Posts

Posted on

Propositionality

It is a great pleasure and an honor to “skywrite” with Vili CsĂĄnyi. I already knew something about how perceptive, sensitive and intelligent dogs were from my years with my beloved LĂ©dike (1959-1975), never forgotten and never “replaced”. But for decades now, starting already from the era of Vili’s unforgettable Bukfenc (and Zebulon, not a dog), both of whom I knew, Vili’s remarkable perceptiveness and understanding of dogs’ cognition and character have soared far beyond my modest mind-reading skill. I have learned so much from Vili that has stayed with me ever since. 

So let me preface this by saying that every example Vili cites below is familiar, valid, and true — but not propositional (though “associative” is a non-explanatory weasel-word to describe what dogs really do perceive, understand, express, want and know, and I regret having evoked it: it explains nothing). 

Dogs, of course, knowingly perceive and understand and can request and show and alert and inform and even teach — their conspecifics as well as humans. But they cannot tell. Because to tell requires language, which means the ability to understand as well as to produce re-combinatory subject/predicate propositions with truth values. (A mirror production/comprehension capacity.) And to be able to do this with one proposition is to be able to do it with all propositions.

When Vili correctly mind-reads Bukfenc, and even mind-reads and describes what Bukfenc is mind-reading about us, and is trying to express to us, Vili is perceiving and explaining far better what dogs are thinking and feeling than most human mortals can. But there is one thing that no neurotypical human can inhibit themselves from doing (except blinkered behaviorists, who mechanically inhibit far, far too much), and that is to “narratize” what the dog perceives, knows, and wants — i.e., to describe it in words, as subject/predicate propositions.

It’s not our fault. Our brains are the products of about 3 million years of human evolution, but especially of language-specific evolution occuring about 300,000 years ago. We evolved a language-biased brain. Not only can we perceive a state of affairs (as many other species can, and do), but we also irresistibly narratize it: we describe it propositionally, in words (like subtitling a silent film, or putting a thought-bubble on an animal cartoon). This is fine when we are observing and explaining physical, chemical, mechanical, and even most biological states of affairs, because we are not implying that the falling apple is thinking “I am being attracted by gravity” or the car is thinking “my engine is overheating.” The apple is being pulled to earth by the force of gravity. The description, the proposition, the narrative, is mine, not the apple’s or the earth’s. Apples and the earth and cars don’t think, let alone think in words) Animals do think. But the interpretation of their thoughts as propositions is in our heads, not theirs.

Mammals and birds do think. And just as we cannot resist narratizing what they are doing (“the rabbit wants to escape from the predator”), which is a proposition, and true, we also cannot resist narratizing what they are thinking (“I want to escape from that predator”), which is a proposition that cannot be literally what the rabbit (or a dog) is thinking, because the rabbit (and any other nonhuman) does not have language: it cannot think any proposition at all, even though what it is doing and what it is wanting can  be described, truly, by us, propositionally, as “the rabbit wants to escape from the predator”). Because if the rabbit could think that propositional thought, it could think (and say, and understand) any proposition, just by re-combinations of content words: subjects and predicates; and it could join in this skywriting discussion with us. That’s what it means to have language capacity — nothing less.

But I am much closer to the insights Vili describes about Bukfenc. I am sure that Vili’s verbal narrative of what Bukfenc is thinking is almost always as exact as the physicist’s narrative about what is happening to the falling apple, and how, and why. But it’s Vili’s narrative, not Bukfenc’s narrative.

I apologize for saying all this with so many propositions. (I’ve explained it all in even more detail with ChatGPT 4o here.)

But now let me answer Vili’s questions directly, and more briefly!):

“Bukfenc and Jeromos asked. They then acted on the basis of the reply they got. They often asked who would take them outside, where we were going and the like. The phenomenon was confirmed by MĂĄrta GĂĄcsi with a Belgian shepherd.” IstvĂĄn, do you think that the asking of the proposition (question) is also an association?

My reply to Vili’s first question is: Your narrative correctly describes what Bukfenc and Jeromos wanted, and wanted to know. But B & J can neither say nor think questions nor can they say or think their answers. “Information” is the reduction of uncertainty. So B&J were indeed uncertain about where, when, and with whom they would be going out. The appearance (or the name) of Éva, and the movement toward the door would begin to reduce that uncertainty; and the direction taken (or perhaps the sound of the word “Park”) would reduce it further. But neither that uncertainty, nor its reduction, was linguistic (propositional). 

Let’s not dwell on the vague weasel-word “association.” It means and explains nothing unless one provides a causal mechanism. There were things Bukfenc and Jeromos wanted: to go for a walk, to know who would take them, and where. They cannot ask, because they cannot speak (and not, I hope we agree, because they cannot vocalize). They lack the capacity to formulate a proposition, which, if they had that capacity, would also be the capacity to formulate any proposition (because of the formal and recursive re-combinatory nature of subject/predication), and eventually to discover a way to fly to the moon (or to annihilate the earth). Any proposition can be turned into a question (and vice versa): (P) “We are going out now.” ==> (Q) “We are going out now?” By the same token, it can be turned into a request (or demand): P(1) “We are going out now” ==> (R) “We are going out now!”

