Jon Freeman

Fig. 1. a. Visual and auditory regions of interest (ROIs). b. Responses in a combination of visual (e.g., early dorsal visual stream; Fig. 1a, middle panel) and auditory regions were used to predict responses in the rest of the brain using MVPN. c. In order to identify brain regions that combine responses from auditory and visual regions, we identified voxels where predictions generated using the combined patterns from auditory regions and one set of visual regions jointly (as shown in Fig. 1b) are significantly more accurate than predictions generated using only auditory regions or only that set of visual regions.

I’m excited to share my 1st first-authored paper, “Distinct portions of superior temporal sulcus combine auditory representations with different visual streams” (with @mtfang.bsky.social and @steanze.bsky.social ), now out in The Journal of Neuroscience!
www.jneurosci.org/content/earl...

Refusing to release the requested data on how these questions performed undermines the Census Bureau’s scientific integrity and prevents accountability when LGBTQ+ communities are left invisible. (4/4)

Congress allocated $10 million, the Census Bureau tested nearly half a million households, and now the results are being unlawfully suppressed. (3/4)

Policymakers, researchers, and multiple federal agencies requested this testing to help enforce civil rights and better understand and address disparities in health, education, employment, and other areas affecting LGBTQ+ Americans. (2/4)

Democracy Forward & I filed a lawsuit against the U.S. Census Bureau, seeking the release of data from its testing of sexual orientation & gender identity questions in the nation's most important annual demographic survey, key to upholding LGBTQ+ rights.

democracyforward.org/updates/sogi...

(1/4)

The results open a path for new interventions that don’t just target stereotypes but also attempt to recalibrate biased visual perception directly, with the hopes of mitigating such high-stakes misjudgments under stress and uncertainty.

(6/6)

While past work has generally assumed such weapon-identification biases involve an accurate perception of the object but then a racially biased impulse that is difficult to control, our findings suggest that part of the problem is a temporary visual distortion as well.

(5/6)

These neural representational shifts predicted subjects' delays in recognizing these tools as tools, rather than weapons, suggesting an initial tendency to perceive them as weapons.

(4/6)

Using neural decoding techniques, we find that when subjects saw a Black man’s face before an image of a tool, their brain’s object-processing regions shifted toward a weapon-like representation.

(3/6)

Unarmed Black people in the US are 3X more likely than unarmed White people to be shot and killed by police. In many tragic cases, unarmed Black men were holding innocuous objects like a wrench, wallet, or cell phone when fatally shot by an officer.

(2/6)

New findings from my lab in Nature Communications suggest that racial stereotypes can lead the brain's perceptual system to temporarily "see" weapons where they don't exist.

Led by: @dongwonoh.bsky.social

Paper: www.nature.com/articles/s41...

(1/6)

Three @spspnews.bsky.social members will discuss their grant terminations and next steps, followed by Q&A, story sharing, and community discussion.

Moderator: Cynthia L. Pickett, Professor; Past SPSP President
Speakers: @lkfazio.bsky.social, @freemanjb.bsky.social, and @frankikung.bsky.social

We’re Hiring: Technology Policy Fellow – FABBS Location: Washington, D.C. (hybrid) Salary: Approximately $7,000 per month Start Date: September 15, 2025 Duration: 3 months, with potential for longer Position

The Federation of Associations in Behavioral and Brain Sciences (@fabbs.org) is searching for a short-term technology policy fellow. Great opportunity for a new PhD interested in policy, maybe before a research postdoc or faculty position:
fabbs.org/news/2025/08...

First paper from my lab out @commspsychol.nature.com

www.nature.com/articles/s44271-025-00275-w

Latent factor models are popular for mental representation of people, e.g. warmth & competence

But we show in naturalistic contexts, more complex representations are needed: high-dimensional networks

Congrats!

Paper in @pnas.org in which @d-melnikoff.bsky.social and I provide evidence for model-based effects on automatic evaluation. This was a super fun “adversarial” collaboration with 0 adversariality. It may have been nice to be right, but getting it right is nearly as nice: www.pnas.org/doi/10.1073/...

Curious for any reactions/feedback!

We think it's important to view impressions not so much as drawing on a fixed low-dimensional structure but as emerging in a combinatorial fashion out of the dynamics of a high-dimensional space. This approach may also be valuable for thinking about other dimensional models in social cognition (8/8)

The model can explain growing findings:
▪️cross-cultural & individual perceiver variation
▪️variation by targets' race/gender/groups

And makes novel predictions:
▪️"proximal" vs. "distal" traits in cascades (competent → intelligent → creative)
▪️earlier activation of putatively latent dimensions (7/8)

In the model, the structure of trait relationships (e.g., trustworthiness–dominance) can change due to targets or context and cultural and individual learning. Top-down factors—like goals, stereotypes, or attention—reshape the attractor landscape, influencing which traits become most stable. (6/8)

Here the trustworthiness/warmth dimension isn’t a latent mechanism or have a privileged functional/cognitive status—it’s an emergent pattern from correlated traits. That’s why it appears in PCA or factor analysis. But we argue that it’s only a mere snapshot of a fluid, high-dimensional space (5/8)

How does it work? You encounter another person. Features trigger many trait concepts (e.g., sociable, caring, competent), which activate each other or compete, influenced by top-down goals & higher-order processes. The network settles into a stable neural pattern, resulting in impressions. (4/8)

Instead, using attractor neural networks, we propose a high-dimensional model. In the brain, social impressions would operate as dynamic trajectories in a neural-state space that can be shaped by sensory cues, conceptual associations, and higher-order social cognition.
(3/8)

How do we infer countless traits? Models have treated trait perception like color vision: impressions arise from combinations of, e.g., “red” (trustworthy), “green” (dominant), & "blue" (youthful). But unlike color, there’s no evidence for this, and we question the value of latent dimensions (2/8)

A high-dimensional model of social impressions People form social impressions from visual cues such as faces, which are argued by various models to arise from some limited set of fixed dimensions (e.g., trustworthiness and dominance). We argue tha...

In a TiCS paper, @chujunlin.bsky.social & I propose a high-dimensional model of social impressions.

Existing models focus on 2–4 latent dimensions (e.g. trustworthy/warm), but they often fall apart across different contexts, cultures, & perceivers. We need a paradigm shift.

shorturl.at/7GD1n (1/8)

Want your NIH and NSF program officers and division directors to be fired and turned into political appointees?

Deadline extended: 3 days left! Already 33,000+ public comments

Comments can tank a proposed rule in court.

📣 Oppose the rule with a brief comment: shorturl.at/WKuBj

So thrilled @youngkihong.bsky.social will be starting up at UC Boulder!

Postdoctoral Researcher in social/cognitive neuroscience with focus on social learning Do you want to contribute to top quality medical research? The Mechanisms of Social Behavior lab at Karolinska Institutet (PI: Björn Lindström) is seeking a highly qualified postdoctoral researcher.

Join our @erc.europa.eu funded lab @ki.se! We're hiring a postdoc to explore how humans learn from each other — with fMRI, modeling, & behavioral experiments. 🧠👫
A chance to lead cutting-edge work in social neuro & computational psychology.
Apply: shorturl.at/GJQuW

#ScienceNotPolitics #ProtectScience #StandUpForScience #NSF #NIH

It's currently at 19,430+ public comments, but the deadline is now extended to June 7.

The agency may be worried the large, well-reasoned opposition could tank the final rule in court—and is buying time to flood the docket with support.

Keep it coming!