People Who Can't Visualize Anything Are Challenging a 300-Year-Old Theory of Thought

Mental imagery might not be as central to complex human thought as we believed, philosophers say.

Seeing Theory

A visual introduction to probability and statistics.

Visual design rules you can safely follow every time

You do not have to follow these rules every time. If you have a good reason to break any of them, do. But they are safe to follow every time.

Situational Blindness

This response may be read as a standalone piece, though it will of course be more meaningful if you read the original article first.

Computer Vision Syndrome

In no point in human history have people been staring at bright screens just a few feet from their face like they are today. Computer vision syndrome or CVS (no…

Visual Variables

Visual variables are “the differences in map elements as perceived by the human eye” (wiki.gis.com). No matter what the type of map, these are the fundamental ways in which graphic symbols can be distinguished.

All-in-one visual testing and review platform | Percy

Where to Find the Colors Your Screen Can’t Show You

An atlas of the vibrance of the real world

Where to Find the Colors Your Screen Can’t Show You

An atlas of the vibrance of the real world

aesthetics

Aesthetics is relevant to many aspects of computing. Here, we are mostly concerned of the superficial visual appearances and their technological bases.

Defense Against Dishonest Charts

This is a guide to protect ourselves and to preserve what is good about turning data into visual things.

Mirage: The Illusion of Visual Understanding

Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual–language reasoning remain surprisingly poorly understood. We report three findings that challenge prevailing assumptions about how these systems process and integrate visual information. First, Frontier models readily generate detailed image descriptions and elaborate reasoning traces, including pathology-biased clinical findings, for images never provided; we term this phenomenon mirage reasoning. Second, without any image input, models also attain strikingly high scores across general and medical multimodal benchmarks, bringing into question their utility and design. In the most extreme case, our model achieved the top rank on a standard chest X-ray question-answering benchmark without access to any images. Third, when models were explicitly instructed to guess answers without image access, rather than being implicitly prompted to assume images were present, performance declined markedly. Explicit guessing appears to engage a more conservative response regime, in contrast to the mirage regime in which models behave as though images have been provided. These findings expose fundamental vulnerabilities in how visual–language models reason and are evaluated, pointing to an urgent need for private benchmarks that eliminate textual cues enabling non-visual inference, particularly in medical contexts where miscalibrated AI carries the greatest consequence. We introduce B-Clean as a principled solution for fair, vision-grounded evaluation of multimodal AI systems.