@p-backup.bsky.social
Reposted
Fourth, some strange ideas on theory, which would if taken to their logical conclusion result in the end of being able to work theoretically, something already under threat (section 6 doi.org/10.31234/osf... also see table 2). Declaring a conflict of interest has almost become meaningless.
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October 4, 2025 at 6:24 AM
Fourth, some strange ideas on theory, which would if taken to their logical conclusion result in the end of being able to work theoretically, something already under threat (section 6 doi.org/10.31234/osf... also see table 2). Declaring a conflict of interest has almost become meaningless.
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Reposted
Third, the peculiar idea that somehow we don't need to read, write, or perform literature reviews anymore; popping up like a satanic mushroom in almost all so-called OK uses of LLMs.
Companies writing our papers via their chatbots is not scientific at all. See section 5: doi.org/10.31234/osf...
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Companies writing our papers via their chatbots is not scientific at all. See section 5: doi.org/10.31234/osf...
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![5 Ghostwriter in the Machine
A unique selling point of these systems is conversing and writing in a human-like way. This is imminently understandable, although wrong-headed, when one realises these are systems that
essentially function as lossy2
content-addressable memory: when
input is given, the output generated by the model is text that
stochastically matches the input text. The reason text at the output looks novel is because by design the AI product performs
an automated version of what is known as mosaic or patchwork
plagiarism (Baždarić, 2013) — due to the nature of input masking and next token prediction, the output essentially uses similar words in similar orders to what it has been exposed to. This
makes the automated flagging of plagiarism unlikely, which is
also true when students or colleagues perform this type of copypaste and then thesaurus trick, and true when so-called AI plagiarism detectors falsely claim to detect AI-produced text (Edwards, 2023a). This aspect of LLM-based AI products can be
seen as an automation of plagiarism and especially of the research paper mill (Guest, 2025; Guest, Suarez, et al., 2025; van
Rooij, 2022): the “churn[ing] out [of] fake or poor-quality journal papers” (Sanderson, 2024; Committee on Publication Ethics,](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:vsyj5jtayxcq5dw5mtja4xtb/bafkreifmac3sz5upxcmvhzbqfb3p2yt6cy7tkshv3ere7ttffsm6qmmboq@jpeg)
October 4, 2025 at 6:16 AM
Third, the peculiar idea that somehow we don't need to read, write, or perform literature reviews anymore; popping up like a satanic mushroom in almost all so-called OK uses of LLMs.
Companies writing our papers via their chatbots is not scientific at all. See section 5: doi.org/10.31234/osf...
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Companies writing our papers via their chatbots is not scientific at all. See section 5: doi.org/10.31234/osf...
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Reposted
Second, if we uncritically adopt AI in psychology, we outsource programming of our experiments to companies. This undoes a lot of open source: experiments are under industry capture, written without formal specifications. It deskills us: psychologists spent the last decade learning to code.
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![4 Outsourcing Programming to Companies
[Sanford’s mechanical vernier chronoscope] had
two pendulums, one set longer than the other but
both set at known lengths. When the stimulus
appeared, the longer pendulum was released mechanically. When the participant pressed the key in
response, the shorter pendulum was similarly released. By counting the number of swings it took
the shorter pendulum to catch up with the longer,
experimenters could use a mathematical formula
to calculate the difference in time between the two
events. (Evans, 2000, p. 322–323)
A century later, all this is now done by programming computers to display stimuli, and collect and analyse data. The largely
harmless and perhaps even beneficial (cf. Guest, 2025) transition
to computational experimentation, where all instruments (e.g.
EEG, fMRI, eye-tracking) are hooked up to the codebase for running experiments and the database for storing participant data,
has been completed through: the adoption of programmes such
as SPSS for statistics (Landau & Everitt, 2003) and E-Prime for
presenting stimuli (Spapé et al., 2019); and training psychologists
to use programming languages (e.g. R, Python; British Psychological Society, 2017; Peirce et al., 2022). The last part, from the
mid 2010s onwards, where psychologists spent time and effort
learning how to program is a massively laudable case of reskilling
(N. D. Anderson, 2016; Guest & Forbes, 2024; Scherer et al.,
2019)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:vsyj5jtayxcq5dw5mtja4xtb/bafkreib4ovbeiidibx2mjxl4hgju2sgosrfsq5d4awmi6u2d76wr3phbkm@jpeg)
![As Danielle Navarro (2015) says about shortcuts through using inappropriate technology, which chatbots are, we end up digging ourselves into “a very deep hole.” She goes on to explain:
The business model here is to suck you in during
your student days, and then leave you dependent on
their tools when you go out into the real world. [...]
And you can avoid it: if you make use of packages
like R that are open source and free, you never get
trapped having to pay exorbitant licensing fees. (pp.
37–38)
These were and are the reasons to switch from e.g. SPSS, which is
owned by IBM, to R or Python (Wills, 2019). The same and more
hold for being locked in and addicted to AI products, which have
closed or industry-controlled source code (Hao, 2025; Liesenfeld & Dingemanse, 2024; Liesenfeld et al., 2023; Maffulli, 2023;
Maris, 2025; Mirowski, 2023; Solaiman, 2023) , and mislead us
into overestimating our programming abilities (Lehmann et al.,
2025)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:vsyj5jtayxcq5dw5mtja4xtb/bafkreihurkp2kmiy4ao2thwn3aor6zbvfh6vs2q6kcos5gqagtp3vivjo4@jpeg)
October 4, 2025 at 6:07 AM
Second, if we uncritically adopt AI in psychology, we outsource programming of our experiments to companies. This undoes a lot of open source: experiments are under industry capture, written without formal specifications. It deskills us: psychologists spent the last decade learning to code.
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Reposted
Without Critical AI Literacy (CAIL) in psychology (doi.org/10.31234/osf...) we risk the following:
1️⃣ misunderstanding statistical models, thinking correlation is causation;
2️⃣ confusing statistical models and cognitive models, undermining theory;
3️⃣ going against stated open science norms.
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1️⃣ misunderstanding statistical models, thinking correlation is causation;
2️⃣ confusing statistical models and cognitive models, undermining theory;
3️⃣ going against stated open science norms.
4/
October 4, 2025 at 5:51 AM
Without Critical AI Literacy (CAIL) in psychology (doi.org/10.31234/osf...) we risk the following:
1️⃣ misunderstanding statistical models, thinking correlation is causation;
2️⃣ confusing statistical models and cognitive models, undermining theory;
3️⃣ going against stated open science norms.
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1️⃣ misunderstanding statistical models, thinking correlation is causation;
2️⃣ confusing statistical models and cognitive models, undermining theory;
3️⃣ going against stated open science norms.
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