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Reposted by Jérémie Beucler

Cognition @cognitionjournal.bsky.social · 7d

In our study, we investigated how people evaluate everyday socio-political arguments in the context of their prior beliefs about the topics being discussed.

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Reposted by Jérémie Beucler

Kobe Desender @kobedesender.bsky.social · 13d

Introducing hMFC: A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion! Now out in @plos.org Comp Bio.
led by Robin Vloeberghs with @anne-urai.bsky.social Scott Linderman

Paper: desenderlab.com/wp-content/u... Thread ↓↓↓

#PsychSciSky #Neuroscience #Neuroskyence

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Reposted by Jérémie Beucler

Kobe Desender @kobedesender.bsky.social · 19d

Common neural choice signals reflect accumulated evidence, not confidence! Now out in @cerebralcortex.bsky.social w @helenevanmarcke.bsky.social @pierreledenmat.bsky.social @yfvisser.bsky.social @denizerdil.bsky.social a.o.

Paper: desenderlab.com/wp-content/u... Thread ↓↓↓

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Reposted by Jérémie Beucler

Jamie Cummins @jamiecummins.bsky.social · 20d

Can large language models stand in for human participants?
Many social scientists seem to think so, and are already using "silicon samples" in research.

One problem: depending on the analytic decisions made, you can basically get these samples to show any effect you want.

THREAD 🧵

The threat of analytic flexibility in using large language models to simulate human data: A call to attention

Social scientists are now using large language models to create "silicon samples" - synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. How...

arxiv.org

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Reposted by Jérémie Beucler

Kyle Fiore Law, PhD @kyleflaw.com · Aug 27

🪦 New in @pnas.org: we analyzed 38 million U.S. obituaries to ask what signals a life well lived:

What values are people most remembered for?

How do legacies shift with cultural events?

How do age and gender shape what it means to have lived well?

www.pnas.org/doi/10.1073/...

An exploration of basic human values in 38 million obituaries over 30 years | PNAS

How societies remember the dead can reveal what people value in life. We analyzed 38 million obituaries from the United States to examine how perso...

www.pnas.org

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Reposted by Jérémie Beucler

Nicolas Beauvais @nicolasbeauvais.bsky.social · Aug 21

Happy to share that my first paper is out in Thinking & Reasoning! 📄📢
With Aikaterini Voudouri, @boissinesther.bsky.social & @wimdeneys.bsky.social we show that deliberate reasoning helps not just to correct but also to justify intuitive judgments.

🔗Full paper: shorturl.at/JTeTi
Quick thread below!

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Reposted by Jérémie Beucler

Edgar Dubourg @edgardubourg.bsky.social · Aug 12

Today’s popular fictions can be extremely far from reality: The Lord of the Rings, Avatar, The Legend of Zelda, Avengers: Endgame. But has this always been the case?

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 11

🙌 Lucky to work with the brilliant
@zoepurcell.bsky.social,
@luciecharlesneuro.bsky.social,
@kobedesender.bsky.social and
@wimdeneys.bsky.social

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 11

Curious about the mechanisms behind biased reasoning and metacognition? 🤔

📍 Come see our poster at #CCN2025, Aug 12, 1:30–4:30pm

We show how a biased drift-diffusion model can explain choice, RT and confidence in a base-rate neglect task, revealing why more deliberation doesn’t always fix bias.

poster about why we don't stop and think, showing the main results of the study

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Reposted by Jérémie Beucler

Jean-François Bonnefon @jfbonnefon.bsky.social · Aug 6

We need data, not guesses, on how future tech may reshape behavior & society. Our new paper with @azimshariff.bsky.social and @iyadrahwan.bsky.social out in @nature.com spells out a framework we call the ❝science fiction science method❞ (sci-fi-sci) +

www.nature.com/articles/s41...

