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Tyler Wilcox

@ktylerwilcox.bsky.social

14 followers 26 following 3 posts

Statistical Consultant @cornellcscu.bsky.social

Posts Replies Media

Reposted by Tyler Wilcox

404 Media @404media.co · 27/08/2026

“The academic record is being quietly haunted” by researchers with names like Elena Vasquez and Marcus Chen.

404media.co

The AI ‘Ghosts’ Contaminating Academic Publishing

“The academic record is being quietly haunted” by researchers with names like Elena Vasquez and Marcus Chen.

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Tyler Wilcox @ktylerwilcox.bsky.social · 24/08/2026

I’ll be teaching a three-part intro to R for data analysis, data visualization, and statistics with my @cornellcscu.bsky.social colleagues in September. Free and in person to everyone @cornelluniversity.bsky.social 😄📊👩‍💻 #Cornell

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Tyler Wilcox @ktylerwilcox.bsky.social · 06/08/2026

Happy to share a new meta-analysis I worked on with Samhita Korukonda :)

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Reposted by Tyler Wilcox

Iris van Rooij 💭 @irisvanrooij.bsky.social · 16/06/2026

Published version now available in Current Directions in Psychological Science: journals.sagepub.com/doi/10.1177/...

journals.sagepub.com

Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong? - Iris van Rooij, Olivia Guest, 2026

The current AI hype cycle combined with psychology’s various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical fou...

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Tyler Wilcox @ktylerwilcox.bsky.social · 16/06/2026

Congratulations @urielmenalled.bsky.social !

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Reposted by Tyler Wilcox

Olivia Guest · Ολίβια Γκεστ @olivia.science · 11/06/2026

I wrote a very short piece on models & modelling in part because we need to protect our scientific praxis damage caused by AI in terms of addictive frictionless thinking, esp since it harms society broadly too. "Models" doi.org/10.5281/zeno... Thanks @carvalhais.org for inviting me to write this.

Models in cognitive and related sciences are part of an overarching methodology, computational
or broadly formal modelling, that aims to test, refine, and showcase our understandings of
brain, cognition, and behaviour.1 In their foundational book Models as Mediators, Mary S.
Morgan and Margaret Morrison (1999)2 make the case that models play a mediatory role in
science between what we observe and what we want to build to house our understandings,
theories. Under this conception of models, known as the pragmatic view,3 we can carve out
not only their unique role, but also why and how they must be protected from forces that
do not prize understanding. Taking a broad perspective that sees all human activity that
strives towards understanding as scientific or as under our purview as modellers, allows for
this characterisation of models as mediators to be more generally fruitful.4 In the context of
human-centred design, for example, our theories may be anything from our understanding of
users’ behaviours in a specific context to our understanding of what a user expects from a
product or system a priori.
Importantly, scientific modelling is apart from other scientific praxis, such as concocting
a verbal or formal theory or designing and running experiments.5 Models, as described by
Morrison and Morgan,6 have the following characteristics:
a. construction — models are neither directly derived from theory nor from data, but
through an effortful friction between the modeller and the medium (e.g. their favourite
programming language, mathematics, natural language) through which they are building
their model;7

In the contemporary entanglements with industry that scholarly fields find themselves —
such as with the imposition of gargantuan statistical models, which consume breathtaking
amounts of energy, and which require the building of evermore data-centres to house the glut of
data unavoidably required for their so-called human-like abilities9 — the idea of using models
in such slow10 ways is seemingly out the window. What is worthy of perseverating on in our
specific context is not so much that so-called artificial intelligence (AI) models may harm our
individual practice or displace our labour. They certainly may.11 However, the threat from AI
worthy of highlighting here is that a culture of frictionless practice, which such models (in
name only) promote, is not only harmful to people’s mental health and skils, but also directly
undermines scholarly praxis.12
Avoiding the effortful and difficult process of building, testing, and throwing away models
— be they written in well-crafted code, be they not-yet-fully-formal models, or be they mock-ups
of potential final products — with the goal of deeply understanding our theories about the world
is anti-scientific and anti-scholarly. This spills over into an anti-intellectual stance for a designer
or user of models. Such avoidance is normalised by the addictive convenience culture facilitated
and entrenched by contemporary AI. A culture in which so-called prompting a machine for a
seemingly polished output requires none of the above characteristics of modelling as a process.
Furthermore, presenting anti- or non-scientific modelling endeavours as something other than
what they are, may even veer into pseudo-science and pseudo-technology.13 To add insult
to injury, if junior modellers are not trained to perform this praxis, the end of skills being
inherited by future generations is nigh.
To subvert this, we must preserve the special status of crafting models. I propose this
can be done in a two-pronged manoeuvre: a) rejection …

