r/ScienceNcoolThings

I’ve been working on an app called Frog Spot over the past year to identify frogs by their calls. It started as a personal project, and I’ve been building it out in my free time.

You can record a frog and get an identification in seconds, along with a spectrogram and additional information about the species. I recently added a heat map showing where frogs are likely to be calling based on hourly weather data.

It originally launched on iOS, and I just released it on Android as well.

Right now, it’s focused on species in the United States, and I’m working on expanding it to places like the UK and other regions as I build out more data.

I’m continuing to improve the accuracy, expand species and regions, and add new features over time. The goal is to make something genuinely useful for people who are out listening to frogs and trying to understand what they’re hearing.

There’s a free trial to try out the app, and after that, it’s supported through a subscription or one-time purchase to help cover ongoing development and server costs.

If anyone here spends time listening for frogs or recording calls, I’d be interested to hear what you think, as I continue to improve and refine the app.

If you're interested, please give it a try!

Android: https://play.google.com/store/apps/details?id=com.epochtechnologies.FrogFinder

IOS: https://apps.apple.com/us/app/frog-spot/id6742937570

For 100 years, we assumed learning worked like reps at the gym: more practice = more learning.

Pavlov’s dog heard a bell, got food, repeat repeat repeat — and that became the foundation of basically every learning theory in psychology.

A study just published in Nature Neuroscience (Feb 2026) challenges a key part of that model.

UCSF researchers trained mice to associate a sound with sugar water, but split them into two groups:

∙	Group A heard the tone every 60 seconds

∙	Group B heard the tone every 600 seconds

Group A got 20x more repetitions in the same amount of time. By the old model, they should have learned way faster. They didn’t. Both groups learned exactly the same total amount.

The brain doesn’t count reps, it measures time between rewards and uses that gap to decide how much to learn from each experience.

Rare, spaced-out events get treated as high-value information. Rapid-fire repetitions get discounted, your dopamine system essentially says “this keeps happening, so each instance isn’t that informative.” The learning per trial scales proportionally with the gap between trials.

What this actually means in practice:

∙	This is a neurochemical explanation for why cramming fails — not just “you forget faster afterward,” but your brain literally extracted less information per session during cramming in the first place

∙	The researchers suggest it could inform addiction treatment — cue-reward timing plays a role in how associations like smoking habits form and potentially how to disrupt them. This is speculative though, the study only tested mice

∙	Current AI is built on the old repetition-heavy model. The researchers think incorporating timing intervals could help machines learn faster from less data — though that’s also still theoretical

The lead researcher’s quote is pretty striking: “Our brains can learn faster than our machines, and this study helps explain why.”

TL;DR: Your brain learns the same amount whether you cram 20 sessions or space out 1, as long as the total time is equal. Repetition count matters less than the gaps between repetitions. The century-old assumption that more trials = more learning appears to be wrong.

Full paper: https://idp.nature.com/authorize?response\_type=cookie&client\_id=grover&redirect\_uri=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41593-026-02206-2