The science fiction science method - Nature

The ‘science fiction science’ method simulates future technologies and collects quantitative data on the attitudes and behaviours of participants in various future scenarios, with the aim of predictin...

www.nature.com

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

8/8

Huge thanks to my co-authors Aikaterini Voudouri & @wimdeneys.bsky.social, and our lab, @lapsyde.bsky.social .

Read the full paper here: osf.io/preprints/ps...

#Reasoning #SemanticIllusion #MosesIllusion #DualProcess #Metacognition

OSF

osf.io

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

7/8

Bottom line: Responding to semantic illusions isn’t just about slow, deliberate correction. It’s a dynamic interplay between competing intuitions: the correct "It's Noah!" and the incorrect "Two animals!" This calls for a revised dual-process account.

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

6/8

Finding #3: The strength of the illusion is key! As the semantic overlap gets stronger (e.g., "Moses" is closer to "Noah" than "Goliath" is), confidence in incorrect answers tended to increase, while confidence in correct answers tended to decrease. 📈📉

Figure shows confidence results as a function of illusion strength.

Figure 5. Regression results of initial confidence as a function of illusion strength for control no-anomaly correct (baseline), anomaly correct and anomaly incorrect responses. Illusion strength = mean initial no-anomaly accuracy − mean initial anomaly accuracy for each item. The shaded bands are 95% confidence bands.

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

5/8

Finding #2: Even when participants got it wrong and fell for the illusion, they showed a significant error sensitivity (lower confidence). Interestingly, this effect was not affected by load and deadline, suggesting this error sensitivity is intuitive.

Figure showing confidence results.

Figure 3. Confidence ratings at anomaly and control no-anomaly trials in the initial response stage as a function of accuracy in Experiment 1 and Experiment 2. The lower and upper hinges of the boxplot correspond to the first and third quartiles, and the middle line shows the median. The lower (resp. upper) whiskers extend from the hinges to the smallest (resp. largest) value no further than 1.5 times the interquartile range. Overlaid black dots represent the mean and error bars are standard errors of the mean.

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

4/8

Finding #1: You don't always need to be slow to be right! 🐢 A significant number of participants intuitively spotted the anomaly from the start, without needing extra time and resources to deliberate. 🐇 Sound intuitive reasoning does happen.

figure on accuracy

Figure 2. Accuracy and Direction of Change in Experiment 1 and Experiment 2. a) Response
accuracy at anomaly and control no-anomaly trials as a function of response stage. b) Proportion
of each direction of change category at anomaly and control no-anomaly trials; “00” = incorrect
initial and incorrect final response; “01” = incorrect initial and correct final response; “10” =
correct initial and incorrect final response; “11” = correct initial and correct final response. The
lower and upper hinges of the boxplot correspond to the first and third quartiles, and the middle
line shows the median. The lower (resp. upper) whiskers extend from the hinges to the smallest
(resp. largest) value no further than 1.5 times the interquartile range. Overlaid black dots represent
the mean and black error bars are standard errors of the mean.

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

3/8

To test this, we ran 4 experiments with over 500 participants! We used a two-response paradigm: first, a quick intuitive answer under time pressure & cognitive load. Then, a final, deliberated response with no constraints. Here are the main results:

Shows a figure describing the paradigm and two cognitive load matrices

Figure 1. Experiment 1 trial sequence and examples of load patterns in Experiment 1-3. a) Example of one trial in Experiment 1. Participants had to respond to a trivia question twice, once with a deadline and a concurrent load and a second time without any constraint. b) Example of the to-be-memorized load patterns in Experiment 1-3 (upper panel) and Experiment 2 (lower panel).

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

2/8

These semantic illusions are often used to test for deliberate "System 2" thinking (e.g., in the verbal Cognitive Reflection Test). The classic theory? We intuitively fall for the illusion & need slow, effortful deliberation to correct the mistake. But is it really that simple?

