31–40 of 86 posts
In case you were wondering what N was, their first experiment involved 16 undergrads psych students and the second experiment involved 12. https://link.springer.com/article/10.3758/BF03206939 Edit: I believe this linked survey is not the subject of the OP.
No wonder they didn't cite the actual numbers in this summary write-up.
"In our example, we may see a vase of flowers, but some activity in the brain informs us there’s a hint of cat about it." IMHO this is not the same as computer vision thinking a rolled over school bus is a snow plow. This is asking if someone sees an elephant or a unicorn in a cloud. Asking if a picture of a stop light at an intersection is "cat like" seems to be pretty suspectable to over fitting. Rorschach inkblot…
Consider the difference between a human stating "that's an impossible nonsense picture, but if had to describe it then it's a half-Cat and half-Truck abomination" compared to a computer yielding " There is a 50% chance that is a Truck, and a 50% chance that is a Cat."
A million brainscans. A million images. Fed into an AI image gen. It's doable.
"In our example, we may see a vase of flowers, but some activity in the brain informs us there’s a hint of cat about it." IMHO this is not the same as computer vision thinking a rolled over school bus is a snow plow. This is asking if someone sees an elephant or a unicorn in a cloud. Asking if a picture of a stop light at an intersection is "cat like" seems to be pretty suspectable to over fitting. Rorschach inkblot…
To me that evokes a dimension of disbelief, or suspicion that the data is wrong, separate from what it does or doesn't resemble. Consider the difference between a human stating "that's an impossible nonsense picture, but if had to describe it then it's a half-Cat and half-Truck abomination" compared to a computer yielding " There is a 50% chance that is a Truck, and a 50% chance that is a Cat."
All examples (sheep vs chair, dog vs bottle, cat vs truck, elephant vs clock) are organic vs inorganic. Perhaps participants are reusing bouba/kiki[1] skills, evaluating whether the image looks organic (rounded) or inorganic (spiky) - and making their choice accordingly. [1]: https://en.wikipedia.org/wiki/Bouba/kiki_effect
In case you were wondering what N was, their first experiment involved 16 undergrads psych students and the second experiment involved 12. https://link.springer.com/article/10.3758/BF03206939 Edit: I believe this linked survey is not the subject of the OP.
> https://link.springer.com/article/10.3758/BF03206939
What is this link for? You linked an article published in 1993. The posted article is about a totally different one: https://www.nature.com/articles/s41467-023-40499-0
Edit: It seems you clicked the first link on their page. That lead you to the historical springer article and you are mistakenly describing that as if that is the current study. It is not, it is one they are citing as prior research.
I wonder if that kind of trick could be used during the training of adversarial networks - GANs, to have a strong adversary
I'm not an expert per se, but I think the issue at its core is that convolutional networks are trained to look at small features out of context, and tricking those smaller features detectors is possible without changing the overall structure of the image.