Ryan Yan Yan

We show that synesthesia is sensory and automatic in nature: the pupil scales with the brightness of experienced synesthetic colors. doi.org/10.7554/eLif...
Now in its new dress @elife.bsky.social (convincing & valuable in round 1).
If anyone wants to pick up the method, happy to share & explain!

Subcomponents of reward processing in adolescent anhedonia Nature Reviews Psychology - Understanding the link between anhedonia and reward processing in adolescence can enhance mental health interventions specifically for young people. In this Review, Ma...

Excited to share our new review on #adolescent #anhedonia and #reward processing, published in
@NatRevPsych
with my lab @XueqingMa @AngadSahni9
rdcu.be/e2ENh
bsky.app/profile/unir...

The gut-brain vagal axis governs mesolimbic dopamine dynamics and reward events Gut-brain vagal signals gate dopamine ensembles, reshaping reward and motivation beyond a brain-centric view.

"These findings challenge the traditional brain-centric view of reward processing, supporting a more integrated model in which vagus-mediated interoceptive signals intrinsically shape motivation and reinforcement."
Awesome work by @peppeganga.bsky.social & his team 👏
www.science.org/doi/10.1126/...

Our new paper is out in @natmed.nature.com 😱! A thread:

Can our thoughts and feelings directly affect our physical well-being? Our pre-registered, double-blind RCT investigated this by testing if modulating the brain's reward system could enhance immune responses to vaccination.

This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!

The Reward Positivity signals a goal prediction error The Reward Positivity (RewP) is an electroencephalogram (EEG) feature that emerges following performance feedback and is commonly understood to index both positive and negative reward-prediction error (RPE+ and RPE−, respectively) signals. In contrast to this dominant perspective, we argue that the RewP is an independent EEG feature that selectively responds to positive RPE and is superimposed on a common background signal. We further propose that the RewP signals a goal prediction error: it is elicited by abstract signals instead of by hedonic 'rewards'. This goal prediction error appears to be produced by a critic-like architecture that is associated with the actor–critic framework in reinforcement learning. This perspective emphasizes the role of the RewP in goal attainment and cognitive control as opposed to being a simple indicator of reward receipt.

Online Now: The Reward Positivity signals a goal prediction error

Subjective functions Where do objective functions come from? How do we select what goals to pursue? Human intelligence is adept at synthesizing new objective functions on the fly. How does this work, and can we endow arti...

Goal selection through the lens of subjective functions:
arxiv.org/abs/2512.15948
I welcome any feedback on these preliminary ideas.

Non-invasive ultrasonic neuromodulation of the human nucleus accumbens impacts reward sensitivity - Nature Communications This study shows that non-invasive ultrasound to the human nucleus accumbens can modulate deep brain activity and enhance reward-guided learning, offering a potential alternative to invasive neuromodu...

wowza - www.nature.com/articles/s41...

A habit and working memory model as an alternative account of human reward-based learning Nature Human Behaviour - In this study, Collins proposes an alternative dual-process (working memory and habit) model of reinforcement learning in humans.

My paper is out!
Computational modeling of error patterns during reward-based learning show evidence that habit learning (value free!) supplements working memory in 7 human data sets.
rdcu.be/eQjLN

Worth a read - context is everything. #neuroskyence #immunosky #medsky

Super happy to see this review out! We ask why people are so reluctant to abandon goals and how this commitment could be understood computationally. Work with Jill O'Reilly & @yaelniv.bsky.social

Really interesting work by Bakhurin and colleagues challenging the reward prediction error hypothesis of dopamine:
www.nature.com/articles/s41...
I love this figure which both echoes and undermines the famous figure from Schultz et al. (1997).

Add me please, thanks Uma!

Cognitive modeling of real-world behavior for understanding mental health A core strength of computational psychiatry is its focus on theory-driven research, in which cognitive processes are precisely quantified using computational models that formalize specific theoretical mechanisms. However, the data used in these studies often come from traditional laboratory-based cognitive tasks, which have unclear ecological validity. In this review we propose that the same theoretical frameworks and computational models can be applied to real-world data such as experience sampling, passive data, and digital-behavior data (e.g., online activity such as on social media). In turn, modeling real-world data can benefit from a theory-driven computational approach to move from purely predictive to explanatory power. We illustrate these points using emerging studies and discuss the challenges and opportunities of using real-world data in computational psychiatry.