Shows this title:

Measuring cognitive reflection without maths: Development
and validation of the verbal cognitive reflection test

Miroslav Sirota1 | Chris Dewberry2 | Marie Juanchich1 | Lenka Valuš3 |
Amanda C. Marshall

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Jérémie Beucler @jeremiebeucler.bsky.social · Aug 6

1/8

New (and first) paper accepted at JEP:LMC 🎉

Ever fallen for this type of questions: "How many animals of each kind did Moses take on the Ark?" Most say "Two," forgetting it was Noah, and not Moses, who took the animals on the Ark. But what’s really going on here? 🧵

When asked “How many animals of each kind did Moses take on the Ark?”, most people answer “Two”, failing to notice that it was Noah, and not Moses, who took the animals in the Ark. “Fast-and-slow” dual process accounts of such semantic illusions posit that incorrect responders are not sensitive to their error and that overcoming the illusion requires deliberate correction of an intuitive erroneous answer. We present three experiments that force us to revise this dual process view. We used a two-response paradigm in which participants had to give their first, initial answer under cognitive load and time pressure. Next, participants could take all the time they wanted to deliberate and select a final answer. This enabled us to identify the intuitively generated response that preceded the final response given after deliberation. Results show that participants do not necessarily need to deliberate to avoid the illusion and that incorrect respondents consistently display error sensitivity (as reflected in decreased confidence), even when deliberation is minimized. Both reasoning performance and error sensitivity in the initial, intuitive stage tended to be driven by the semantic relatedness between the anomalous word (e.g., “Moses”) and the undistorted word (e.g., “Noah”). We show how this leads to a revised model where the response to semantic illusions depends on the interplay of both incorrect and correct intuitions.

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Reposted by Jérémie Beucler

Cédric Cortial @ccortial.bsky.social · Jul 15

Excited to share my latest research !

Key findings reveal that a negative emotion triggered reasoning.

#epistemology #Psychology #Cognition #Research #Science #Neuroscience #Reasoning #CognitiveScience #AcademicTwitter

📖 Read the full study: www.tandfonline.com/doi/full/10....

www.tandfonline.com

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Reposted by Jérémie Beucler

Zoe Purcell 🕵🏻‍♀️ @zoepurcell.bsky.social · Jun 11

Do AI builders hold different values from AI users?

We show that AI builders and men are more utilitarian and less supportive of pro-diversity outputs, highlighting ongoing concerns about workforce diversity and whose values are shaping AI.

tinyurl.com/AIcognit

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Reposted by Jérémie Beucler

Laura Charbit @lauracharbit.bsky.social · Jul 7

👀 Just out in Thinking & Reasoning with @boissinesther.bsky.social @mts-raoelison.bsky.social @wimdeneys.bsky.social

Curious how intuitive reasoning develops through adolescence?

🔗 www.tandfonline.com/eprint/KFH5K...

Quick summary👇

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Reposted by Jérémie Beucler

David Rand @dgrand.bsky.social · Jun 17

🚨In PNAS🚨
The right often accuses fact-checkers of political bias
But we analyzed Community Notes on Musk's X and found posts flagged as "misleading" are 2.3x more likely to be written by Reps than Dems!
The issue is Reps sharing misinformation, not fact-checker bias...
www.pnas.org/doi/10.1073/...

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Reposted by Jérémie Beucler

Paul Bogdan @pbogdan.bsky.social · Apr 9

🚨New paper!🚨

Meta-analysis on 4M p-values across 240k psych articles: How has psychology changed since the replication crisis began? How is replicability linked to citations, impact factor, and university prestige? 🧵

Paper: journals.sagepub.com/doi/10.1177/...

Interactive: pbogdan.com/meganal

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Reposted by Jérémie Beucler

Josef Fruehwald @jofrhwld.bsky.social · Nov 18

this is roughly my gutfeel for interpreting logit coefficients. #stats #rstats

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Reposted by Jérémie Beucler

Stephan Lewandowsky @lewan.bsky.social · Apr 10

Honest people don’t lie. Or do they? Liars aren’t honest. Or are they?
One puzzling conundrum in contemporary politics is that politicians who seem to be estranged from facts and evidence are nonetheless considered honest by their followers.
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