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Study sheds light on why some people keep self-sabotaging

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Most people, after suffering consequences for a bad decision, will alter their future behavior to avoid a similar negative outcome. That's just common sense. But many social circles have that one friend who never seems to learn from those consequences, repeatedly self-sabotaging themselves with the same bad decisions. When it comes to especially destructive behaviors, like addictions, the consequences can be severe or downright tragic.

Why do they do this? Researchers at the University of New South Wales (UNSW) in Sydney, Australia, suggest that the core issue is that such people don't seem able to make a causal connection between their choices/behavior and the bad outcome, according to a new paper published in the journal Nature Communications Psychology. Nor are they able to integrate new knowledge into their decision-making process effectively to get better results. The results could lead to the development of new intervention strategies for gambling, drug, and alcohol addictions.

In 2023, UNSW neuroscientist Philip Jean-Richard-Dit-Bressel and colleagues designed an experimental video game to explore the issue of why certain people keep making the same bad choices despite suffering some form of punishment as a result. Participants played the interactive online game by clicking on one of two planets to "trade" with them; choosing the correct planet resulted in earning points.

For each click in two three-minute rounds, there was a 50 percent chance of choosing the correct planet and being rewarded with points. Then the researchers introduced a new element: clicking on one of the two planets would result in a pirate spaceship attacking 20 percent of the time and "stealing" one-fifth of a player's points. Selecting the other planet would result in a neutral spaceship 20 percent of the time, which did not attack or steal points.

The result was a very distinct split between those who figured out the game and stopped trading with the planet that produced the pirate spaceship, and those who did not. None of the participants enjoyed losing points to the attacking space pirates, but the researchers found that those who didn't change their playing strategy just couldn't make the connection between their behavior and the negative outcome.

The team identified three distinct behavioral phenotypes as a result of their experiments, representing the varying sensitivity of people to the adverse consequences (punishment) of their actions. Sensitives easily make the connection between their choices and the outcomes and can adapt their behavior to gain rewards and avoid punishment. Those who fail to make the link are either Unawares—people who, once given further information or clues, can re-evaluate and change their behavior—and Compulsives, i.e., people who still persist in making bad decisions despite suffering consequences.

Expanding the pool

This latest study builds on that earlier work, using a variation of the same experimental online game: After a few rounds, the researchers told all the subjects which planet was linked to which ship and also which ship triggered the point losses. "We never directly tell them what the best strategy is; we just reveal how each action leads to particular cues and 'attack' (the point-loss outcome)," Jean-Richard-dit-Bressel told Ars. "The reason being our studies have reliably shown all behavioral phenotypes, including Compulsives, are valuing cues and outcomes normally—and are totally aware of cue-attack relationships."

They also expanded the pool of participants beyond the Australian psychology students who were subjects in the 2023 study, sampling a general population from 24 countries of different ages, backgrounds, and experiences. And the researchers conducted six-month follow-ups in which subjects played the same game and were asked afterward whether they thought their choices and strategies were optimal.

The resulting phenotype breakdown was roughly similar to that of the 2023 study using just Australian students. About 26 percent were Sensitives, compared to 35 percent in the earlier study; 47 percent were Unawares, compared to 41 percent in 2023; and 27 percent were Compulsives, compared to 23 percent in the prior work. Those behavioral profiles remained unchanged even six months later. And the poor choices of the Compulsives could not be attributed simply to bad habits. The follow-up interviews showed that Compulsives were well aware of why they made their choices.

graphs showing the Cognitive-behavioral trajectories of behavioral phenotypes. Cognitive-behavioral trajectories of behavioral phenotypes. Credit: L. Zeng et al., 2025

"The thing they seemed to specifically struggle with is seeing the link between their actions and its consequences," said Jean-Richard-dit-Bressel. "Basically, lots of people (our Unaware and Compulsive phenotypes) don't readily learn how their actions are the problem. They fail to recognize their agency over things they are highly motivated to avoid. So we give them the piece of the puzzle they seemed to be missing. Correspondingly, simply telling people how their actions lead to negative outcomes completely changes the behavior of most poor avoiders (Unawares), but not all (Compulsives)."

