This comment is incorrect.

For experiments 1 through 4, N was 38, 389, 396, and 389. The subjects were not undergrad psych students.

The article linked in the parent comment does not correspond to any experiment in the blog post or the Nature Comms paper.

Here's the participants subsection in full for anyone that can't access the paper. Emphasis my own, made to help read

> __Experiment 1__ included 38 participants with normal or corrected vision. Participants gave informed consent and were awarded reasonable compensation for their time and effort. Participants were recruited from our institute but were not involved in any projects with the research team. __Experiment 1 control__ (i.e., Experiment SI-5) included 50 participants recruited from an online rating platform. For __Experiments 2–5__, we performed psychophysics experiments using an online rating platform. In each experimental condition, approximately 100 participants were recruited to participate in the task (see Supplementary Table 18 for the exact number). No statistical method was used to predetermine the number of participants, but the sample size was decided to be comparable to that used in previous similar studies. Participants received compensation in the range of $8–$15 per hour based on the expected difficulty of the task. No sex or age information was gathered from the participants for all our studies. Our participants were all located in North America and were financially compensated for their participation. __We excluded participants if__ they were not engaged in the task, as assessed using randomly placed catch trials with an unambiguous answer (e.g., pairing an unperturbed dog image with a cat image and asking which image is more cat-like). If a participant failed one catch trial for Experiments 2, 3, and 5, or two catch trials for Experiment 4, the task automatically terminated and their data was not analyzed.

Parent's numbers are specifically drawn from Figure 3 caption. (Some text may not format correctly. Apologies if I didn't catch)

> a Participants are shown two perturbations of the same image, of true class T, and are asked to select the image which is more like an instance of some adversarial class A. The image pair remains visible until a choice is made. b One of the two choices is an adversarial perturbation that increases the probability of classifying the image as A, denoted A↑. Experiment 2: T = A; the second image is perturbed to be less A-like, denoted A↓. Experiment 3: T ≠ A; the second image is formed by adding a right-left flipped version of the adversarial perturbation, which controls for the magnitude of the perturbation while removing the image-to-perturbation correspondence. Experiment 4: T ≠ A; the second image is an adversarial perturbation toward a third class , denoted . c We show examples of adversarial images which empirically yielded human responses consistent with those of the ANN (indicated by the red box) for ϵ = 2 and 16, corresponding to the lowest and largest perturbation magnitudes used in these experiments. Example images in (a–c) are obtained from the Microsoft COCO dataset62 and OpenImages dataset63; images in (a, b, and c) left are used for illustration outside of our stimulus set due to license limitations. d Box plots (same convention as Fig. 2c) quantifying participant bias toward A↑ (where A = T for Experiment 2 and A ≠ T for Experiments 3 and 4), as a function of ϵ for four different conditions (each a different adversarial class A) collected from n=389 participants for Experiment 2 (cat n = 100, dog n = 100, bird n = 90, bottle n = 99), n = 396 participants for Experiment 3 (cat n = 96, dog n = 100, bird n = 101, bottle n = 99) and n = 389 independent participants for Experiment 4 (sheep vs chair n = 97, dog vs bottle n = 99, cat vs truck n = 98, elephant vs clock n = 94). The red points (with ± 1 SE bars) indicate the mean across conditions. The black dashed line indicates the performance of a random strategy that is insensitive to the adversarial perturbations.

So when they say "more than half the time," they could very well be 9 and 7 people?

No wonder they didn't cite the actual numbers in this summary write-up.

This result would be a lot more believable to me if I could take my own 20-30 question quiz and see how "good" I am at picking the "more X like" images. It seems like a missed opportunity for gathering more data, even if that data would be gathered in a less controlled setting.

Yeah, I was disappointed there wasn't example pairings with the result on another page or something just so I could give it a try! Every example is paired with info about how it was perturbed right next to it.

What do your power calculations for the effect size say a minimal sample should be?

My stats professor worked in cardiology and he shared a paper about n=1 or 2 studies: "If nobody dies, is everything OK?"

What are you talking about? The paper says that for experiment 1 they had 38 participants. Experiment 1 control had 50 participants. Experiment 2-5 had approximately 100 participants each.

> 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.

Am I the only one who finds this to be a sort of wasteful experiment for one of the supposed top research labs in the country to be publishing in such a (supposedly) prestigious journal? The findings aren't super shocking although they would be interesting enough if they had managed to collect a large enough sample.

Instead they barely grasp at straws and come to an obviously inflated conclusion that neural nets and human brains are similar in some way because of this (which _certainly_ isn't something they arrive at or even experiment for, in my opinion).

> findings aren't super shocking

Aren’t they? Are you sure you understand what is the finding?

> they would be interesting enough if they had managed to collect a large enough sample

They did. The grand parent comment failed to read the right paper.

> obviously inflated conclusion that neural nets and human brains are similar in some way

But that is what they find. The human looks at two almost identical looking images of flowers. And yet when they are asked which one is more cat like they pick the one which the neural network thinks is cat-like too.(Or at least they pick it more often than if they were just selecting randomly in this seemingly nonsense task.) That is exactly “similar in some way”. Similar in which image they find more cat-like. That is the similarity.

> Aren’t they? Are you sure you understand what is the finding?

Yeah, I got it.

> But that is what they find. The human looks at two almost identical looking images of flowers. And yet when they are asked which one is more cat like they pick the one which the neural network thinks is cat-like too.(Or at least they pick it more often than if they were just selecting randomly in this seemingly nonsense task.) That is exactly “similar in some way”. Similar in which image they find more cat-like. That is the similarity.

I'm saying the implication is that there is a not-yet-understood deeper connection illustrated by this discovery. I don't think they tested for that and I don't think it's hinted at. There are lots of reasons why this trick would work on both humans and neural nets that would amount to effectively no similarities in structure or process otherwise. That isn't to say it's not related somehow, just that their experiment simply shows the outcome is the same, but doesn't indicate why it's the same.

Having said all of that, you're definitely correct to imply I haven't read the full study. And I'm happy to admit my original comment contains misunderstandings. Sorry about that.