New preprint (by Vandendriessche et al.)

In everyday life, choices often lead to multiple simultaneous outcomes — some positive, some negative. Yet most reinforcement learning research has focused on situations where each choice produces only a single outcome 1/5

osf.io/preprints/ps...

I wrote up something that's been in my head for a while: psychometric methods alone can't tell us what cognitive tasks and their indicators measure.

Correlating indicators across tasks is circular when constructs are defined by those same correlations.

osf.io/preprints/ps... 🧵1/3

This adds to evidence suggesting that autism involves not only disadvantages, but also a distinct cognitive style that can carry important strengths. Huge thanks to @lironrozenkrantz.bsky.social and @shaharnitzan.bsky.social for their guidance throughout this project. www.nature.com/articles/s41...

A more literal cognitive style may reflect a reduced tendency to search for hidden structure where none exists, leading to a more optimal behavior in these contexts.

One possible explanation for this association comes from the “Communication” subscale of the autistic traits questionnaire. Lower endorsement of items such as “I find it easy to read between the lines when someone is talking to me” was linked to lower outcome-irrelevant learning.

At the same time, we replicated a previous finding that higher compulsivity is associated with higher outcome-irrelevant learning. This is especially interesting because autistic traits and compulsivity are positively correlated, yet show opposite associations with outcome-irrelevant learning.

Here, we found that individuals with autism showed greater resistance to bias driven by outcome-irrelevant information. Across the full sample, higher autistic traits were associated with decreased learning of outcome-irrelevant information.

Our new paper asks whether autism is linked to the way people learn from rewards. We’ve previously shown that people not only learn to value the features that predict reward, but also assign credit to features of their actions that they know are irrelevant (in this case, the card's location).

How does the brain decide which mental strategy to use when inferring others' beliefs?

Excited to (finally!) see my first first-author paper out @natneuro.nature.com

Summary below 🧵 #CogSci #CogNeuro

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

The hippocampal map has its own attentional control signal!
Our new study reveals that theta #sweeps can be instantly biased towards behaviourally relevant locations. See 📹 in post 4/6 and preprint here 👉
www.biorxiv.org/content/10.6...
🧵(1/6)

🚨 Want to research the computational & neural mechanisms of planning and its disruption in mental health? If so, join our lab!

Here's one prestigious postdoc fellowship that just opened: azrielifoundation.org/azrieli-fell...

reach out w/your CV to paul.sharp@biu.ac.il

lab: sharplabbiu.github.io

Mapping the anatomy of placebo analgesia The identification of somatotopy in brainstem pain modulatory pathways could help treat chronic pain

Thanks to @massih.bsky.social for inviting me to co-author this commentary @science.org Mapping the anatomy of placebo analgesia | Science www.science.org/doi/10.1126/...

Many thanks to my PhD supervisor, @shaharnitzan.bsky.social, and to our collaborators Rani Moran, Maayan Pereg and Roy Luria for their invaluable contributions.
Read the preprint here:
osf.io/preprints/ps...

On a practical note, some of what appears to be “random exploration” could be explained by modeling humans associating rewards with random noise in the task.

Do humans automatically assign credit to all task-relevant (but outcome-irrelevant) features?
Does outcome-irrelevant learning persist even when the cost of it goes up?
Do high working memory individuals encode irrelevant values but inhibit them from influencing choices, or ignore them altogether?

Computational modeling shows that outcome-irrelevant learning is quite reliable across sessions, yet not everyone does this equally. Working memory capacity strongly predicts outcome-irrelevant learning. Suggesting working memory is central for maintaining a causal structure guiding learning.

To examine the possibility that participants are not convinced by the instructions, in Experiment 2, we gave 600 trials across three days, allowing them to infer that locations should be neglected. But, we find they keep assigning credit to outcome-irrelevant locations.

So we created a “magical forest” narrative, telling participants that the offered leaves are randomly driven to their locations by the wind. We find participants still show outcome-irrelevant learning, leading them to choose suboptimally and win a smaller money bonus.

Experiment 1 (N=504) was aimed at ensuring people truly understand the causal structure of the task. Previously, it was suggested that such credit assignment is due to participants forming a wrong model of the task, rather than due to an automatic model-free credit assignment.

We asked participants to choose cards to win rewards. Some cards had higher chances of winning than others, but the card locations on the screen were completely irrelevant. No matter how hard we tried, people still assigned value to locations.

Excited to share our new preprint! 🚨
Does human learning have an automatic aspect? Is it possible that we learn things that are counterproductive and only lead to reduced gains?

Excited to be in #NeurIPS2024

Visit my poster at the Behavioral ML workshop or just come say Hi

openreview.net/forum?id=JAD...

I was fucking joking!