so I thought I'd share my thoughts more broadly.)

(tl;dr: It's letter of recommendation season, and so I decided to write one to a paper that's really been influential in my recent thinking.

(tl;dr: explanation of confounding and covariate adjustment) Every year, one of the trickiest concepts for me to teach in my experimental methods course is the difference between experimental confounds and covariates.

Let's do a thought experiment.

(tl;dr: wonky post on statistical modeling) I fit linear mixed effects models (LMMs) for most of the experimental data I collect.

I don't often get the chance to talk directly and openly to people who are skeptical of the methodological reforms that are being suggested in psychology.

I'm awestruck by the processes of development that operate over children's first five years.

(tl;dr: Design features of psychology studies to avoid if you want to run a good study!)

For psychometrics fans: I helped out with a post by Brent Roberts, "Yes or No 2.0: Are Likert scales always preferable to dichotomous rating scales?"

(tl;dr: Some questions I'm thinking about, inspired by the idea of studying the broad structure of child development through larger-scale datasets.)