Online Now: Cognitive modeling of real-world behavior for understanding mental health

The answer is yes! But the effect sizes were not huge. We argued that the utility of online RL tasks was not to replace EMA or clinical surveys, but to probe the mechanisms of affective variability.

Check out the preprint here! 🤩
osf.io/preprints/ps...

We used a Bayesian model to parse affective variability into:
⚡ noise (reactivity to recent rewards)
🌊 volatility (sustained responses to past rewards)
We asked whether affective noise and volatility in the RL task distinctly map onto their counterparts in EMA.

New preprint alert with @mikebrowning.bsky.social
and Chamith Halahakoon!
People have been using computer-based reinforcement learning tasks with affect probes. But to what extent do they reflect real-life affective experience? We tested this with 3 weeks of EMA + RL tasks in 339 participants.

‪@benhayden.bsky.social‬
@tyrellturing.bsky.social‬
@jmgrohneuro.bsky.social‬
@pessoabrain.bsky.social
I see a lot of talk on here about how we should avoid
"x does y" talk because the brain is "a dynamic, reverberant, reciprocally interconnected system".
But this does not follow.
A thread...

New preprint from the lab! 🧠
Led by Juliana Trach, w/ Sophia Ou

Using fMRI, we discovered evidence for time-sensitive reward prediction errors (RPEs) in the human cerebellum.

Builds on, and extends, recent work in both rodents and NHPs

Rate and noise in human amygdala drive increased exploration in aversive learning - Nature Human exploration is driven by two distinct neural mechanisms, a valence-independent rate signal and a valence-dependent global noise signal.

This is an exquisite demonstration of using intracranial recordings in humans to validate our findings that amygdala neurons encode the value of exploring in NHPs.

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

Ok, reinforcement learning fans: RL is great, but what do we do when there's no obvious reward from the environment? What about perfecting a golf swing or a foxtrot or a musical performance? We may have an answer. A tale of 🐦 🎶 + 🧠. 🧵1/ #bioacoustics #prattle 💬 #neuroai #compneuro

Multi-timescale reinforcement learning in the brain - Nature Individual dopaminergic neurons encode future rewards over distinct temporal horizons.

Two papers out today on RL in the dopaminergic neurons of the midbrain of mice (one from McGill's new PI @paulmasset.bsky.social). Both papers demonstrate heterogeneity in discount factors!

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

🧠📈 🧪 #NeuroAI

Behavioral, computational and self-reported measures of reward and punishment sensitivity as predictors of mental health characteristics - Nature Mental Health Reinforcement learning task-based behavioral and computational measures displayed low test–retest reliability at the individual level. Also in contrast to self-assessed personality measures, behaviora...

New in Nature MentalHealth! with Vrizzi, Najar, Lemogne, and @mael-lebreton.bsky.social

We tested whether behavioural and RL-based model parameters are test-retest reliable and predict mental health traits.

The result? Not really.

A cautionary tale for comp. psychiatry
doi.org/10.1038/s442...

Autonomic physiological coupling of the global fMRI signal - Nature Neuroscience The brain and body are necessarily connected. Here the authors show that brain blood flow and electrical activity are coupled with systemic physiological changes in the body.

Widespread cofluctuations in the low-frequency range between resting-state global fMRI signals, EEG activity, and a host of peripheral autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems 🧪🧠

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

email starting, "The U.S. National Science Foundation (NSF) has undertaken a review of its award portfolio. Each award was carefully and individually reviewed, and the agency has determined that termination of certain awards is necessary because they are not in alignment with current NSF priorites."

yesterday, my postdoc funding (salary and research funds) was cancelled by the National Science Foundation, effective immediately. I received the same generic, vaguely threatening, typo-ridden email as many of my colleagues who have had their awards terminated recently. (1/n)

Excited to see this in "print"! Work led by @jinke.bsky.social decoding emotional arousal across fMRI movie datasets!

Epic recordings of model-based and model-free learning signals from Kennerley lab (Bruno Miranda). Now at @elife.bsky.social

doi.org/10.7554/eLif...