Mastodawn

Dawn Ahukanna Sep 9, 2023

@NicoleCRust @awaisaftab @summerfieldlab @knutson_brain @tyrell_turing @Neurograce @eikofried

You had me at “The challenge of escaping reductionism.” - 😉

Blake Richards Sep 11, 2023

@NicoleCRust @awaisaftab @summerfieldlab @knutson_brain @Neurograce @eikofried

Fascinating discussion! Thanks for sharing this.

But, I can't help myself, I have to engage in my usual refrain here (sorry, lol), since it comes up in the second paragraph:

Brains are *literally* computers and information processing devices, it's not a metaphor!!! 🙂

Dan Goodman Sep 11, 2023

@tyrell_turing @NicoleCRust @awaisaftab @summerfieldlab @knutson_brain @Neurograce @eikofried is this an attempt to break mastodon? 😂

bk Sep 11, 2023

@neuralreckoning @tyrell_turing @NicoleCRust @awaisaftab @summerfieldlab @Neurograce @eikofried Almost fell for it…

@knutson_brain @neuralreckoning @tyrell_turing @awaisaftab @summerfieldlab @Neurograce @eikofried

Nicole Rust Sep 12, 2023

I appreciate the collegial pushback @tyrell_turing and your excellent piece on the topic (here
https://www.frontiersin.org/articles/10.3389/fcomp.2022.810358/full). To summarize your point: whether the computer is a metaphor for the brain or not depends on how you define "computer".

Definition 1 (it's not a metaphor):

"based on the usage of the word “computer” in computer science, a computer is merely some physical machinery that can in theory compute any computable function. According to this definition the brain is literally a computer; there is no metaphor."

Definition 2 (it is a metaphor):

computers and computation as being necessarily sequential, or discrete, or restricted to passive processing of a stream of inputs using a step-by-step program ... we can say not only that brains are not computers, we can also say that computers are poor metaphors for brains, since the manner in which they operate is radically different from how brains operate.
(Van Gelder, 1998; Cisek, 1999; Brette, 2018, 2019; Cobb, 2020).

I was indeed using the second definition of computer that your article employs; the one that I see as most pervasive in brain research today (including my own work). So, as you say, it IS a metaphor 😉. But your point is appreciated and I will do better to clarify going forward.

Why did I use definition 2? As we both know, definition 2 has been tremendously successful in driving brain research toward some incredible discoveries (in eg vision and memory) and interactions between that arm of brain research and AI/ML have bene tremendously impactful.

I am thus concerned that those incredible successes will lead researchers to presume that this is a good approach to understanding everything the brain does. However, I strongly believe that deviations in thinking about the brain away from definition 2 are crucial for impactful progress in thinking about some of the brain functions related to dysfunction, such as affect/emotion.

For instance:

When thinking about emotions (like mood and anxiety), emphasizing the brain as an information processor that computes fails to capture the notion that we don't just compute/predict what's bad and good in the world, we are drawn toward good and repelled from bad with these very strong experiences we call feelings like craving, disgust, joy and fear (and those don't seem to be just tacked on at the end after goodness/badness computation; it's all wrapped up together).

Likewise, thinking about the brain as the type of thing that processes input to generate behaviors fails to capture that the input we receive depends on what we do and thus input and output operate in a continuous feedback loop (ala control systems/active sensing; approach good/hide from bad).

When some researchers deviate from definition 2, it leads them down intriguing paths. To quote Paul Cisek:" You realize that neither the term ‘decision-making’ nor the term ‘attention’ actually corresponds to a thing in the brain. Instead, there are certain very pragmatic circuits in the brain, and they do certain things like ‘approach’ or ‘avoid.’ … Some of those things are going to look a bit like attention."

These are the paths I very much believe we need to traverse for this arm of brain research to be impactful for brain disorders.

The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics

It is commonly assumed that usage of the word “computer” in the brain sciences reflects a metaphor. However, there is no single definition of the word “computer” in use. In fact, based on the usage of the word “computer” in computer science, a computer is merely some physical machinery that can in theory compute any computable function. According to this definition the brain is literally a computer; there is no metaphor. But, this deviates from how the word “computer” is used in other academic disciplines. According to the definition used outside of computer science, “computers” are human-made devices that engage in sequential processing of inputs to produce outputs. According to this definition, brains are not computers, and arguably, computers serve as a weak metaphor for brains. Thus, we argue that the recurring brain-computer metaphor debate is actually just a semantic disagreement, because brains are either literally computers or clearly not very much like computers at all, depending on one's definitions. We propose that the best path forward is simply to put the debate to rest, and instead, have researchers be clear about which definition they are using in their work. In some circumstances, one can use the definition from computer science and simply ask, what type of computer is the brain? In other circumstances, it is important to use the other definition, and to clarify the ways in which our brains are radically different from the laptops, smartphones, and servers ...

