Ryan Smith (@rssmith.bsky.social)

Huge congrats to Claire Lavalley on this new paper. nature.com/articles/s4139… Results show that methamphetamine users show a specific deficit in model-based planning, in which they fail to simulate plans with large short-term losses (elevated aversive pruning). Brief thread below!

Computational modeling revealed that individuals with methamphetamine use disorder show reduced information-seeking and slower belief updating when trying to maximize long-term reward. @CarterMGoldman @toru1789taka @RyanSmith_LIBR nature.com/articles/s4427…

Big thanks to co-authors as well! @NRDlab @SamuelTaylorCS @KhalsaLab @mpwpaulus (and others without Twitter)

Congratulations to @toru1789taka and @CarterMGoldman on their newly published paper! This paper shows that individuals with methamphetamine use disorder show reduced directed exploration and slower dynamic learning rates in both high/low anxiety states nature.com/articles/s4427…

These and other results in the paper could help explain difficulties considering abstinence and vulnerability to relapse. Big thanks to @docqhuys @mpwpaulus @KhalsaLab @MarishkaMehta @SamuelTaylorCS and other twitterless co-authors for their very helpful contributions!

Groups also differed on a cognitive reflectiveness test, and a mediation model suggested differences in reflectiveness partially accounted for greater pruning in the methamphetamine users. The effect of anxiety induction on craving was also positively associated by pruning levels

Huge congrats to Claire Lavalley on this new paper. nature.com/articles/s4139… Results show that methamphetamine users show a specific deficit in model-based planning, in which they fail to simulate plans with large short-term losses (elevated aversive pruning). Brief thread below!

@RENiemeyerPhD Sure, I don’t see why not. Would be interesting to try!

@RyanSmith_LIBR Do you think it would be possible to apply this measure to evaluate the effectiveness of AI partners and contrast with human counterparts?

"The Inter-Personal Affect Regulation Test (IPART): a Performance-Based Assessment of the Ability to Improve the Emotions of Others" emotionandpsychopathology.org/journal/articl… Congratulations to co-first author Claire Lavalley on validating this new performance-based measure!

We expect greater skills in regulating others emotions will be crucial for an individual to maintain social support, which is especially important for individuals with emotional disorders and other forms of psychopathology. We hope others will also find this measure useful!

@Ella_Maru @molpsychiatry Thanks! Great question. People with high trait anxiety experience anxious states more frequently, leading to development of compensatory strategies. So our thought was that anxiety induction might interfere less with reflective cognition in these individuals as a result.

@RyanSmith_LIBR @molpsychiatry Impressive work, Dr. Smith & team! Is trait anxiety less disruptive because it’s already baseline?

Very excited to congratulate Ning Li and Claire Lavalley on their new paper published in @molpsychiatry. "Directed exploration is reduced by an aversive interoceptive state induction in healthy individuals but not in those with affective disorders" (nature.com/articles/s4138…) 🧵👇

An important note - an earlier preprint from this study instead reported that anxious depression showed elevated directed exploration than HCs that was reduced by the anxiety induction. This was due to a coding error we caught during revision. We apologize for this error.

In this model/task, the slower learning rates suggest that uncertainty in anxious depression may have reduced at a slower rate. Big thanks to our co-authors: @NRDlab @KhalsaLab @mpwpaulus @AnnieChuning @SamuelTaylorCS @CarterMGoldman @rowan_hodson @LIBR_Tulsa

We also test their sensitivity to task manipulations, and how strongly parameters in each model correlate with affective measures, trait measures, and psychiatric symptoms. We hope these results offer useful insights into the relative strengths and weaknesses of each framework.

We first compare their relative ability to account for behavior on a three-armed bandit task in a sample from the US and a sample from Taiwan. We then look at how the parameters correlate with each other and how strongly their mechanistic explanations overlap.

Reinforcement Learning and Active Inference are two frameworks used in computational psychiatry, but these are rarely directly compared empirically. Here, we aimed to compare these in a more systematic manner by fitting each to multiple datasets: papers.ssrn.com/sol3/papers.cf…