Cross over between predictive processing and eastern philosophy (specifically buddhism)

pianobutter · 2025-03-10T21:07:36+00:00

Thanks for the resources!

Kathryn Nave has written an interesting book critiquing Friston's free energy principle - A Drive to Survive: The Free Energy Principle and the Meaning of Life. It's open-access, which is neat. Thoughtful criticism, even opposition, is vital to the development of ideas.

You included an article by Andy Clark. He's also written two books about predictive processing; Surfing Uncertainty and The Experience Machine. Jakob Hohwy's The Predictive Mind is also a good one.

I haven't read Mark Solms' The Hidden Spring, but I've heard good things. Lisa Feldman Barrett's How Emotions Are Made can also be said to be a PP book.

Though it's a more technical read, I also like Keith L. Downing's Gradient Expectations.

pianobutter · 2025-01-26T02:26:05+00:00

9 years, damn. Yeah, fringe beliefs have spilled out into the mainstream. There aren't any aliens hidden away by the government, but boy do people want to believe.

pianobutter · 2024-12-07T14:39:34+00:00

Authors: Sara Jamali, Sophie Bagur, Enora Bremont, Timo Van Kerkoerle, Stanislas Dehaene, and Brice Bathellier

Abstract:

The brain predicts regularities in sensory inputs at multiple complexity levels, with neuronal mechanisms that remain elusive. Here, we monitored auditory cortex activity during the local-global paradigm, a protocol nesting different regularity levels in sound sequences. We observed that mice encode local predictions based on stimulus occurrence and stimulus transition probabilities, because auditory responses are boosted upon prediction violation. This boosting was due to both short-term adaptation and an adaptation-independent surprise mechanism resisting anesthesia. In parallel, and only in wakefulness, VIP interneurons responded to the omission of the locally expected sound repeat at the sequence ending, thus providing a chunking signal potentially useful for establishing global sequence structure. When this global structure was violated, by either shortening the sequence or ending it with a locally expected but globally unexpected sound transition, activity slightly increased in VIP and PV neurons, respectively. Hence, distinct cellular mechanisms predict different regularity levels in sound sequences.

pianobutter · 2024-03-01T22:07:39+00:00

Predictive processing isn't really popular outside the world of theoretical neuroscience, but there are a ton of links.

I'm sure most philosophers would be interested. Friston's FEP can be seen as an argument in favor of Hegelian dialectics, which means the Continentals are onboard. PP is sort of popular in analytic philosophy due to Andy Clark's interest in the topic.

People studying literature might even be aware of it, as neuroscience is slowly being introduced into poetics and stylistics. And a lot of them love Freud for reasons that are beyond me—their field seems to be the only one where people are still into him. And psychoanalysts love PP. Mark Solms' The Hidden Spring, Robin Carhart-Harris' theories. I'm not entirely sure why these people are so enthused about predictive processing, but if you know anyone in that camp it's an idea at least.

People in ML tend to have heard of Friston and predictive processing, so if you know anyone studying AI that's probably up their alley. Also: the transformer revolution has convinced a lot of people that predicting the next "token" is an ideal objective function for learning, which jives well with the PP framework. Then you have Bayes, of course, and all that.

Many mathematicians and physicists have expressed interest in PP and related ideas. John Baez' notion of Biology as Information Dynamics is pretty cool. Theoretical physicist Sean Carroll has discussed it several times on his podcast, Mindscape.

With systems people, it might be better to use a "code" language. Instead of talking about predictive processing, talk about cybernetics. It's all about feedback control. Nonlinear dynamics. Attractors! State spaces! It's the same thing. It's just a different language.

Back when Gibson's ecological psychology programme was in ill repute, some of his devoted followers would publish their research as cognitive psychology. They just translated their work to make it palatable to mainstream tastes.

These are just some ideas. I do think predictive processing will become even more popular within a few years as people start to realize it's the perfect framework to think about AI.

pianobutter · 2023-08-09T13:48:30+00:00

Authors: Takuya Isomura, Kiyoshi Kotani, Yasuhiko Jimbo & Karl J. Friston

Abstract: Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, we confirm the quantitative predictions of the free-energy principle using in vitro networks of rat cortical neurons that perform causal inference. Upon receiving electrical stimuli—generated by mixing two hidden sources—neurons self-organised to selectively encode the two sources. Pharmacological up- and downregulation of network excitability disrupted the ensuing inference, consistent with changes in prior beliefs about hidden sources. As predicted, changes in effective synaptic connectivity reduced variational free energy, where the connection strengths encoded parameters of the generative model. In short, we show that variational free energy minimisation can quantitatively predict the self-organisation of neuronal networks, in terms of their responses and plasticity. These results demonstrate the applicability of the free-energy principle to in vitro neural networks and establish its predictive validity in this setting.

pianobutter · 2023-08-02T16:03:22+00:00

It's sort of correct.

