The change is part of a Republican plan to make it more difficult to vote or outright disenfranchise a bloc of voters viewed as more inclined to vote Democratic, the DNC lawsuit argues.

Neuroscientific learning theories propose that the brain acquires knowledge by constructing internal world models. Supervised learning, the dominant approach in deep neural networks (DNN), relies on external category labels, making it difficult to reconcile with biological learning. There is an increasing trend towards more biologically valid approaches, such as predictive (minimize future surprise) or contrastive (minimize response to expected, maximize to unexpected inputs) objectives, but these approaches typically rely on pretrained DNN models that vary widely in architecture, size, and hyperparameters, making direct comparisons difficult. Here, we isolate the effect of learning by comparing small, identical networks trained with predictive, contrastive, and supervised learning objectives, as well as local (layer-restricted) vs. global (full backpropagation) learning. We show that brain representations after statistical learning are better modeled by a predictive local target than a supervised or contrastive target, and that during learning, the brain attenuates category-specific representations while retaining predictive ones. We additionally show that predictive objectives explain brain variance that standard supervised DNNs do not, and that this variance is tied to predictive rather than stimulus or mismatch processing. These findings demonstrate a controlled approach for testing the algorithmic basis of learning and identify prediction as a core learning mechanism. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, 101229735 European Research Council, 406.22.24GO.030

Inequalities in education, health, technology and social status are often studied in isolation. This Focus issue shows how these inequalities are interconnected in the real world, and how competing vi...

Seeing a deadly ‘civil war’ unfold among chimpanzees has been a painful experience for John Mitani. His decades of watching wild great apes now evoke his family’s experience with xenophobia. But to hi...

The nativist–empiricist debate has long organized cognitive science, framing cognitive mechanisms as either innate or learned. Recent advances in controlled rearing and scalable domain-general neural ...

Using experiments and theory, the authors examine how olfactory memories are encoded in the zebrafish olfactory cortex. They find that experience reshapes the internal map of odor space, making releva...

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YouTube video by Veritasium

Neural network models can express computational hypotheses about brain information processing, but their high parametric capacity makes discriminating model alignment to experimental data challenging....

Although language models can be helpful in molecular design, they are not typically calibrated for uncertainty. Rankovic and colleagues present a method to train language models while taking into acco...