Richard Huskey

Leibniz, looking at the universe: "Why is there something instead of nothing?"

Me, looking at my Outlook calendar: same

Of all the analogies, this one about horses is the dumbest.

Recently, van der Stigchel and colleagues posted a provocative commentary suggesting that we should be wary of bots in online behavioral data collection (🧵by @cstrauch.bsky.social here: bsky.app/profile/cstr...). But should we? Here is my response letter osf.io/preprints/ps.... 1/5

Ha! The original Lancet article on the dangers of reading in bed is here: doi.org/10.1016/S014...

Very excited to see the first issue of @gpccomm.bsky.social is nearly ready! Some good work is cooking very soon!

Online Studies Psychological Science requires that authors who use samples from online data collection include a statement in the Method section explicitly addressing their approach to preventing and detecting automated or AI-generated responses. Rationale As large language models and other generative AI tools become more accessible, the risk of data contamination by non-human respondents has increased dramatically in research. Psychological science (and the social sciences generally) is particularly susceptible to this issue given its growing reliance on online data collection. Preventing automated responses during data collection and detecting them afterward often involve methodological trade-offs. For instance, technical barriers that aim to prevent LLM use (e.g., blocking copy-pasting functionalities) may eliminate behavioral indicators needed for detection (e.g., pasting rather than typing). This policy aims to enhance transparency and reproducibility of reported results by requiring authors to articulate their approach across both prevention and detection dimensions, enabling readers and reviewers to assess the likelihood of reported data being influenced by automated responses. Scope This policy applies to any submission with at least one study that includes data collected online without direct human supervision (e.g., via crowdsourcing platforms, student participants who complete the study online, online recruitment ads, or remote survey distribution tools). Required Reporting Authors must include in the Methods section either: A statement confirming that procedures were in place to prevent and/or detect and exclude automated or AI-generated responses, including a description of those procedures (e.g., explicit participant instructions against LLM use, disabled copy–paste functionality, CAPTCHA use, IP filtering, consistency checks, attention checks, adversarial prompting) as well as the types of automated responses that these procedures are suitable …

Maybe of interest: The submission guidelines of Psychological Science now demand an explicit statement on measures taken to reduce the risk of AI-generated responses for all online studies!

www.psychologicalscience.org/publications...

Deeply honored by this recognition from @psychscience.bsky.social, and to be in the company of scientists whose work I greatly admire! A big thank you to lab members, collaborators and mentors, past and present for the work we do together!

This one feels good 😊 Humbled and honored to be in the company of Spence Award winners, past and present. All credit goes to mentors, colleagues, and especially members of my lab -- thank you!!!

Comm Horizons @ UCD 2026: Communication in the Age of AI and Algorithms

Researching communication, AI, or algorithms? Join us at Comm Horizons 2026!

Keynotes from Jeff Hancock & @angelhwang.bsky.social, high-quality competitive programming, great feedback, & Napa wine tasting.

Abstract deadline approaching fast: March 1 (AOE)

communication.ucdavis.edu/horizonconf2...

Congratulations to the 2026 APS Spence Award recipients: Dorsa Amir, William Brady, Emily Finn, Daniel Yon, Yuan Chang Leong, Andrew Grotzinger.

Congratulations to the 2026 APS Spence Award Recipients! @dorsaamir.bsky.social, @williambrady.bsky.social, @esfinn.bsky.social, @andrewgrotzinger.bsky.social, @ycleong.bsky.social, @danieljamesyon.bsky.social,

www.psychologicalscience.org/members/awar...

Are you a junior faculty member interested in spending 2-4 weeks at Princeton Psych? Please apply for our Microsabbatical program! It’s a fully funded visit for professional development and creating long-term collaborations.
psych.princeton.edu/diversity/mi...

Know a promising undergrad who wants more time before applying to grad school? Pitt has a funded postbac program for students from underrepresented groups.

This year, my lab will consider applications for solo supervision or to be co-supervised by @mehrgol.bsky.social!

App deadline is March 15!

Feeling so grateful for being able to engage in such an exciting conversation in a room full of superstars! Thankful for my QE Committee, for my advisor, and for finally reaching this milestone! Next steps… even greater things! 🤩

I will be hiring a full-time pre-doctoral Research Professional to work with me at Chicago Booth.

Know someone interested in studying conversation and connection? Please help spread the word!

More details, including application instructions, are here: www.chicagobooth.edu/-/media/facu...

It’s unfortunate that the job is for someone pre-doctoral, or else I’d be interested in applying. This is a great opportunity to work with an amazing scientist and person, don’t pass it up!

A congratulatory poster from the UC Davis Department of Communication celebrating Rachael Kee for successfully defending her qualifying exam titled “Beyond the Laboratory: Introducing High-Throughput Communication Science.” The poster includes a summary of her project, which proposes integrating high-throughput, multimodal, and temporally sensitive data streams with Marr’s (1982) tri-level framework to better capture multilevel and reciprocal causal mechanisms in communication research. On the right side is a large blue illustration of the UC Davis water tower. At the bottom is a photo of five people standing in front of a screen displaying Rachael’s presentation slide. From left to right: Soojong Kim, Richard Huskey, Rachael Kee, Emorie Beck, and Drew Cingel. The UC Davis logo and “Department of Communication” appear along the bottom.

Today @rachaelkee.bsky.social successfully defended her QE! She submitted two papers: (1) a positioning statement introducing what she is calling “high-throughput communication science” and (2) a proposal for the first-ever demonstration of this research agenda.

Way to go, Rachael! 🎉🎉🎉

Thank you for sharing this! Is there a link to positions you are currently recruiting for?

