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Overall this led us to conclude that outcome-irrelevant learning reflects an automatic credit assignment process, whereby explicit knowledge about the causal structure of the decision environment is limited in its influence on reward learning. Stay tuned for future work...

We tried to make colors outcome-irrelevant yet task-relevant, just like locations (part of the task-set, but should not be assigned credit). We found that when colors signified the availability of locations but were known to be outcome-irrelevant, they were still assigned credit.

We weren't sure whether outcome-irrelevant learning is location-specific or reflects a general tendency to assign credit to all features that were attended to during action execution. So we made location the outcome-relevant feature and checked whether outcome-irrelevant colors were given credit...

In the process, we found that individual differences in the computational model parameter (λ) capturing outcome-irrelevant learning are reliable across sessions and that outcome-irrelevant learning is greater in participants with low working memory abilities.

In Experiment 2, we then considered the possibility that instructions are not enough to inhibit outcome-irrelevant learning, and participants need to experience irrelevance "at first hand". So we let them complete the task on three consecutive days, and yet outcome-irrelevant learning persisted...

The task was framed as a magical forest, and participants chose between colored leaves and received a probabilistic reward. Despite knowing that the leaves' locations were irrelevant, they still learned their arbitrary associations with rewards, leading to suboptimal choices and reduced gains...

Stefano Palminteri @stepalminteri.bsky.social · 14/04/2026

Excited to share our recent publication in @natcomms.nature.com We started by trying to make people stop associating the money rewards they obtain with irrelevant features of their behavior. In the first experiment, we used clear instructions framed as a story, and it didn't help...

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Reposted by Ido Ben-Artzi

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...

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Reposted by Ido Ben-Artzi

Gidon Frischkorn @gidon-frischkorn.bsky.social · 05/04/2026

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

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Autism-associated learning patterns show reduced credit assignment to outcome-irrelevant features - Translational Psychiatry

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...

nature.com

Translational Psychiatry - Autism-associated learning patterns show reduced credit assignment to outcome-irrelevant features

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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.

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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.

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Niklas Buergi @niklasbuergi.bsky.social · 09/03/2026

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).

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Reposted by Ido Ben-Artzi

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...

nature.com

A neural signature of adaptive mentalization | Nature Neuroscience

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Paul Sharp @paulbsharp.bsky.social · 02/09/2025

🚨 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

sharplabbiu.github.io

Lab Website

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Reposted by Ido Ben-Artzi

Lauren Atlas @laurenatlas.bsky.social · 29/08/2025

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/...

science.org

Mapping the anatomy of placebo analgesia

The identification of somatotopy in brainstem pain modulatory pathways could help treat chronic pain

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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...

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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?

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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.

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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.

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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.

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