iiinova — where your digital self keeps evolving

Jack Duff @thebarnfell.bsky.social · 15/06/2026

This paper emerged from years of collaborative work with Zapotec speakers in the Monterey Bay, Los Angeles, and Santiago Laxopa. To all who volunteered their time and efforts, duxklhenhu', and cheers for continued long-term collaborative research!

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Jack Duff @thebarnfell.bsky.social · 15/06/2026

Whatever the best explanation, we were excited and surprised to see such an unfamiliar pattern emerge here, and we're looking forward to future cross-linguistic processing research across diverse languages that can test these ideas further.

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Jack Duff @thebarnfell.bsky.social · 15/06/2026

Why would this language avoid early predictions? We discuss two promising options: either highly-specific predictions are even here in absence of an optional marker, or the patterns of ambiguity in the language make prediction relatively unimportant.

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Jack Duff @thebarnfell.bsky.social · 15/06/2026

Some familiar patterns do emerge elsewhere: for instance, we see fast resolution when there's disambiguation, and apparent memory interference when co-arguments are similar. But the different prediction profile suggests striking language-specificity.

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Jack Duff @thebarnfell.bsky.social · 15/06/2026

In many other languages, various evidence suggests an early prediction that the head will be the subject, especially for nouns describing humans. But we find that in this case, listeners wait until the next noun before showing a preferred meaning.

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Jack Duff @thebarnfell.bsky.social · 15/06/2026

In Santiago Laxopa Zapotec, relative clauses can be ambiguous between cases describing a head noun as a subject or an object. So, "bi'i xyage'nh txhube koche'nh" (the_boy [pulls the_car]) can describe a boy who a car pulls, or who pulls a car.

Two images, side-by-side. The left image shows a green car pulling a man, who is attached by a rope to the back bumper, standing but about to fall over. The right image shows a man with a rope over his shoulder, pulling a green car on the other end of the rope.

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Jack Duff @thebarnfell.bsky.social · 24/03/2026

Through this work, I've built up a lot of zeal for (py)ACT-R as a still-useful tool for algorithmic-level modeling (not just for cue-based retrieval!)---if you want to chat, or take a look at some tutorial assignments I've put together, don't hesitate to get in touch! (or say hi at HSP!)

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Jack Duff @thebarnfell.bsky.social · 24/03/2026

Of course, the model shows some faults when it comes to fitting all the human data, but the part that most excites me here is the possibility of taking a hypothesis ("people use general skill learning to get situated in pragmatic tasks") and generating an explainable model with testable predictions.

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Jack Duff @thebarnfell.bsky.social · 24/03/2026

We model a default-literal reasoner that learns task-specific utility of implicatures on-task—a simple way to explain changes in responses as an experiment progresses. Cool part: correctly predicts that params tracking individual variation in on-task learning also track diffs in pragmatic tasks!!

Line plot titled "Empirical relationship with persistence", showing responses changing for individuals with higher persistence values. On the x-axis is "Participant persistence sextile", from 1 to 6, and on the y-axis is "Proportion of target selections", from a dotted line at 50% up to 100%. Three lines are plotted. A line for "Unambiguous" trials stays at 100% for all sextiles, a line for "Simple" trials grows from 60% to 80% across sextiles, and a line for "Complex" trials grows from 50% to 65%.

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Jack Duff @thebarnfell.bsky.social · 24/03/2026

This paper with Sasha Mayn and Vera Demberg was the main project of my post-doc at @lst.uni-saarland.de! Really proud of it, and happy to have it out in @openmindjournal.bsky.social. Headline: A time-conserving ACT-R agent does a nice job explaining low implicature rates in communication games.

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Jack Duff @thebarnfell.bsky.social · 05/09/2025

Bonus example: In my diss, I found a Maze slowdown aligned with the classic "subordinate access" effect on homonyms. I interpreted it then as just a target processing cost... but was it? Interestingly, diffusion models agree: cases of slowdowns without extra errors are well-fit as slower t0. Cool.

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Jack Duff @thebarnfell.bsky.social · 05/09/2025

Beyond just proof-of-concept, this is a cool way to see how Maze data comes from various processes, all affected by context. If your theory cares whether your Maze slowdowns come from target processing vs. foil processing vs decision-making, consider this modeling approach to get some insight!

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Jack Duff @thebarnfell.bsky.social · 05/09/2025

Some simple max-lik fitting (nice tutorial: cran.r-project.org/web/packages...), and we can see the best fit to Laura's data indeed comes from models where context-related foils lead to lower drift rate---but ALSO faster non-decision processing! These foils seem harder to ignore AND faster to read.

Heading says "faster non-decision time and slower v: many more errors and noise, but just a subtle increase in time". There are two plots below. On top is an unrelated-foil condition, where diffusion trajectories start late but progress fairly quickly towards the top boundary for a target response. Below is a related-foil condition, where diffusion trajectories start earlier but are very noisy and slow, many of them end at the bottom boundary for a foil response.