The researchers admit it's a bit perplexing that so many Compulsives still persisted in making bad choices, even after receiving new information. Is it that Compulsives simply don't believe what the researchers have told them?

"There's maybe a little of that going on," said Jean-Richard-dit-Bressel. "We ask them which actions they thought led to attacks and how they value each action, and they do strongly update their beliefs/valuations after the information reveal but not as much as the Unawares. So, Compulsives are a little less on board about the relative values of actions than other phenotypes. But we've shown this still doesn't fully account for how poorly they continue to avoid."

Better interventions needed

That's something the scientists are keen to explore further. "We showed Compulsives are very aware of how they're behaving, and also think their behavior is optimal—even though it really wasn't," said Jean-Richard-Dit-Bressel. "This suggested a key failure point is between recognizing the relative values of actions and forming a corresponding behavioral strategy."

Compulsives, in other words, exhibit deficits in cognitive-behavioral integration. "It's like they're thinking, 'Yeah, sure, Action A is good, Action B is bad... instead of a 50-50 split, I'll do 60-40,'" said Jean-Richard-dit-Bressel. "They really should be going cold turkey and doing 100-0. An implication of the trajectory analysis we did is that no amount of action belief updating would get them to behave optimally. We need a way to improve how those beliefs translate to perceptions of what's optimal."

What might be the underlying cause of this persistent bad decision-making? "We don't know, but the fact most people have the same profile at retest suggests this is a kind of trait: a stable cognitive-behavioral tendency," said Jean-Richard-dit-Bressel. "It could be related to genetics, but we don't have the data for that. We know there are environmental factors that contribute to the Compulsive profile: It's significantly more likely to emerge if the Action→Punisher relationship is infrequent, i.e., people will be poor avoiders and ignore helpful information if punishment probability is low, even if the punishment is severe. But this would be a case of trait-x-environment interactions. My neuroscientist side would love to explore what's going on in the brain and map what contributes to adaptive vs maladaptive decision-making."

This could help drive more effective public health messaging, which is typically focused on providing factual information about the risks of compulsive behaviors, whether we're talking about smoking, drinking, eating disorders, or gambling, for instance. The results of this study clearly demonstrate that for Compulsives, information is not sufficient to change their self-sabotaging behavior. One of Jean-Richard-dit-Bressel's lab members is now investigating better interventions for different profiles of decision-making, particularly for Compulsives.

"We definitely haven't cracked the case yet," said Jean-Richard-dit-Bressel. "There's a body of work that says early over late information intervention might do the trick, but we've shown Compulsives in low probability punishment scenarios are impervious to early information. If the issue is they can't infer the winning strategy with Action→Punisher, maybe explicitly outlining the winning strategy will make more of a difference. Or maybe some potent combination of prompts. We have ideas, but the proof will be in the pudding."

Then again, "It could be the case that no amount/type of information will be enough to really sway 'that friend,' and that something far more involved would be needed," he said. "But most people will have a least some response to helpful information, so my suggestion in the absence of a full answer is to just be a good friend and give that friend the info/advice they seem to need to hear (again). It won't go the distance for everyone, but it's cheap and you'd be surprised at how many people need what seems obvious pointed out to them."

Nature Communications Psychology, 2025. DOI: 10.1038/s44271-025-00284-9 (About DOIs).

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sirshannon
11 days ago
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Wow.
sarcozona
4 days ago
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Epiphyte City
acdha
14 days ago
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Washington, DC
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Fun Detail in "The Terminator"

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"There's a storm coming in!"






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sirshannon
14 days ago
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Busted.
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Radar AI Training

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Marko Zivkovic (via Ric Ford, Reddit):

Apple announced its plans for a new opt-in Apple Intelligence training program. In essence, users can let Apple use content from their iPhone to train AI models. The training itself happens entirely on-device, and it incorporates a privacy-preserving method known as Differential Privacy.

The opt out seems to be via the Share iPhone & Watch Analytics button, which is the iOS equivalent of the Mac button that Mysk demonstrated Apple doesn’t actually honor.

In a social media post, developer Joachim outlined a new section of Apple's privacy notice in the Feedback application. When uploading an attachment as part of a bug report, such as a sysdiagnose file, users now need to give Apple consent to use the uploaded content for AI training.