quantitative predictions of the tides, which have been being refined over the millennia,24 to the
qualitative shift in explanation produced by carving out the concept of gravity. Quantitative
predictions did not pile up to produce our contemporary understanding of gravity.
Protecting modelling praxis may be difficult, but needs must when the devil drives. The
alternative is stifling our knowledge about (our understandings of) the world. And so to
avoid making theorising and modelling seem superfluous, we must remember that scientific
models do not operate like inferential statistical models wherein prediction is the goal. As
expounded on above, models are the instrument through which we perform fully-fledged
scientific reasoning on our theories, and which mediate between theory, formal or otherwise,
connecting it to observations, data, and phenomena, and back again. A model (in name only)
that is built through applying statistics over a dataset, which all contemporary artificial neural
network-based AI models are, will neither function as mediator between data and theory —
it has been contaminated — nor provide qualitative predictions.25 Nor will it serve any user
who seeks to gain insight and skills, nor any designer of systems who wishes for a society
comprising such fellow humans.
Ultimately, “all science would be superfluous if the outward appearance and the essence of
things directly coincided.”26 If we merge essences and appearances in our reasoning, we risk
our understandings of the world. All modelling, all metaphorical, theoretical, and mediatory
thinking, enrich us and our societies and make possible not only valuable understandings of
our own humanity, but constitute our humanity as such

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Reposted by Tyler Wilcox

Olivia Guest · Ολίβια Γκεστ @olivia.science · 04/10/2025

New preprint 🌟 Psychology is core to cognitive science, and so it is vital we preserve it from harmful frames. @irisvanrooij.bsky.social & I use our psych and computer science expertise to analyse and craft: Critical Artificial Intelligence Literacy for Psychologists. doi.org/10.31234/osf... 🧵 1/

Cover page of Guest, O., & van Rooij, I. (2025, October 4). Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1

Table 1 Guest, O., & van Rooij, I. (2025, October 4). Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1

Table 2 Guest, O., & van Rooij, I. (2025, October 4). Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1

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Reposted by Tyler Wilcox

Cornell Statistical Consulting Unit @cornellcscu.bsky.social · 18/05/2026

We will be holding drop-in consulting on the 3rd floor of Savage Hall this summer. Come visit us with your questions! cscu.cornell.edu @cornellgrad.bsky.social @research-and-innovation.cornell.edu

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Reposted by Tyler Wilcox

Cornell Statistical Consulting Unit @cornellcscu.bsky.social · 23/01/2025

CSCU Assisted Pub: Iodinated contrast dye-diluent combination exhibits longer time to full dye saturation compared to lidocaine, bupivacaine, and water in porcine cadaveric nervous tissue pubmed.ncbi.nlm.nih.gov/39631168/ @cornellvet.bsky.social @statsysteve.bsky.social

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Reposted by Tyler Wilcox

Cornell Statistical Consulting Unit @cornellcscu.bsky.social · 16/01/2025

Our spring semester workshop schedule is up! Workshops are open and free for all @cornelluniversity.bsky.social. To learn more and register, visit cscu.cornell.edu/workshops/cu... @cornellgrad.bsky.social @cornellrsrch.bsky.social

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Reposted by Tyler Wilcox

Cornell Statistical Consulting Unit @cornellcscu.bsky.social · 22/01/2025

Applications are open for the WHO/Cochrane/Cornell University Joan Klein Jacobs Center for Precision Nutrition and Health Summer Institute for Systematic Reviews in Nutrition for Global Policy Making 2025 @cornelluniversity.bsky.social Ithaca. @cpnh.bsky.social academicjobsonline.org/ajo/jobs/29504

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