Good question! We haven't tested these cases you've mentioned but Jason Fischer's 2016 paper found that PN doesn't respond strongly to social prediction (on hieder and simmel-like displays)

We have started to look in the cerebellum. It is still early days so keep an eye out for updates in the future!

Thanks to my co-authors and all the people who gave constructive feedback over the course of this project! Special shout out to Kris Brewer for shooting the videos used in Experiment 1 and @georginawooxy.bsky.social for her deep neural network expertise.

(12/12)

Our findings show that PN has abstract object contact information and provide the strongest evidence yet that PN is engaged in predicting what will happen next. These results open many new avenues of investigation into how we understand, predict, and plan in the physical world

(11/n)

Our main results are i) not present in the ventral temporal cortex, ii) not present in the primary visual cortex -- i.e, our stimuli were unlikely to have low-level visual confounds and iii) are replicable with different analysis criteria & methods. See paper for details.

(10/n)

Short answer: Yes! Using MVPA we found that the PN has information about predicted contact events (i.e., collisions). This was true not only within a scenario (the ‘roll’ scene above), but also generalized across scenarios indicating the abstractness of representation.

(9/n)

That is,
(8/n)

When we see this: Does the PN predict this?

In our second pre-registered fMRI experiment, we tested the central tenet of the ‘physics engine’ hypothesis – that the PN runs forward simulations to predict what will happen next. If true, PN should contain information about predicted future states before they occur.

(7/n)

Given their importance for prediction, we hypothesized that the PN would encode object contact. In our first pre-registered fMRI experiment, we used multi-voxel pattern analysis (MVPA) and found that only PN carried scenario-invariant information about object contact.

(6/n)

If a container moves, then so does its containee, but the same is not true of an object that is merely occluded by the container without contacting it!

(5/n)

However, there was no evidence for such predicted future state information in the PN. We realized that object-object contact is an excellent way to test the Physics Engine hypothesis. When two objects are in contact, their fate is intertwined:

(4/n)

These results have led to the hypothesis that the Physics Network (PN) is our brain’s ‘Physics Engine’ – a generative model of the physical world (like those used in video games) capable of running simulations to predict what will happen next.

(3/n)

How do we understand, plan and predict in the physical world? Prior research has implicated fronto-parietal regions of the human brain (the ‘Physics Network’, PN) in physical judgement tasks, including in carrying representations of object mass & physical stability.

(2/n)