Frontiers

@NicoleCRust @knutson_brain @neuralreckoning @awaisaftab @summerfieldlab @Neurograce @eikofried

Thanks for the clarification! To clarify a bit on my own, my reply was more of a call-back joke to the OG days on Twitter (per @neuralreckoning ).

But, I take your point well, and agree, it is important for people to realise that brains don't do sequential, discrete, passive processing of a stream of inputs using a step-by-step program. And so, in that sense, it's a metaphor (and a poor one).

@NicoleCRust @knutson_brain @neuralreckoning @awaisaftab @summerfieldlab @Neurograce @eikofried

Also, I really like your point that it helps people to get people out of thinking that things like "attention" and "decision making" are discrete pieces of a program in our heads. 🙂

Nicole Rust Sep 12, 2023

@tyrell_turing @knutson_brain @neuralreckoning @awaisaftab @summerfieldlab @Neurograce @eikofried
Look at that @neuralreckoning - it's all good over here on the furry elephant where we're figuring it all out. I bet they can't say the same over on Threads 😊.

madhadron Sep 12, 2023

@NicoleCRust @knutson_brain @neuralreckoning @tyrell_turing @awaisaftab @summerfieldlab @Neurograce @eikofried I don't think definition 1 is correct, though. Computable functions describe a process taking an input state into a desired output state. The theory doesn't describe online data processing which is what the brain does.

@madhadron @NicoleCRust

That's an incorrect (though very common!) interpretation of the ideas from computer science.

The definition of a computable function is merely a function that a Turing machine will halt on with the correct answer. But, if you want, you could run such a function online with some system.

For example, I could have a system constantly in real time multiplying the current temperature outside by 4. That doesn't make multiplying by 4 a non-computable function.

madhadron Sep 12, 2023

@tyrell_turing @NicoleCRust I'm afraid this will get long, so (1/N). This is something we dealt with a lot in the functional programming community.

Functions act on data. Data is fixed and fully defined at the start. Online systems are described by cofunctions acting on codata.

You're asserting that every cofunction has a corresponding function under some kind of mapping. That is not true.

madhadron Sep 12, 2023

@tyrell_turing @NicoleCRust (2/N) Counterexample: a cofunction that receives some other cofunction in its codata and replaces itself with that. There is no corresponding function.

Given that brains learn behavior from the outside world, I am inclined to think that functions are insufficient.

It's possible that cofunctions over codata are a useful way to model brain activity, but computable functions aren't the right tool.

@NicoleCRust @knutson_brain @neuralreckoning @tyrell_turing @awaisaftab @summerfieldlab @Neurograce @eikofried

Pine Sep 12, 2023

This is an excellent discussion. One thing is see is a confusion between computing and information processing. info processing happens at all levels of biology, "computers" only certain levels. I can't do it justice in brief here, so i'll just reference:

Intelligence as Information Processing: Brains, Swarms, and Computers

https://www.frontiersin.org/articles/10.3389/fevo.2021.755981/full

Intelligence as Information Processing: Brains, Swarms, and Computers

There is no agreed definition of intelligence, so it is problematic to simply ask whether brains, swarms, computers, or other systems are intelligent or not. To compare the potential intelligence exhibited by different cognitive systems, I use the common approach used by artificial intelligence and artificial life: Instead of studying the substrate of systems, let us focus on their organization. This organization can be measured with information. Thus, I apply an informationist epistemology to describe cognitive systems, including brains and computers. This allows me to frame the usefulness and limitations of the brain-computer analogy in different contexts. I also use this perspective to discuss the evolution and ecology of intelligence.

Frontiers

bk Sep 12, 2023

@pinecone @NicoleCRust @neuralreckoning @tyrell_turing @awaisaftab @summerfieldlab @Neurograce @eikofried
What is information? (*runs away*)

Awais Aftab Sep 12, 2023

@knutson_brain @pinecone @NicoleCRust @neuralreckoning @tyrell_turing @summerfieldlab @Neurograce @eikofried I’d love to read a good analysis of the concept of “information” as it pertains to neuroscience!