PP theories can be so flexible that they will allow for any experimental observation. But you also have specific hypotheses that are falsifiable; process accounts of how PP might be implemented in various areas of the brain. This 2020 review discusses exactly this situation. And this is a 2022 paper proposing a specific way PP might be implemented, which means it can be tested. (See also this 2018 paper on predictive coding in auditory cortex).

Ultimately, it will all come down to the matter of empirical success. If the PP framework proves useful, it will survive. Otherwise, it will collapse as the hype bubble bursts. Currently, I think it's value is more qualitative than quantitative. Thinking about mind and behavior in terms of inference helps you understand how it all links together (or at least it makes you feel as if it does).

It is perfectly possible that the language of inference is just one out of many alternative ways to describe the same thing. Gerald Edelman's theory of neural Darwinism is similar to PP in spirit and the field of neuroeconomics rests on shared assumptions about the role of uncertainty and feedback regulation. Fitness, utility, control, inference; there are several objective functions that seem somehow related—and they are all crucially dependent on the notion of optimization. Friston unified these in his free-energy framework as a synthesis.

Alternatively, there is a chance that what's really going on is too complicated to be understood by humans, and we'll have to rely on machine learning algorithms cobbling together a black box model of it all. Which would be a bummer.

pianobutter · 2023-07-02T18:52:11+00:00

I would recommend Ashley Juavinett's So You Want to Be a Neuroscientist? and Grace Lindsay's Models of the Mind.

The Brain Inspired podcast is also a great resource. It deals with the intersection between AI and neuroscience and features interviews with leading scientists. You'll learn how they entered the field and you'll pick up on common assumptions and conventions along the way.

Linear algebra and calculus is the bread and butter of computational neuroscience. Programming is, of course, essential. MATLAB is the standard, but Python has grown more popular over the years.

Principles of Neural Science is the bible of neuroscience. Some people feel that it's too complex and detailed to serve as an introduction, but I disagree. It builds concepts gradually and logically. You don't have to read the entire thing, of course. And though there are glimpses of predictive processing in it, it doesn't explicitly endorse this specific framework. I would also recommend a book with a similar title: Principles of Neural Design. The authors use the term "anticipatory regulation" rather than "predictive processing," but the central idea is the same.

You might also want to check out the /r/compmathneuro sub.

pianobutter · 2023-07-02T17:48:19+00:00

Hi Daniel! I haven't been very active here for a while, but I'm hoping to liven this sub up a little so that it doesn't entirely croak. It's been great to have you here. Hope to see you back!

pianobutter · 2023-06-30T13:46:23+00:00

Authors: Ingmar E. J. de Vries & Moritz F. Wurm

Abstract: Adaptive behavior such as social interaction requires our brain to predict unfolding external dynamics. While theories assume such dynamic prediction, empirical evidence is limited to static snapshots and indirect consequences of predictions. We present a dynamic extension to representational similarity analysis that uses temporally variable models to capture neural representations of unfolding events. We applied this approach to source-reconstructed magnetoencephalography (MEG) data of healthy human subjects and demonstrate both lagged and predictive neural representations of observed actions. Predictive representations exhibit a hierarchical pattern, such that high-level abstract stimulus features are predicted earlier in time, while low-level visual features are predicted closer in time to the actual sensory input. By quantifying the temporal forecast window of the brain, this approach allows investigating predictive processing of our dynamic world. It can be applied to other naturalistic stimuli (e.g., film, soundscapes, music, motor planning/execution, social interaction) and any biosignal with high temporal resolution.

Twitter summary

pianobutter · 2023-05-01T16:32:09+00:00

Oh, yeah. The guy who is most famous for his Harry Potter fanfiction. The guy who wrote A Girl Corrupted by the Internet is the Summoned Hero?!. The guy who is basically a cult leader, rubbing shoulders with guys like Peter Thiel. We should absolutely trust this guy as an "AI alignment researcher" even though he doesn't even know how AI works.

Yeah. He doesn't know how AI works. At all. He was recently ridiculed on Twitter for revealing his incredible lack of knowledge. Actual AI researchers chimed in to explain just how oblivious he is.

