New in @pnas.org: doi.org/10.1073/pnas...

We study how humans explore a 61-state environment with a stochastic region that mimics a “noisy-TV.”

Results: Participants keep exploring the stochastic part even when it’s unhelpful, and novelty-seeking best explains this behavior.

#cogsci #neuroskyence

And if you're not at CCN, you can check out our preprint here (tweeprint coming soon!):
osf.io/preprints/ps...

Looking forward to your thoughts and comments! ☺️

Attending #CCN2025?
Come by our poster in the afternoon (4th floor, Poster 72) to talk about the sense of control, empowerment, and agency. 🧠🤖

We propose a unifying formulation of the sense of control and use it to empirically characterize the human subjective sense of control.

🧑‍🔬🧪🔬

And if you're not RLDM, you can check out our preprint here (tweeprint coming soon!):
osf.io/preprints/ps...

Looking forward to your thoughts and comments! ☺️

Attending #RLDM2025?
Come by our poster in the evening to talk about the sense of control, empowerment, and agency. 🧠🤖

We propose a unifying formulation of the sense of control and use it to empirically characterize the human subjective sense of control.

🧑‍🔬🧪🔬

I'm thrilled to share that I was recently awarded the EPFL Dimitris N. Chorafas Foundation Award for my Ph.D. thesis, "Seeking the new, learning from the unexpected: Computational models of #surprise and #novelty in the #brain." 🧠🧑‍🔬🧪

Award news: actu.epfl.ch/news/dimitri...

Yet, we found that combining novelty & information gain nearly always performs best! This aligns with recent experimental results in the neurosciences, suggesting that *novelty* and *information gain* are two principal axes of curiosity-driven behavior!

6/7

We developed an algorithm to systematically generate diverse, realistic environments, in which we tested 6 intrinsic rewards against different performance measures.

*Key result?*
The "best reward" depends on the environment class & performance measure = no universal winner!

5/7

Hence, identifying the merits of curiosity involves three main elements:
1. Intrinsic rewards (drives of curiosity)
2. Exploration performance (objectives of curiosity)
3. Environment structures

*Our goal?*
To demystify the interaction between these elements!

4/7

The brain predicts its future observations, but the computational underpinning of this predictive processing is debated. 🤔
In our workshop in #Cosyne2024 (Monday), we'll discuss the links between different computational models and experimental paradigms on the predictive brain!

... different stages of these behaviors (e.g., from novelty-detection to activation of neuromodulatory systems) and discuss how recent comp. models of curiosity enable integrating different behavioral expressions of curiosity into one theoretical framework. 4/6

We start with a classification of different behavioral expressions of curiosity: On one side, we put *reactive orienting* for seeking immediately available information, and, on the other side, we put *active choices* for actions associated with anticipated information. 2/6

What do we talk about when we talk about "curiosity"?
In our new paper in Trends in Neuro. (with K. Kondrakiewicz, W. Gerstner, & S. Haesler), we address this question by reviewing the behavioral signatures, neural mechanisms, and comp. models of curiosity: doi.org/10.1016/j.ti...

This is really fun! 😋