@awaisaftab @knutson_brain @pinecone @NicoleCRust @neuralreckoning @summerfieldlab @Neurograce @eikofried

I personally am happy to apply the concept of information from information theory directly in neuroscience!

In which case, it simply means anything that reduces uncertainty about some value.

@tyrell_turing @awaisaftab @pinecone @[email protected]@[email protected] @summerfieldlab @Neurograce @eikofried

bk Sep 12, 2023

Whose value?

@knutson_brain @tyrell_turing @awaisaftab @NicoleCRust @summerfieldlab @Neurograce @eikofried

Pine Sep 12, 2023

@axoaxonic the Olimpia Lombardi article is paywalled. But she mentions types of info - semantic, perspective, and syntactic. There is also the notion of Shannon vs semantic information. Semantics gets into encoding. Also, weirdly enough Howard Pattee's "epistemic cut" idea relates information to biology which gives a solid grounding for another perspective. This is all very heady stuff.

Holly A. Gultiano Sep 13, 2023

@pinecone @knutson_brain @tyrell_turing @awaisaftab @NicoleCRust @summerfieldlab @Neurograce @eikofried At least one of her talks on the subj is recorded and on YouTube for anyone who can't get around the paywall https://youtu.be/tzypga2ACTU
I love heady stuff and probably chose the right field for that. I'll check out your ref too

Interpreting the concept of information | Prof. Olimpia Lombardi

YouTube

@pinecone @knutson_brain @awaisaftab @NicoleCRust @summerfieldlab @Neurograce @eikofried @axoaxonic

These are all interesting ideas, but I don't think they are required for simply applying info theory to neuroscience.

Holly A. Gultiano Sep 13, 2023

@tyrell_turing @pinecone @knutson_brain @awaisaftab @NicoleCRust @summerfieldlab @Neurograce @eikofried In the spirit of non-reductionism, I was going off on a tangent. There are lots of straightforward papers and books about information theory applied to neuroscience -- I've found about 22 so far and am starting to dig through them -- but understanding the hisorical context and motivations of it and deconstructing the definition of information felt to be of epistemological value.

These conversations end up coming down to preferences and biases: do you like thinking through a computational lens or not? Someone who does might point out how neurons are fundamentally universal Turing machines and someone who doesn't might not think that's relevant (i.e., to psychology and behavior, or neurology/medicine). I felt impelled to zoom out a bit

@tyrell_turing @knutson_brain @awaisaftab @NicoleCRust @summerfieldlab @Neurograce @eikofried @axoaxonic

Pine Sep 14, 2023

There may be something more required for Shannon information theory to apply to neuroscience. Shannon information only applies to the transmission of messages. Shannon acknowledged it does not address transmission of meaning, semantic information. So, somewhere along the line neuroscience has to explain how meaning is transmitted. A general theory of info would help in that regard.

@knutson_brain @awaisaftab @pinecone @NicoleCRust @summerfieldlab @Neurograce @eikofried

Any value: x

@tyrell_turing @awaisaftab @knutson_brain @NicoleCRust @neuralreckoning @summerfieldlab @Neurograce @eikofried

Pine Sep 13, 2023

You said info is what reduces uncertainty of a value, but that's a stochastic perspective. More generally Information "represents" a value. Information is compressed representation, so to speak. Information is about copying (representation) and transmitting configuration states of the world. It having to do with "state configurations" sets it apart from physical laws.

Holly A. Gultiano Sep 12, 2023

@knutson_brain @pinecone @NicoleCRust @neuralreckoning @tyrell_turing @awaisaftab @summerfieldlab @Neurograce @eikofried Olimpia Lombardi has a lot of really intriguing work on that question, including this one with a title exactly the same as your post's question https://philpapers.org/rec/LOMWII

@awaisaftab
She also has some work related to neuroscience but specifically embedded in the Integrated Information Theory of consciousness. For neuroscience in general, maybe Shun'ichi Amari's earlier work on neural networks and information geometry would give a picture of how information theory got mixed in with neuroscience

Olimpia Lombardi, What is information? - PhilPapers

The main aim of this work is to contribute tothe elucidation of the concept of informationby comparing three different views about thismatter: the view of Fred Dretske's semantictheory of information, ...

CJ Stevens - Metaphysiology Sep 13, 2023

@tyrell_turing @NicoleCRust @awaisaftab @summerfieldlab @knutson_brain @Neurograce @eikofried
Is that an "are" of strict identity? Or do you believe brains also have functions outside of computing/information processing..?

