Niklas Müller

Brain alignment in deep neural networks emerges early and independently of object classification

This study shows brain alignment in deep neural networks is present early and independent of object classification, suggesting it reflects environmental structure rather than classification strategy.

Image-grounded encoding models reveal distinct temporal profiles of naturalistic object and scene processing in the human brain

The paper uses image-grounded encoding models to show that object processing in the human brain has a delayed temporal profile compared to scene processing in natural visual environments.

Object-zoomed training of convolutional neural networks inspired by toddler development improves shape bias

The paper introduces object-zoomed training for CNNs to improve shape bias, aligning CNN performance with human-like object recognition strategies.

Spatial sampling of deep neural network features improves encoding models of foveal and peripheral visual processing in humans

This study improves DNN encoding models of human visual processing by aligning their spatial sampling with human retinal ganglion cell density, revealing distinct temporal profiles for central and peripheral visual information.

Learning to See Like a Child: Why Viewpoint Diversity is Fundamental for Human-Aligned Object Recognition

This paper shows that increasing viewpoint diversity in training improves generalization to novel views, aligning models more closely with human object recognition strategies.