Bastien Blain

It looks quite cool! Is there a preprint version somewhere by any chance?

Origins and consequences of cognitive fatigue

Feature Review by Mathias Pessiglione, Bastien Blain (@bastien-blain.bsky.social), Antonius Wiehler, & Shruti Naik

Free access before May 20: tinyurl.com/2va75b5j

European alternatives for popular services | European Alternatives We help you find European alternatives for digital service and products, like cloud services and SaaS products.

european-alternatives.eu/alternatives...

“How” web searches change under stress - Scientific Reports Scientific Reports - “How” web searches change under stress

New(ish) paper w/Tali Sharot & @bastien-blain.bsky.social! We find that during stress (both COVID and personal events), people search for more “How” questions online. This shift indicates a heightened demand for actionable info.

Link: nature.com/articles/s41...

🧵1/3

Please repost this open PhD studentship.

Neural mechanisms of information seeking We ubiquitously seek information to make better decisions. Particularly in the modern age, when more information is available at our fingertips than e…

After skimming, this looks like a great review of the subjective value of information vis-a-vis neural mechanisms. Straight to the top of my reading list!

www.sciencedirect.com/science/arti...

#SocialPsyc #neuroskyence #PsychSciSky

Join the Lab We're hiring! Postdoctoral Fellowship: Predictive Models for Cognitive Fluctuations  Closing date: 12 noon (IST), June 1st 2024 We are seeking a Research Fellow to pioneer the development of passive s...

We are taking applications for a 3-year POSTDOC position on the topic of tracking cognitive dynamics using passive smartphone sensor data, in clinical and non clinical samples with the Neureka app gillanlab.com/join-the-lab/ Closing June 1st. Pls share 🔁👍 or reach out with any informal queries!

RTing for the weekend crowd!

Check out a new paper on how motivation shapes predictions to interact w/ predictive processing in forming social memories.

Out in Neuroscience and Biobehavioral Reviews, part of a special issue spearheaded by Yee Lee Shing, Oded Bein, & Sophie Nolden.

An inductive bias for slowly changing features in human reinforcement learning Identifying goal-relevant features in novel environments is a central challenge for efficient behaviour. We asked whether humans address this challenge by relying on prior knowledge about common properties of reward-predicting features. One such property is the rate of change of features, given that behaviourally relevant processes tend to change on a slower timescale than noise. Hence, we asked whether humans are biased to learn more when task-relevant features are slow rather than fast. To test this idea, 100 human participants were asked to learn the rewards of two-dimensional bandits when either a slowly or quickly changing feature of the bandit predicted reward. Participants accrued more reward and achieved better generalisation to unseen feature values when a bandit’s relevant feature changed slowly, and its irrelevant feature quickly, as compared to the opposite. Participants were also more likely to incorrectly base their choices on the irrelevant feature when it changed slowly versus quickly. These effects were stronger when participants experienced the feature speed before learning about rewards. Modelling this behaviour with a set of four function approximation Kalman filter models that embodied alternative hypotheses about how feature speed could affect learning revealed that participants had a higher learning rate for the slow feature, and adjusted their learning to both the relevance and the speed of feature changes. The larger the improvement in participants’ performance for slow compared to fast bandits, the more strongly they adjusted their learning rates. These results provide evidence that human reinforcement learning favours slower features, suggesting a bias in how humans approach reward learning. Author Summary Learning experiments in the laboratory are often assumed to exist in a vacuum, where participants solve a given task independently of how they learn in more natural circumstances. But humans and other animals are in fact well known to “meta learn”, i.e. to leverage generalisable assumptions about how to learn from other experiences. Taking inspiration from a well-known machine learning technique known as slow feature analysis, we investigated one specific instance of such an assumption in learning: the possibility that humans tend to focus on slowly rather than quickly changing features when learning about rewards. To test this, we developed a task where participants had to learn the value of stimuli composed of two features. Participants indeed learned better from a slowly rather than quickly changing feature that predicted reward and were more distracted by the reward-irrelevant feature when it changed slowly. Computational modelling of participant behaviour indicated that participants had a higher learning rate for slowly changing features from the outset. Hence, our results support the idea that human reinforcement learning reflects a priori assumptions about the reward structure in natural environments. ### Competing Interest Statement The authors have declared no competing interest.

In a world full of noise, how do we decide what's important? Our research reveals that humans leverage a key insight: relevant signals change slowly, but noise fluctuates rapidly.
Excited to share the first project of my PhD!
🧪 🧠📈 🧠💻 #PsychSciSky (1/8)

Attention neuroscientsts!

We just launched Neurosynth Compose: A free and open platform for neuroimaging meta-analysis. NS-Compose makes it easy to perform custom neuroimaging meta-analyses without leaving the browser.

It's live, check it out! compose.neurosynth.org

Contemplative neuroaesthetics and architecture: A sensorimotor exploration This paper takes initial steps towards developing a theoretical framework of contemplative neuroaesthetics through sensorimotor dynamics. We first arg…

Contemplative neuroaesthetics and architecture: A sensorimotor exploration
www.sciencedirect.com/science/arti...

You had to beg me though, don't know which one is worst

New paper alert!
With his permission, I'd like to reproduce here the explanation thread by the fantastic Roeland Heerema on this very nice paper (obviously a perfectly objective opinion) to which I modestly contributed!

Naturalistic reinforcement learning Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitiv…

🌟First post over here, which I'm very happy to use to share a new paper in TICS with Angela Radulescu & Kara Emery. 🌟

We review work taking an approach we refer to as "naturalistic reinforcement learning", which uses more naturalistic approaches to study learning and decision-making.

Distractor-specific control adaptation in multidimensional environments bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution

Now that I'm here, I'd like to draw your attention to a new preprint by Davide Gheza (not yet here) and me. We ask how people deal with cognitive control demands in multidimensional environments, and find that people adapt to control demands in a highly dimension-specific fashion.

#compneuro #compneurosky

Hey folk!

I’m Assistant Professor in Paris, working on:
- reinforcement learning
- mood
- intrinsic motivation
- how they are impaired in depression,
using tools from neuroscience, psychology, AI, and economics.

Looking forward to connect!
#Neuroskyence #AcademicSky #PsychSciSky #MedSky