Joachim Kurz:

After a long time, I filed another bug report using Feedback Assistant because the bug was bad enough that it’s worth the effort of writing it all down.

When uploading a sysdiagnose (or probably any other attachments) you get the usual privacy notice that there is likely a lot of private and other sensitive info in those log files. It’s not a great feeling but it is what it is with diagnostic data and I mostly trust the folks at Apple to treat it with respect and I trust the Logging system to redact the most serious bits.

However, when filing a feedback today a noticed a new addition to the privacy notice:

“By submitting, you […] agree that Apple may use your submission to [train] Apple Intelligence models and other machine learning models.”

WTF? No! I don’t want that. It’s extremely shitty behavior to a) even ask me this in this context where I entrust you with my sensitive data to help you fix your shit to b) hide it in the other privacy messaging stuff and to c) not give me any way to opt out except for not filing a bug report.

I could understand if the plan were for Apple to train some kind of internal AI model to help them triage bugs. Some developers might still have a problem with this because they don’t want their private data leaking out of the context of their particular bug. But when Apple says Apple Intelligence models that sure sounds like training the general models that will be available to the general public.

They probably have something in the terms of service that allows them to retroactively do this for previously submitted bugs, going back decades. Really, the only solution for keeping your data private is not to share data—even for internal use by the Privacy Company—that you don’t want to be shared. That is, only submit sysdiagnoses from a clean test Mac.

Joachim Kurz:

Also, there is a lot of sensitive information in a sysdiagnose. Taking it and throwing it into a big pile of data and compute and hoping something useful comes out of it is not treating my data with the respect it deserves.

On the topic of Radar, also see this thread by Max Seelemann:

Apple’s disrespect for the time and energy going into developer bug reports is making me sad. 🙁

Reported a performance issue with a sample app a couple of months ago. Of course, no feedback.

And now, Beta 2, they just ask if it’s still present and a sysdiagnose. They could have just launched the sample themselves and would have seen that NOTHING has changed. My guess is that no single developer at Apple has ever seen the issue and they just randomly ask about this out of procedure? Depressing.

Der Teilweise:

My model of the radar world is that they tag reports like “Finder icon position” or “… performance” and the devs add tags to their commits. Whenever a release contains a commit where the tags match, you automatically get those “please verify” mails.

Like “if we touch a part of the code that is closely related to a report, just ask the reporter if we fixed it as a side effect.”

I doubt this is the case because I’ve had bugs that did get fixed but where I never got this e-mail, even though really rough tagging would have made my bugs match. Or maybe some percentage of bugs just never get tagged.

Peter Steinberger:

The best is when they personally reach out via DM and then you make them an example and you NEVER hear back.

My favorite is when they do write back once and say that you can ask for updates on the bug, and then each year you ask for an update and never ever hear anything again.

Previously:

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sirshannon
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NEW APP: SYNTH ONE J6 (FREE & AMAZINGLY COOL!)

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&&&&&&

Introducing AudioKit Synth One J6, Your New Go-To Synth

Remember AudioKit Synth One? Millions of downloads, beloved by musicians and educators everywhere. The dedicated volunteers behind AudioKit have done it again. After nearly three years of hard work, we’re excited to share Synth One J6, a pro-quality synth app for iPhone and iPad with mobile AUv3 support. And yes, it’s totally free.

Synth One J6 is not just an app. It’s a creative instrument inspired by iconic analog synthesizers, built by people who believe music belongs to everyone.

Powered by ethical machine learning, Synth One J6 delivers lush pads, deep basses, classic keys, and vibrant leads that feel truly alive.

No ads. No subscriptions. No hidden fees. Just an exceptional instrument, freely given to inspire creativity everywhere.

We built Synth One J6 for people who are often left out. Many students and aspiring artists have mobile devices through schools or other programs, but those devices cannot install paid apps. We wanted to change that. With Synth One J6, anyone can explore, learn, and create music right away.

&&&&&&

The interface is intuitive and welcoming. Each function has a dedicated slider or knob, making it simple for beginners and deep enough for pros. You’ll find arpeggiators, sequencers, lush reverbs, tape delays, warm choruses, and over 400 inspiring presets created by talented sound designers worldwide.