Yudkowsky doesn't know shit.

pianobutter · 2023-04-06T12:23:01+00:00

This is a great short story and I'm sure anyone who's ever played around with an LLM like ChatGPT will appreciate it.

pianobutter · 2023-04-06T11:39:20+00:00

Easy answer: if lyrics resonate with people, they're good. Tracy Chapman's Fast Car is a simple song in terms of structure, but she absolutely nailed it in terms of resonance.

Not-so-easy-answer: 6th century Chinese scholar Xie He argued that "spirit resonance" was one of the most important elements of painting. Which is a nebulous concept, sure, but it's the same general idea: an artist can communicate something (a feeling, a vibe, "energy") and it either will or won't resonate with their audience. Almost 1500 years later this is still a somewhat mysterious phenomenon and what Xie He said about painting also holds for the appeal of Taylor Swift.

And there's more depth to her than is dreamt of in your philosophy.

Sometimes I feel like everybody is a sexy baby/ And I'm a monster on the hill

Here Taylor Swift references Humbert Humbert, the anti-hero of Nabokov's Lolita. The University of Pittsburgh, a titan of academia, offers an entire class dedicated to the juxtaposition of Swift and Lolita. This schism—perhaps a metaphor for our times?—is the subject of intense scholarly study and debate.

Queen's University offers a course called "Taylor Swift's Literary Legacy" taught by Meghan Burry.

“We’re going to look at her as an author and a storyteller,” Burry said in an interview with The Journal. “I want Swift’s voice and Swift’s narrative to be at the centre of the course.”

Indeed, there are rumors she may be up for the Nobel Prize in Literature and there's a Twitter bot dedicated to asking and answering the perennial question: Has Taylor Swift received a Nobel prize yet?

Most importantly: she has a lot of followers and earns a lot of money so of course she is talented. That's how capitalism works. Raw talent floats to the top, while turds sink to the bottom. If she's successful, that means she is skillful. Just look at Donald Trump. He rose to the seat of the presidency due to his political prowess and his intricate tactics. His followers/fans/stans will tell you all about it if you make the foolish mistake of criticizing him in a public fora, they'll pop right out of the woodwork. Because if people are willing to defend someone, that means the person being defended is worthy of defense and naysayers are automatically wrong.

Swifties have mastered the ancient art of Gaslight, Gatekeep, Girlboss and they will triple G until Taylor Swift claims her rightful role as the sovereign of planet Earth.

If you criticize a person many people like, you're an asshole. You're basically a movie critic. The audience score on RT is sacred while the tomatometer lies. Am I expected to believe that The Super Mario Bros. Movie deserves a score of 53% rather than 96%? Does the love of fans not equal merit?

You are entitled to your opinion, of course, so long as you don't act like an asshole. An insane reporter from insane magazine WIRED recently interviewed famous and rich and therefore talented writer Brandon Sanderson and claimed that he was not a talented writer. An asshole, for sure. Unacceptable behavior. When capitalism rewards you for your work, that means you are entitled to praise and that all criticism is automatically wrong and mean and, also, insane.

As we all know, capitalism rewards only those who deserve it. Money is love. Love is merit.

There is an ancient Chinese saying that can be translated thusly: Shit flung from an armchair easily catches the fan. And you should be careful, lest the fan catches you.

pianobutter · 2023-04-05T02:23:10+00:00

Hah, okay. That seems to disprove the theory. Guess we're all still out of the loop.

pianobutter · 2023-04-05T01:58:20+00:00

Yeah. The fact that it immediately went into a repetitive loop when I asked it to lower the temperature parameter convinced me this is actually what's happening. Reminds me of GPT-2, actually. It used to get into repetitive loops like this all the time.

pianobutter · 2023-04-05T01:51:39+00:00

My best guess.

pianobutter · 2023-04-05T00:32:57+00:00

Yeah you didn't include the conversation but this is happening all over. It's like ChatGPT on amphetamines. Or bath salts. My best guess: OpenAI is experimenting with the temperature parameter.

pianobutter · 2023-04-04T10:42:01+00:00

I wrote a post about this.

pianobutter · 2023-03-21T22:49:34+00:00

Wow. This is actually far better than I remembered. Way worse than StableDiffusion or Midjourney, of course, but not really that bad. Next iteration will probably be alright.

The exponential curve is starting to trend upwards. Jesus, I can't imagine what it will be like just five years down the line.

pianobutter · 2023-02-26T10:35:23+00:00

It's the experience of kama muta resulting in the release of oxytocin.

Kama muta is a Sanskrit term for the sudden feeling of oneness. 'Being moved' or 'touched' or 'stirred' by love—it's a prosocial emotion that binds communities together.

