I'm quite proud to finally have my first first author journal paper out! It was  published in Frontiers in Psychology Cognitive Science with the title:

„A predictive processing model of perception and action for self-other distinction“.

That is a mouth full, but quite nicely covers what we are aiming to talk about: Our new active inference model that is able to minimize free energy in action and perception of handwritten numbers. All the while it is also able to infer a sense of agency, the feeling of being responsible for the action you're seeing. We run several simulation scenarios and show you the dynamics of its behavior. Please read on 😅


We developed and implemented a hierarchical model for perception and production of hand-written numbers, based on active inference and free energy minimization. Within the predictive processing literature it is argued that to make the world meet your expectations you sometimes produce actions that change the world. This is what is called active inference, and it is one of the ways to minimize your uncertainty about the world. This uncertainty is what we call free energy and it could roughly be described as an information theoretic potential for work you need to do to better explain the world and what happens within it. You can either try to find a different explanation for what you see or you can try to act upon the world (inlcluding other people) so that your uncertainty about it is minimized.

Our hierarchy with which we try to model the processes necessary to represent the dynamics of producing and perceiving handwritten numbers, is roughly associated with areas in the brain.

From a high-level area that creates clusters of similarly looking numbers (C), over an intermediate area that stores sequences of actions needed for coming up with the trajectory for drawing a number (S), down to areas for the visual (V) and muscle coordinating aspects (M) for writing and the perception of writing. 

Our model not only makes use of spatial, but also temporal aspects of actions, so that when it learns to write the number 2, it also learns when and how fast to write the number's upper and lower strokes.

Spatial and temporal aspects of action also inform an embedded model that integrates different cues for sense of agency. To be more specific, what allows us to infer that we are ourselves writing a number, is to perceive the actual stroke that you intended to write, but also a crucial part is its timing. This allows us to attribute a sense of agency to own actions.

What an attribution of agency also allows us is to infer an early (motor coordination level) distinction of own from other’s actions. Imagine you have no sense of another person being around and you cannot see your hand and the pen in your hand. You only see a white canvas in front of you and you have the ability to write something. This is all the model knows about its world. Now, when you write something all you can do to distinguish your own handwriting from that of somebody else, is to look at whether you can recognize your own writing. This is especially tricky when somebody else would try to write something at the same  time as you.

This is what we have the model go through and we show its dynamics and the outcome in the following figure.

We simulate and compare different scenarios of perception and production to showcase the belief dynamics, and how free energy is minimized on the different levels of the hierarchy. We also show how prediction contradicting feedback creates free energy and decreases precision to disrupt a build-up of sense of agency.

We also show how prediction contradicting feedback creates free energy to disrupt a build-up of sense of agency. What we did here, was to have the model write out a 1, and it can perceive its muscles to actually write a 1, but at the same time it would visually perceive the writing of a 3.

And here is the comparison figure for a successful and an unsuccessful attribution of agency to a produced action.

A higher agency means that you perceive the writing as your own. 

I hope you enjoy reading the paper and please contact me if you have any questions :)