@WorldImagining @NicoleCRust @awaisaftab @summerfieldlab @knutson_brain @Neurograce @eikofried

Strict identity - if we're using the broad definitions of computing and info processing from computer science, anything else implies magic/spirit.

Yogi Jaeger Sep 13, 2023

Cognition is not computation. It is an elaboration of organismic agency, which is not of an algorithmic nature. There is nothing magical about that.

You're fundamentally misinterpreting Turing's theory of computation, which was always about the human act of computing, not about the brain or the world. Turing would turn in his grave if he would hear you.

Some historical context: https://plato.stanford.edu/entries/church-turing.

This may also help: https://arxiv.org/abs/2307.07515.
Longer paper coming soon!

The Church-Turing Thesis (Stanford Encyclopedia of Philosophy)

Yogi Jaeger Sep 13, 2023

Computationalism may be useful as a methodological assumption. It is totally nuts (& philosophically founded on absolutely nothing but hot air) if you take it as your worldview.

Same applies to reductionism in general, by the way. Good method (within its limits), crap worldview, and a disaster if you confuse the two...

Albert Cardona Sep 13, 2023

@yoginho @tyrell_turing

On that topic: I am reminded of this paper by Roth et al. 2007 on "The self-construction and-repair of a foraging organism by explicitly specified development from a single cell" https://direct.mit.edu/artl/article-abstract/13/4/347/2575 (Free PDF here: https://direct.mit.edu/artl/article-pdf/13/4/347/1662436/artl.2007.13.4.347.pdf)

The authors describe and implement a self-constructing virtual organism capable of Braitenberg vehicle-like properties and which can self-repair in response to damage. The properties of the damaged organism are quite interesting, in that its performance as a vehicle degrades gracefully rather than catastrophically.

#neuroscience #ArtificialLife #BraitenbergVehicle

The Self-Construction and -Repair of a Foraging Organism by Explicitly Specified Development from a Single Cell

Abstract. As man-made systems become more complex and autonomous, there is a growing need for novel engineering methods that offer self-construction, adaptation to the environment, and self-repair. In a step towards developing such methods, we demonstrate how a simple model multicellular organism can assemble itself by replication from a single cell and finally express a fundamental behavior: foraging. Previous studies have employed evolutionary approaches to this problem. Instead, we aim at explicit design of self-constructing and -repairing systems by hierarchical specification of elementary intracellular mechanisms via a kind of genetic code. The interplay between individual cells and the gradually increasing self-created complexity of the local structure that surrounds them causes the serial unfolding of the final functional organism. The developed structure continuously feeds back to the development process, and so the system is also capable of self-repair.

MIT Press

@albertcardona @tyrell_turing

Yogi Jaeger Sep 13, 2023

Interesting paper. But it's a computational model, so not really self-manufacturing & capable of open-ended evolution...

Then again, I am definitely aspiring to degrading gracefully, rather than catastrophically! That's a rather poetic abstract for an engineering paper ;-)

Albert Cardona Sep 13, 2023

@yoginho @tyrell_turing

Rodney Douglas has always had a poetic, trascendent touch. Was great working alongside him back in 2005 for a brief while, when developing the #TrakEM2 software in Zurich.

Holly A. Gultiano Sep 13, 2023

@yoginho @tyrell_turing Computing being a subset of what people can do, as in the human computers that the machines were made to emulate them, being turned around to apply to everything their brains do feels a little tautological to me, even if it's effectively true and/or pragmatically useful.

BTW, your second seems to lead to a paper on applying deep learning to covid research, is that what you meant to post?

@axoaxonic There was a typo in the link. It's fixed now!

Respectfully, I disagree. I don't think you're accurately summarising computability theory, nor the implications for neuroscience.

As Yoshua Bengio once said to someone at a workshop I was at (paraphrasing):

"Computation just means physics. Saying the brain is a computer is just a way of saying that it is a physical device. I don't even know what a non-computational theory of the brain means, unless you're talking about magic."

Remember, as the very article from the Philosophy Encyclopedia describes, the original goal of both Turing and Church was simply to formalise the idea of an "effective method", i.e. a mechanical way of solving a problem.

If the Church-Turing thesis holds, then any problem that a mechanical (i.e. purely physical) system can solve represents a computable function, and the object implementing it is engaged in a computation.

Did you know that this argument only came up with Deutsch and Lloyd in the 1980/90s? Before that, nobody (including Church & Turing) ever thought that it would be reasonable to apply the term "computation" to physics.