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Jack Duff @thebarnfell.bsky.social · 05/09/2025

Enter Ratcliff's diffusion model of decision-making, tried-and-true and easy to work with in R (thanks to @singmann.bsky.social and the rtdists package). Harder decisions can be modeled as lower drift rates, which have a joint effect, widening the distribution of expected RTs and increasing errors.

Two plots are compared, featuring collections of many diffusion-model trajectories overlayed on top of each other to create distributions of expected RTs and responses. The top plot is labeled "Harder decisions: lower drift rate" and has more errors and slower RTs. The bottom plot is labeled "Easier decisions: higher drift rate" and has fewer errors and faster RTs.

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Jack Duff @thebarnfell.bsky.social · 05/09/2025

Seems like both effects come from the foil making the decision harder. But we can go beyond "seems like", cognitive psych has a long tradition of modeling how decision difficulty affects both RTs and accuracy! We wanted to see if these patterns work with those models.

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Jack Duff @thebarnfell.bsky.social · 05/09/2025

We focus on how Maze measures can be affected by the difficulty of deciding between target and foil. In Laura's dissertation, she found a cool pattern: when you use a foil associated with the literal meaning of a prior metaphor ("early bird" → "fly"), you get slower RTs, and more errors.

Graphic demonstrating two trial types where sentences are seen in word by word self-paced reading, including the phrase "is an early bird". On top, there is a later Maze decision between the words "attend" or "fly". This decision had an average RT of 1377ms and an error rate of 18%. On the bottom, the decision is between "attend" and "cry", with average RT of 1269ms and error rate of 3%.

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Jack Duff @thebarnfell.bsky.social · 05/09/2025

Sad to be missing AMLaP, home with some surprise COVID, but very thankful to my co-author Laura Pissani for going solo on our Saturday talk on diffusion models for the Maze task! For anyone else stuck at home, let me tell you here about what we've learned by modeling Maze RTs as decision-making.

Title slide from a talk at AMLAP 2025 in Prague. Title is "Jointly modeling maze RT and accuracy using diffusion models: A first case study", by authors Jack Duff (UCLA) and Laura Pissani (Saarland). There is a graphic of a bird facing a series of diffusion model trajectories deciding between the words "attend" and "fly".

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Jack Duff @thebarnfell.bsky.social · 02/05/2025

... and that's not to mention the prospect of joining a department packed with thoughtful, curious, and imaginative linguists. I couldn't be more excited. Meanwhile, recommendations are fully open for favorite LA spots, haunts, and locales!

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Jack Duff @thebarnfell.bsky.social · 02/05/2025

This year, as the opportunities and support to do meaningful science in the US shrink day after day, I feel *beyond* fortunate to have this news. Especially since it brings a chance to double down on community-oriented work, + especially work to help serve 200k+!!! local speakers of Oaxacan langs.

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Jack Duff @thebarnfell.bsky.social · 02/05/2025

Overjoyed to share that this coming fall, I'll be joining UCLA Linguistics as an assistant professor of psycholinguistics!

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Jack Duff @thebarnfell.bsky.social · 28/08/2024

Gorgeous example of a preposition pruning error in what would have already been a confusing and wonderful RC. (Context: www.nytimes.com/athletic/572...)

NYTimes caption reading: "Top photo of Jansen at the plate for the Red Sox in a game he was the listed hitter for the Blue Jays before first pitch: Paul Rutherford / Getty Images"

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Jack Duff @thebarnfell.bsky.social · 27/02/2024

And yet it's so oddly productive! Even "Are we any of us..."

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Jack Duff @thebarnfell.bsky.social · 22/11/2023

If you'd be interested in attending the public portion of the defense to hear more, do reach out, there will be a remote option! In the meantime: many thanks to my committee, and everyone that's helped me along the way. (Yes, I am frantically writing my acknowledgments.)

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Jack Duff @thebarnfell.bsky.social · 22/11/2023

I (begin to) argue for a picture where comprehenders select a single candidate interpretation on varying timelines, depending on the utility/risk of a rapid decision, given the type of ambiguity and their task/goals. Thankfully, lots more work to do to develop this further!

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Jack Duff @thebarnfell.bsky.social · 22/11/2023

The dissertation—"On the timing of decisions about meaning during incremental comprehension"—is about, er, that. It aims to set up a wide view of interpretive garden paths, and when they do/don't show up, incl. with lexical ambiguity, scalar implicature, and causal inferences.

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Jack Duff @thebarnfell.bsky.social · 22/11/2023

∃x & news(x) & |x| = 2: (1) Next Friday, Dec 1, I'm defending my dissertation (!) (2) In Jan, I will be moving to Germany (!) to join Vera Demberg's lab at Saarland as a post-doc, studying (& modeling) individual differences in pragmatic processing Couldn't be more excited!

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