Huge congrats to Claire Lavalley on this new paper. 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!

RT @CommsPsychol: Computational modeling revealed that individuals with methamphetamine use disorder show reduced information-seeking and s….

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.

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

HCs and methamphetamine users completed an established multistep planning task. From some starting positions, the optimal path required transitioning through a large loss. Unlike other parameters (horizon, reward sensitivity), group differences were selective to aversive pruning.

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!.

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

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.

In general, results suggest state anxiety may interfere with reflection on relative uncertainty and reduce DE in healthy individuals, while consistently elevated anxiety in anxious depression may lead to compensatory strategies that reduce sensitivity to this effect.

A Bayesian (Kalman filter) learning model also revealed slower learning rates in anxious depression independent of anxiety induction.

Anxiety induction reduced directed (but not random) exploration in healthy individuals, but not in those diagnosed with anxious depression. Directed exploration was also associated with scores on a cognitive reflection test.

We combined the Horizon Task with a somatic (interoceptive) anxiety induction using breathing resistance. Both healthy individuals and those diagnosed with anxious depression participated (N=58 and 61, respectively).

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" ( 🧵👇.

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:

RT @Ch_Verdonk: I'm excited to attend the #ECNP Workshop on Applied Neuroscience and present our findings on markers of abnormal gastrointe….