So, no: it's you, not me who's misreading Turing. Effective computation means literally "something a human being can do by rote" (i.e. calculating, scheduling, optimizing). In Church & Turing's 30s papers, a computer is a (usually underpaid, female) human.

Church & Turing, as long as they lived, were fiercely opposed to applying the theory of computation to the brain or physics.

Computation is a tiny fraction of what our human brain does, and is not at all what the brain evolved for.

Is physics computation? No! You fail to see the difference between simulating a mechanism (a model of the world) and the claim that the world *is* a computer. The latter implies that physical mechanisms have symbolic content, which is absurd.

Plus (and that is the argument in my upcoming paper): organisms use their brains to deal with a large world, in which most problems are not formally defined (hence not computable), Before you can solve a problem using computation, you need to formalize it. This is called relevance realization. This process cannot itself be formalized. This is why how we come to know the world is fundamentally different from how algorithms solve problems.

Basically, the category mistake you make (it's called the equivalence fallacy) is that you mistake the map for the territory, your model for the real world.

That's what computationalism is. Since the 1980s (no earlier) we've slid into the deluded opinion (esp. in the fields of cognitive neuroscience & AI) that the world is a computer.

It's a dangerous view, that leads people like Bengio & many others to take on really foolish views about AGI.

Ultimately, it is computationalism (and its other reductionist-mechanistic relatives) that are causing our delusion that we can control and manipulate a predictable world.

I may sound a little hyperbolic, but the kind of worldview you are propagating here is likely to kill us all, unless we learn to realize the world for what it is, large, ill-defined, fundamentally unpredictable & beyond our grasp.

Computationalism is delusion of grandeur. The ultimate human foolishness.

P.S. I find it really difficult to discuss this with computationalists.

Since you live inside your model, and think there is nothing outside (see also your Bengio quote), you are completely blind to what is obviously quite a different reality your live in.

Computationalism is a comforting illusion for those who like their world tidy & predictable. They tend to fiercely refuse to embrace the fact that there is so much beyond mechanism in the world.

There is nothing mystical or obscure about physical processes that are not mechanisms. If organism can realize relevance, they cannot be completely captured by algorithmic computation, without breaking any known laws of physics.

That kind of argument is already published. See Rosen's work (e.g. "Life Itself"), Deacon's "Incomplete Nature," or my own "Fourth Perspective" on evolution: https://osf.io/2g7fh.

Blake Richards Sep 14, 2023

I don't really want to engage in an extended debate here, but I will just note this:

Turing and others very much so connected humans being able to do some calculation by rote to the ability to create a mechanical device to do that function.

So, there was always a direct connection to physics/mechanics, because the implication was always that if an effective method existed, then a physical/mechanical system could in principle implement the function for us.

Nobody doubts that you can mechanicize rote human behavior. In fact, that's exactly the point of a universal Turing machine. It is designed to implement any process that can be machanicized that way.

How you get from that to "the brain or the world are a computer/mechanism" is mysterious to me. And it was to Turing and Church who always resisted such an unwarranted extrapolation.

No extended discussion needed, really. There is simply nothing that supports your worldview...

Nicole Rust Sep 14, 2023

@yoginho @tyrell_turing

@neurobuzz ⬆️

Ben de Bivort Sep 14, 2023

@yoginho @tyrell_turing This thread is a fun read, but I wonder if you are both working from the same definition of computing to ask if cognition or physical processes are computation?

If you each were to independently write down definitions of computation, how operationally similar would they be?

If you have very different definitions, it's a disagreement of assumptions, not one that can be resolved through evidence.

cc @NicoleCRust re: my comment in your thread on jargon.

Ben Kanter Sep 14, 2023

@debivort @yoginho @tyrell_turing @NicoleCRust This thread, while educational to me, is the opposite of fun. I'm sure @tyrell_turing is used to it for having provocative opinions, but this style of academic attack turns many people off from engaging in discussions in the first place. If you find someone's take "absurd" why not approach it with curiosity and good faith instead? @NicoleCRust is a great example to follow - often disagrees, never attacks.

Ben de Bivort Sep 14, 2023

@beneuroscience @yoginho @tyrell_turing @NicoleCRust True! Much of what I meant by "fun" was "spicy."

@beneuroscience I've spent a whole life engaging reductionists & computationalists with curiosity. So now I can call them out in good faith for the utter bullshit they are heaping on humanity. Computationalism is an existential threat. I'm really tired playing Mr. Nice Guy with people who literally live in their model of the world, while screwing up the real world in the process. It may be unpleasant, but certainly not bad faith. Deal with it.

