McGurk effect - Auditory Illusion - BBC Horizon Clip

sixesfullofnines1 HN recommenders · 2021–2025
745K views · demo · evergreen · Uploaded 2016-03-16

A two-minute BBC Horizon clip demonstrating the McGurk effect, where what you see of a speaker's mouth changes the sound you hear.

Recommended as a quick demo of vision overriding hearing, and cited as an example of perception producing confident wrong answers.

Watch on YouTube

4 comments from Hacker News

@jcampbell12021 · in “The Legendary Study That Embarrassed Wine Experts (2014)”

The visual processing part of the brain can completely overwhelm auditory processing: see the mcgurk effect. [VIDEO] I am not at all surprised that there are also olifactory illusions which are impossible to “unsee”.

@beagle32021 · in “Google open-sources the Lyra audio codec”

Are you aware that the same exact uncompressed recording sounds different depending on context? This is known as the McGurk effect. Very worth your two minutes if you're not yet familiar with the effect: [VIDEO]

RecommendsView on HN
@smartscience2024 · in “Why you can hear the temperature of water”

The McGurk effect [VIDEO] is another example, which might be described as people learning to lip read without them being aware that they have done so.

@MostlyStable2025 · in “AI Blindspots – Blindspots in LLMs I've noticed while AI coding”

This is, I think, a better way to think about LLM mistakes compared to the usual "hallucinations". I think of them as similar to human optical illusions. There are things about the human visual cortex (and also other sensory systems, see the McGurk Effect [0]), that, when presented with certain kinds of inputs, will consistently produce wrong interpretations/outputs. Even when we are 100% ware of the issue, we can't prevent our brains from generating the incorrect interpretation. LLMs seem to have similar issues along dramatically different axes, axes that humans are not used to seeing these kinds of mistakes; where nearly no human would make this kind of mistake and so we interpret it (in my opinion incorrectly) as lack of ability or intelligence. Because these are engineered systems, we may figure out ways to solve these problems (although I personally think the best we will ever do is decrease their prevalence), but more important is probably learning to recognize the places that LLMs are likely to make these errors, and, as your comment suggests, design work flows and systems that can deal with them. [0] [VIDEO]