Built by volunteers who deeply value music education and accessibility, this app ensures everyone, from classrooms to community programs, can start creating music instantly.

&&&&&&

In blind listening tests, Synth One J6 matched the sound quality of expensive desktop plugins, with many musicians even preferring its tone. But the real win is getting professional music tools into hands that have never had access before.

Please help us share it. Tell a teacher. Tell a student. Tell anyone who’s dreamed of experiencing a vintage synthesizer but lacked the opportunity.

Together, we can make sure that creativity is not limited by money, location, or background.

Because music belongs to everyone.

LEARN MORE:

Official Synth One J6 Webpage

Download:

Synth One J6 isn’t “freemium” or loaded with annoying in-app purchases. You download it. You play it. You create. End of story.

The post NEW APP: SYNTH ONE J6 (FREE & AMAZINGLY COOL!) first appeared on AudioKit Pro.
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New Screencast Series: Learn AudioKit (Beginner Friendly!)

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Ever wanted to dive into audio programming but didn’t know where to start?
Enter the newest NSScreencast series featuring our favorite audio framework: AudioKit!

Ben Scheirman has kicked off a new series that takes you from the very basics of audio all the way to building your own synths, ambient soundscapes, and even a multi-track audio engine. And the best part? You don’t need a PhD in coding, just a little curiosity and a working copy of Xcode (Free Download).

Here’s what’s covered so far:

Audio Fundamentals
Understand how sound works and what it means in code. No jargon, just the basics to get your brain in sync with your ears.

Getting Started with AudioKit
Learn how to install AudioKit and use it in your first project. A quick and painless start.

Build a Monophonic Synth
Create a simple synth using oscillators, mixers, faders, and reverb. A hands-on project you’ll actually want to show off.

Ambient Noise Designer
Blend pink, white, and brown noise. Add panning, reverb, and auto-sweep to create a chill audio space.

Multi Track Audio Engine
Build a basic engine with sync, mix, and mute features. It’s a great step toward more complex audio apps.

Each episode is short, clear, and packed with real code you can use.
Start watching at nsscreencast.com and bring your audio ideas to life.

See more AudioKit Tutorials here

Learn more about AudioKit code here: AudioKit.io

The post New Screencast Series: Learn AudioKit (Beginner Friendly!) first appeared on AudioKit Pro.
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sirshannon
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Using Claude with Coding Assistant in Xcode 26

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Xcode 26 introduces Coding Assistant, a new tool that helps you write code with the help of AI. It comes with built-in support for ChatGPT. But what's really nice is that Apple lets us add our own model providers, including Anthropic's Claude.

This means you can use Claude Sonnet 4 in Xcode, just like you would with the built-in ChatGPT, as shown in the video below.

Coding Assistant in Xcode 26 requires an M1 Mac running macOS 26 Tahoe.

Let's walk through how to add Claude as a model provider.

Step 1: Generate an Anthropic API Key

First, go to console.anthropic.com/settings/keys and generate an API key. Xcode will use this key to send requests to Claude. You can name the key anything. I chose "Xcode 26".

Screenshot showing Anthropic's interface for creating an API key.

Copy the API key to your clipboard and store it somewhere if needed. You will need it in a couple of seconds.

Screenshot showing Anthropic's interface for copying an API key to the clipboard.

Step 2: Add Anthropic as a Model Provider

Now we're ready to set things up in Xcode by following the steps below.

  1. Open Xcode's Settings.
  2. Go to IntelligenceAdd a Model Provider.
  3. Select Internet Hosted and enter the following:

URL: https://api.anthropic.com/
API key: Paste in the key you just generated in Anthropic's console.
API key header: x-api-key

It should look something like this:

Screenshot showing Xcode's Intelligence settings configured to be used with Anthropic.

You can now select Anthropic's models for use with Coding Assistant. You may need to restart Xcode before the model provider shows up.

Screenshot showing Xcode's model selector in Coding Assistant

That's it. Now you're ready to start using Claude to write Swift code directly inside Xcode.

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sirshannon
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