@beneuroscience One more thing: it'd be nice if at least some AI nerds & computationalist neuroscience people would engage with the kinds of criticisms they get from me (and others with similar arguments) in good faith and with an open mind. They are the mainstream in power, after all, so should be comfortable with a bit of critique. All I get is a wall of silence (see @tyrell_turing's cop-out above, for example) & some dismissive slogans. And yet, you won't see me crying in a corner.

Ben Kanter Sep 14, 2023

@yoginho @tyrell_turing If you sincerely wanted people with differing views to engage, you'd consider the real possibility that you may be wrong. Or at the very least that there is value in other ways of thinking. Otherwise what's the point of arguing about it?

@beneuroscience Hmm. Yes. I'm here because I was longing to get advice on epistemic humility and communication strategy from you. Thank you very much.

Do you have anything to say about the content of the argument? If not, why do you think you need to weigh in?

@tyrell_turing can defend himself, if he wants to, and he surely does know how provocations work...

@yoginho @beneuroscience @tyrell_turing

Nicole Rust Sep 14, 2023

I understand your frustration @yoginho. It's clear that you regard the stakes on this as exceedingly high and you're over being patient when it leads to individuals ultimately dodging your criticisms.

But even when we are frustrated, let's punch up, not down.

(For what it's worth, I've never heard you comment on these issues before and I'm very much listening).

http://www.johannesjaeger.eu/blog/the-thing-about-epistemic-humility

Yogi Jaeger Sep 15, 2023

@NicoleCRust

Thanks for your intervention! I was getting frustrated with this particular individual, but there is not reason to punch down.

The situation inspired me to write a short blog post called "The Thing about Epistemic Humility."

I think it's important to know when such humility is appropriate (even essential) and when it is definitely not.

The Thing about Epistemic Humility

Twice now, in the short span of one week, I've been reminded on social media that I should be more humble when arguing — that I lack epistemic humility .

Untethered in the Platonic Realm

Nicole Rust Sep 15, 2023

Thank you for this. I really appreciate your post, and the reminder of what epistemic humility is and is not (for all of us).

(I'm also excited to stumble upon this: http://www.johannesjaeger.eu/philosophy.html
Thanks for making that available!)

Philosophy Courses for Researchers

Philosophy for Researchers

Untethered in the Platonic Realm

Ben Kanter Sep 17, 2023

@yoginho I appreciate your blog post as it provides context to why you're so frustrated. I'm fine with calling people out, but the manner in which you did it I take issue with. Shouting at someone in repeated posts claiming their worldview is dangerous and delusional and without merit, even if you're correct (!), is not a fruitful way to start a conversation. It's human behavior to avoid such an attack. And even as a passive observer it's unpleasant. So I'm calling you out ;)

And to be sure, I wouldn't care if you were a troll or kook but your ideas are fascinating and I want people to pay attention. I listened to you on Brain Inspired (after this exchange) and it was one of my favorite episodes of the 50 or so I've listened to. I loved in particular the part about limitations of dynamical systems thinking for brains/organisms, and the idea of organismal closure. I'm very sympathetic to your critique of computationalism, too, particularly because functions the brain carries out are ill defined and much of its function is almost surely not algorithmic, in my opinion.

In the podcast, however, you acknowledge the utility of the computational approach for exploring many things the brain does and appreciate that we can (and maybe already have) achieve great insights with that framework. The problem is that some proponents started to confuse the map for the territory, as it were. Much more even handed than this exchange with Blake, and much more likely to get people to listen and discuss!

@NicoleCRust

Yogi Jaeger Sep 17, 2023

Thanks, @beneuroscience, for your engagement (& your nice words about my work).

Hell will freeze over before someone like @tyrell_turing will change their mind or will engage in a true reflection on their world view. It was never my aim to achieve that.

I cultivate a grumpy online persona to make a statement about my frustration with the way philosophy is treated by scientists these days. Also: my provocations reveal the kind of tactics that are used to avoid real discussion about ...

Yogi Jaeger Sep 17, 2023

@beneuroscience ... fundamental philosophical issues that scientists really *should* be thinking about today.

This kind of discussion would reveal just how shaky much of contemporary research in the life & neurosciences is, not to mention AI research (which is the most metaphysically confused of all) ...

Being nice just leads to deadly silence. That's the standard discussion suppression mechanism. I try to break through it by being a pain in the ass. It's not pleasant, but it often works.