8/ This framework unites decades of fragmented data — from monkey neurophysiology to human EEG — into a single, testable theory. Just as hippocampal place cells map the world for navigation, peripersonal neurons map value around the body — for action.

7/ These ANNs also naturally separate into sub-networks specialised for avoidance and interception, mirroring the modularity of both the macaque brain and the egocentric value map that we proposed.

6/ Remarkably, these action value fields naturally assemble into an *egocentric map*: a modular, composable predictive model of the world around the body, which allows rapid adaptation to novel situations by recombining existing knowledge.

5/ To test this, we used #ReinforcementLearning and computational modelling. We trained agents to interact with the world — and their networks spontaneously developed body-part-centered fields, not because of spatial proximity, but *because of value*.

4/ Our key insight was that peripersonal responses might not simply reflect stimulus configuration, but *action values* — the expected rewards or punishments from contacting objects.

3/ For decades we've known of neurons responsive to objects near the body—aka peripersonal neurons—initially viewed as proximity detectors.

However, these neurons also respond to stimulus value, speed, and motor repertoire.

Why are these neurons so flexible? What are they for?

2/ We show that #peripersonal neurons aren’t just proximity detectors — they encode the value of interacting with objects.

Together, they form egocentric value maps: short-term predictive models of the near-body space, akin to hippocampal maps of far space.

Short story below!

1/ Thrilled to start on Bluesky with our latest publication in Nature Neuroscience (@natneuro.nature.com) – and huge congratulations to RoryBufacchi who led this work (first at @ucl.ac.uk and then @iitalk.bsky.social) with extraordinary insight and fastidiousness.

Free full text: rdcu.be/eo3G3