11/11 If we want to understand relationships between media and mental health, we have to model the decision process itself.

Mental health shapes what we choose and how we choose it.

Preprint: doi.org/10.21203/rs....

Thanks to Valerie Klein, @gongxuanjun.bsky.social, and @aeden.bsky.social !!!

10/11 What does this mean for mood management and coping theories?

These frameworks assume people select media to regulate affect.

Our results suggest regulation is constrained by symptom severity.

At higher depression/anxiety, the decision process itself shifts, altering what feels preferable.

9/11 Most media research treats mental health as an outcome of media exposure.

We show it also functions as a moderator of media selection dynamics.

Media choice isn’t static preference. It’s a moment-by-moment decision process shaped by mental health.

8/11 These findings show that mental health explains how affective features are weighted during decision-making.

Depression and anxiety appear to dampen preference for highly arousing content whereas loneliness amplifies preference for negative valence.

This is driven by drift rate (v).

PHQ-9, depression: A negative interaction between depression and arousal on drift rate. As depression increases, preference for high-arousal movies decreases and reverses toward low-arousal content at moderate symptom levels. Other parameters (boundary, bias, non-decision time) show no credible effects.

GAD-7, anxiety: A similar negative interaction between anxiety and arousal on drift rate. Higher anxiety predicts reduced preference for high-arousal movies, with reversal at higher symptom levels. A small effect appears on decision bias for valence, but drift rate shows the primary effect.

UCLA-L, loneliness: A negative interaction between loneliness and valence on drift rate. As loneliness increases, preference for negatively valenced movies strengthens. No credible effects on other decision parameters.

7/11 As symptoms shift, preferences shift.

↑ Depression and anxiety: preferences shifts from high- to low-arousal movies.

↑ Loneliness: stronger preference for negative movies.

These effects emerge specifically for drift rate, but *not* caution (a) or bias (Z).

Four posterior probability distribution plots from a hierarchical Bayesian drift diffusion model. (A) Drift rate: negative valence shows a negative effect (preference for negative movies), and high arousal shows a positive effect (preference for high-arousal movies). Both effects are credibly different from zero. (B) Decision boundary: both valence and arousal differences reduce boundary separation, indicating less cautious decisions. (C) Decision bias: effects centered near zero, showing no credible bias shifts. (D) Non-decision time: small reductions when valence or arousal differ. Vertical dashed lines mark zero for reference.

6/11 First, we replicate our prior work (doi.org/10.1093/joc/...).

People show preferential evidence accumulation (v) for negatively valenced and high-arousal movies.

Differences in valence and arousal also reduce decision boundary. People are less cautious when options differ affectively.

Conceptual diagram of the drift diffusion model. Two horizontal lines represent upper and lower decision boundaries. A starting point (z) lies between them. Jagged lines show noisy evidence accumulating over time toward one boundary or the other, with slope representing drift rate (v). Reaction time distributions are shown at the top and bottom corresponding to choices at the upper and lower boundaries. Parameters labeled include drift rate (v), boundary (a), bias (z), and non-decision time (t).

5/11 We analyzed choices with the drift diffusion model (DDM).

It decomposes decisions into:
• Drift rate (v): preferential evidence accumulation
• Boundary (a): caution
• Bias (Z): starting preference
• Non-decision time (T): perceptual/motor time

We expected mental health moderates drift rate.

Illustration of the experimental task. A participant sits at a laptop while pairs of movie summaries appear sequentially on screen. Each trial presents two summaries side-by-side. Participants press one of two keys (Z or M) to select their preferred option. A dashed arrow labeled “Time” indicates the repeated sequence of trials across the task. In total, participants completed 196 two-choice decisions.

4/11 Does mental health shape media choice?

To test this, participants completed a two-choice decision task (196 trials), selecting movie summaries that systematically varied in both valence and arousal.

Four histograms labeled A–D. (A) Age distribution centered around 20 years, with most participants between 18–22. (B) PHQ-9 scores spread broadly from 0–25, with many students in the mild-to-moderate range. (C) GAD-7 scores similarly distributed from 0–20, with substantial representation in moderate ranges. (D) Loneliness (UCLA-L) scores spanning roughly 20–70, with many students reporting elevated loneliness. Overall, the sample shows meaningful variation and substantial levels of depression, anxiety, and loneliness.

3/11 Our sample (n = 313) shows similar patterns.

Students report substantial symptoms of depression (PHQ-9), anxiety (GAD-7), and loneliness (UCLA-L).

2/11 We start with college students; a group that over-indexes on mental health challenges.

In the U.S., 37% report moderate-to-severe depression, 33% anxiety, and 58% loneliness.

They’re also heavy media users.

Depressed? Anxious? Lonely?

What if mental health doesn’t just result from media use, but shapes how we choose media?

In a new preprint, Valerie Klein, @gongxuanjun.bsky.social @aeden.bsky.social and I and I test this using a computational decision-making model: doi.org/10.21203/rs....

🧵Thread

Helpful Tips for Trainees Contribute to cogcommscience-lab/lab-docs development by creating an account on GitHub.

Oh I love this exercise for your grads! Not sure if this is helpful, but these are the resources I share with my grads when they start at peer review github.com/cogcommscien...

Hybrid neural–cognitive models reveal how memory shapes human reward learning - Nature Human Behaviour Using artificial neural networks applied to human data, Eckstein et al. show that good models of reinforcement learning require memory components that track representations of the past.

This work by @mariaeckstein.bsky.social et al is a nice example of how progress in psychology can be expedited with machine learning.

How long before this type of approach is expected for models-of-behavior papers? My guess: not long. (If you are a trainee, nudge!)

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