We propose a theory of structure learning in the primate brain. We argue that the parietal
cortex is critical for learning about relations among the objects and categories that populate …

To understand a visual scene, observers need to both recognize objects and encode
relational structure. For example, a scene comprising three apples requires the observer to …

Abstract We introduce the Abductive Rule Learner with Context-awareness (ARLC), a model
that solves abstract reasoning tasks based on Learn-VRF. ARLC features a novel and more …

While deep learning has enjoyed significant success in computer vision tasks over the past
decade, many shortcomings still exist from a Cognitive Science (CogSci) perspective. In …

Counting the number of objects in a visual scene is easy for humans but challenging for
modern deep neural networks. Here we explore what makes this problem hard and study …

This work compares large language models (LLMs) and neuro-symbolic approaches in
solving Raven's progressive matrices (RPM), a visual abstract reasoning test that involves …

Years into the insider threat, it remains an universal challenge to predict and defend.
Concerning this problem, there has been a multitude of solutions, including the detection of …

Recently, a provocative claim was published that number sense spontaneously emerges in
a deep neural network trained merely for visual object recognition. This has, if true, far …

The insideness problem is an aspect of image segmentation that consists of determining
which pixels are inside and outside a region. Deep neural networks (DNNs) excel in …

We propose a theory of structure learning in the primate brain. We argue that the parietal
cortex is critical for learning about relations among the objects and categories that populate …