I just collected more data on first- and second-order misperceptions and will post here at a later date when I post the results 5/5

because estimating (often obscure) quantities is challenging. So telling someone the correct answer will likely update an answer they subsequently give, but it likely won't affect existing attitudes (especially since the attitudes are likely not caused by the estimate to begin with). 4/n

Our 2025 PNAS paper (www.pnas.org/doi/10.1073/...) offers one explanation for this: anytime people estimate quantities (% of population that is foreign-born, % of Americans who own an Apple product, % of dots on a page that are blue), they make similar errors ... 3/n

Barnfield et al.'s paper looks similar: they find that providing correct information leads to more accurate estimates of thing people are estimating (here, second order beliefs), but not attitude-related outcomes ("policy feasibility perceptions, attitudes, and behavioural intentions"). 2/n

Exactly. This is also in line with a large body of work that shows correcting misperceptions (including numeric ones, see Hopkins et al. and Thorson's work on estimates related to immigration in the JOP and APSR, respectively) often results in updated estimates, but not attitudes. 1/n

Published a political psychology book this year?

@erincassese.bsky.social, @lchristensen.bsky.social, and I are on the Robert E. Lane Award committee for the best book in political psych published in the past year

Deadline for nominations: March 1, 2026. Details below

apsanet.org/membership/o...

UNC Political Science is hiring in methods!

UNC has an amazing department and the triangle is a great place to live.

Tenure Track Assistant Professor in Methods (Deadline Oct 24)

link to the posting👇
unc.peopleadmin.com/postings/307...

#polisky #psjobs #poliscijobs

Every time this worry comes up (www.ft.com/content/d419...) I post some Landy et al. (2018).

People just answer questions about proportions (of anything) in a rather particular way. So I think it's unlikely that what they are being asked about is as important as you might expect it should be.

@fguelzau.bsky.social Here's the ungated draft of the PNAS paper: www.brianguay.com/files/guay_2...

Zaller remains undefeated

Thanks @florianfoos.bsky.social !!

thanks so much Conrad, I'll fix this!

Thanks to my fantastic co-authors @tylermarghetis.bsky.social , @david-landy.bsky.social , Cara Wong and everyone who gave us feedback over many years

Ungated earlier version of the paper here: www.brianguay.com/files/guay_2...

Our findings suggest that the public knows more about politics than we give them credit for:

People make errors when estimating politically-relevant percentages, but this is due to the format of the question not underlying misinformation about what they are estimating

Of course, characteristics of specific groups may matter, but only at the margins. We should first account for the domain-general errors people make *anytime* they estimate a percentage, then examine group-specific explanations

The same is true of theories that people overestimate the size of groups that they have a lot of social contact with. Very little evidence of this!

We also test popular theories that people overestimate the size of groups they fear. Not the case.

Again, misestimates result mainly from the psychological errors we make anytime we estimate %s, not from anything specific to the group being estimated

We argue that this pattern of over-under estimation arises from 🧠Bayesian reasoning under uncertainty🧠: people often have uncertain ideas in their minds about the size of these groups, but when they convert these ideas to percentages they ‘hedge’ their estimates toward a prior

And this is the same pattern of errors people make when estimating things like the percentage of dots on a page that are red 👇

Here’s the key figure: people make the same estimation errors regardless of what they are estimating---political and *entirely non-political* quantities.

These are 100k estimates of the size of racial and non-racial groups made by 37k people in 22 countries

Instead, people are just really bad at estimating percentages

They systematically overestimate smaller %s and underestimate larger %s, including ENTIRELY NON-POLITICAL %s, such as the % of the population that owns an Apple product, has a passport, or has indoor plumbing

We argue that journalists and academics are *wrong* when they interpret these misperceptions as evidence that the public is ignorant and misinformed 👇

New paper on misperceptions out in PNAS @pnas.org

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

Why do people overestimate the size of politically relevant groups (immigrant, LGBTQ, Jewish) and quantities (% of budget spent on foreign aid, % of refugees that are criminals)?🧵👇

This is not a time for passive citizenship. Silence means approval. So what to do?

I'm very happy to share that I'll be joining the Department of Political Science at UNC Chapel Hill as an Assistant Professor this fall. I'm excited for this next chapter and will always be incredibly grateful for my amazing experience at Stony Brook.

I haven't been on bsky for almost a year. It's nice over here!

How to think about whether misinformation interventions work - Nature Human Behaviour Progress in the burgeoning field of misinformation research requires some degree of consensus about what constitutes an effective intervention to combat misinformation. We differentiate between resear...

Paper here: www.nature.com/articles/s41...
Ungated paper here: osf.io/preprints/ps...

But the MOST important thing is that researchers *justify* their research design & analysis approach on normative/theoretical grounds and *pre-register* it

Doing so will help prevent researchers from talking past each other and move toward tackling problem of misinfo

Key takeaway: choose the design that aligns with your normative claim about how people should interact with information

e.g., the normative claim that aligns with discernment is that people should maximize accuracy of the content that they believe and share

We demonstrate these differences empirically by re-analyzing data from recent misinformation studies

Different research designs and outcomes = different conclusions about whether misinformation interventions work