The AI pioneer on stepping down from Meta, the limits of large language models — and the launch of his new start-up

The Reward Positivity (RewP) is an electroencephalogram (EEG) feature that emerges following performance feedback and is commonly understood to index both positive and negative reward-prediction error (RPE+ and RPE−, respectively) signals. In contrast to this dominant perspective, we argue that the RewP is an independent EEG feature that selectively responds to positive RPE and is superimposed on a common background signal. We further propose that the RewP signals a goal prediction error: it is elicited by abstract signals instead of by hedonic 'rewards'. This goal prediction error appears to be produced by a critic-like architecture that is associated with the actor–critic framework in reinforcement learning. This perspective emphasizes the role of the RewP in goal attainment and cognitive control as opposed to being a simple indicator of reward receipt.

Providing utility functions for fitting Bayesian generalised additive multilevel models (BGAMMs) to time-resolved data (e.g., M/EEG, pupillometry, mouse-tracking, etc) and identifying clusters.

Interpretation of numerous MRI data may be incorrect: blood flow is not a reliable indicator of brain activity.

Aggression evolved to protect resources such as food from competitors, but animals must balance fighting and feeding so that they facilitate rather than hinder re-establishment of energy homeostasis. ...

Nature, Published online: 22 October 2025; doi:10.1038/s41586-025-09761-xDiscovering state-of-the-art reinforcement learning algorithms

ERC funded PhD position on Cultural Evolution posted on October 16, 2025 We are currently seeking a highly motivated individual for a ful...

A team at the Technical University of Munich’s TUM University Hospital has implanted a brain-computer interface in a patient paralyzed from the neck down.