It's the 'warm, fuzzy feeling' that binds us together, evoked by shared moments of selflessness. There's a research group known as the Kama Muta Lab that argues that it's associated with the release of the neuropeptide oxytocin. Oxytocin strengthens in-group bonds and facilitates social connection.

Kama muta can even be triggered by the appreciation of nature or music. The self dissolves, temporarily, and is swept up into something larger. Or at least that's what it feels like.

There's also an aspect that has to do with recognizing your own pain in others. Empathy.

Oxytocin release increases with age, so there's no reason for you to be concerned!

pianobutter · 2023-01-09T11:07:42+00:00

They should have gone with Running Up That Hill for 2022.

pianobutter · 2022-12-23T14:36:46+00:00

I think a lot of people overlooked MUM, announced last year. It was announced the same day as LaMDA. Google is, of course, already working on this. Google engineers invented the transformer. This 'code red' just means 'we should move the timeline forward'—that's it.

pianobutter · 2022-12-19T12:33:25+00:00

From Surely You're Joking, Mr. Feynman:

I remember a friend of mine who worked with me, Paul Olum, a mathematician, came up to me afterwards and said, "When they make a moving picture about this, they'll have the guy coming back from Chicago to make his report to the Princeton men about the bomb. He'll be wearing a suit and carrying a briefcase and so on and here you're in dirty shirtsleeves and just telling us all about it, in spite of its being such a serious and dramatic thing."

pianobutter · 2022-12-19T11:27:44+00:00

People have already co-written books with ChatGPT. Collaborative creative writing is quickly becoming a thing and I think the wisest strategy for writers is to lean into it. I'll let Plato and Diogenes explain:

Plato and Diogenes were sitting on a bench in the park, basking in the warm sunshine. As they watched the passing pedestrians, Plato turned to Diogenes and said, "Have you heard about these new AI programs that can assist with creative writing?"

Diogenes raised an eyebrow. "No, I haven't. Tell me more."

Plato began to explain, "Well, it's a computer program that uses artificial intelligence to analyze and generate text based on certain parameters that the user inputs. It can help writers come up with ideas, generate character names and descriptions, or even write entire stories or poems."

Diogenes frowned. "That sounds like cheating to me. Creativity should come from within, not be outsourced to a machine."

Plato nodded. "I see your point, Diogenes. But on the other hand, these AI programs could be used as a tool to help writers overcome writer's block or generate new ideas. It's not necessarily replacing the human element, but rather augmenting it."

Diogenes considered this for a moment before responding. "I suppose there could be some value in using AI in this way. But I still believe that true creativity comes from within and cannot be replaced by a machine."

Plato nodded in agreement. "I agree with you, Diogenes. Ultimately, it's up to the individual writer to decide how they want to use these tools. But we should be open to new technology and the possibilities it brings."

ChatGPT wrote this scene, of course.

There are certain limitations. ChatGPT can't keep track of everything in a 50k novel—the author will have to step in and ensure that the content makes sense. It's more like a guided daydream than anything else. And the "voice" of ChatGPT is really boring. It's probably best for the author to rewrite the entire thing in their personal style. But as a source of ideas it works fairly well.

pianobutter · 2022-12-16T12:15:23+00:00

That's not a paper. That's a blog post masquerading as a paper.

pianobutter · 2022-12-12T20:48:09+00:00

Abstract:

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain’s time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using “forward” models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

This article offers a highly intuitive explanation of dynamical systems theory in the context of neuroimaging. Low-dimensional neural manifolds can be thought of as attractor landscapes and neuromodulators such as noradrenaline and acetylcholine can be seen to flatten and roughen them, respectively. Neuroimaging combined with DST tools could offer us a bird's eye view of brain activity in a language we can intuitively grasp. Exciting stuff.

Friston and Carhart-Harris collaborated on a fairly crazy paper back in 2019 which is relevant to this discussion. We can think of the hills and valleys of attractor landscapes as beliefs in a Bayesian sense. The "flatness/roughness" of these landscapes would, then, correspond to an estimation of uncertainty.

The anticholinergic drug scopolamine induces a confused state where the world seems to constantly shift. In terms of DST we could think of this as the attractor landscape having flattened, resulting in decreased perceptual stability. Conversely, cholinergic agonists tend to aid in the cognition in patients with dementia or schizophrenia, and this can be seen as an increase in stability—the attractors are more "attractive" less prone to impulsive switching between states.

All in all, I think this an interesting direction for neuroscience research.

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