In the real world, objects always appear in context. Many objects are reliably associated with a certain scene context (e.g., pots appear in kitchens) and other objects that appear in the same context...

Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such world models, but how these are instantiated and how ...

A workshop series on representation construction in cognitive science and AI. CogSci 2026 (Rio) and CCN 2026 (NYU).

By combining magnetoencephalography and eye tracking, this study sheds light on why people fixate on some parts of natural scenes longer than others. Rather than visual complexity, fixation durations ...

The Department of Psychology at the University of Southern Denmark invites applications for a full-time position as tenure-track Assistant Professor. The position is a six-year tenure-track appointmen...

How does visual context shape neuronal responses to local features? Fu et al. demonstrate that surround modulation in primary visual cortex neurons reflects how well context matches the optimal local ...

Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to chang...

Automatic differentiation (AD) frameworks such as JAX and PyTorch have enabled gradient-based optimization for a wide range of scientific fields. Yet, many "hard" primitives in these libraries such as...

The Brain Abstracted (2025) critiques treating abstractions in neuroscience as complete explanations of the brain, for their oversimplification and control-orientation. Chirimuuta argues that neurosci...

Author summary To gain a more complete picture of human visual processing, it is critical to understand the precise format of representations of naturalistic visual scenes. Recent work has approached ...

Thomas et al. examine how dataset effects may limit the generalizability of machine learning-based models of human choice.

While deep learning methods have achieved impressive success in many vision benchmarks, it remains difficult to understand and explain the representations and decisions of these models. Though vision ...

Tangtartharakul and Storrs use standardized neuropsychological tests to compare human visual abilities with those of visual language models (VLMs). They report that while VLMs excel in high-level obje...

Humans learn abstractions and use them to plan efficiently to quickly generalize across tasks -- an ability that remains challenging for state-of-the-art large language model (LLM) agents and deep rei...