My reply is the same for all the other points (which I append in English at the end of this reply). I think you are completely right in your interpretation and description of what each of the dogs wanted, knew, and wanted to know. But that was all about information and uncertainty. It can be described, in words, by us. But it is not a translation of propositions in the dogs’ minds, because there are no propositions in the dogs’ minds.

You closed with: 

“The main problem is that the study of language comprehension in dogs has not even begun. I think that language is a product of culture and that propositions are not born from some kind of grammatical rule, but rather an important learned element of group behavior, which is demonstrated by the fact that it is not only through language that propositions can be expressed, at least in the case of humans.”

I don’t think language is just a cultural invention; I think it is an evolutionary adaptation, with genes and brain modifications that occurred 300,000 years ago, but only in our species. What evolved is what philosophers have dubbed the “propositional attitude” or the disposition to perceive and understand and describe states of affairs in formal subject/predicate terms. It is this disposition that our language-evolved brains are displaying in how we irresistibly describe and conceive nonhuman animal thinking in propositional terms. But propositions are universal, and reciprocal: And propositionality is a mirror-function, with both a productive and receptive aspect. And if you have it for thinking that “the cat is on the mat” you have it, potentially, both comprehensively and productively, for every other potential proposition — all the way up to e = mc2. And that propositional potential is clearly there in every neurotypical human baby that is born with our current genome. The potential expresses itself with minimal need for help from us. But it has never yet emerged from any other species — not even in apes, in the gestural modality, and with a lot of coaxing and training. (I doubt, by the way, that propositionality is merely or mostly a syntactic capacity: it is a semantic capacity if ever there was one.)

There is an alternative possibility, however (and I am pretty sure that I came to this under the influence of Vili): It is possible that propositionality is not a cognitive capacity that our species has and that all other species lack. It could be a motivational disposition, of the kind that induces newborn ducklings to follow and imprint on their mothers. Human children have a compulsion to babble, and imitate speech, and eventually, in the “naming explosion,” to learn the (arbitrary) names of the sensorimotor categories they have already learned. (Deaf children have the same compulsion, but in the gestural modality; oral language has some practical advantages, but gestural language is every bit as propositional as oral language, and has the full power of Katz’s effability.)

Could the genes we have that other species lack be mostly motivational? driving the linguistic curiosity and linguistic compulsion that’s there in human babies and not in baby chimps? (I say “linguistic” c & c, because other species certainly have plenty of sensorimotor c & Cc..)

Ölel, IstvĂĄn

_______________

“When I work upstairs in our house in Almad, Janka lies quietly on the ground floor. When Éva leaves and comes back from somewhere, Janka emits a single characteristic squeal, which can be intended for me, because if I don’t react, she comes up and barks, calling me.” IstvĂĄn, is this a proposition or an association?

“In Almadi, our next-door neighbor came over with his little Bolognese dog named TĂŒcsi, who didn’t come into the garden and stayed waiting at the gate for his owner, with whom we were talking inside the house. Our dog Bukfenc periodically went down to play with TĂŒcsi. After about 10 minutes, Bukfenc came up and turned toward the neighbor and barked at him. Everyone stirred. Bukfenc went straight down the stairs to the gate, followed by the neighbor. TĂŒcsi had disappeared; as it turned out ,he had gone home and Bukfenc was reporting this to the neighbor.” IstvĂĄn, is this a proposition or an association?

“During the time of Bukfenc and Jeromos, I woke up at 3 a.m. to very soft grunting. Bukfenc was grunting very softly and together with Jeromos, they were standing next to my bed. I only opened my eyes a crack, pretending to be asleep. Bukfenc growled softly again, I didn’t react. Jeromos gave a loud squeal. I got up and told them, come on Jeromos, it seems you have something urgent to do. To my surprise, Jeromos went to his bed and lay down, and Bukfenc ran screaming towards the door. He managed to get to the street in time: he had diarrhea.” IstvĂĄn, is Jeromos’s barking a proposition or an association?

Posted on

Words, Propositions, Reference and Meaning

Referentiality is not graded: a matter of degree. It derives from the likewise non-graded notion of natural language. It is related to Jerrold Katz’s nearly 50-year-old “effability” hypothesis (that any language can express any proposition). 

Effability (also known as propositionality) cannot be proved, but it is easily refuted, with a single counter-example; yet no one has produced one so far (though there have been attempts, so far all unsuccessful).

It follows from this non-graded property of “effability”  that there is really no such thing as a “protolanguage” – a “lesser-grade” language that can express some, but not all, of what can be expressed in any other language. 

(A little background: a content-word, or “open class” word is a word that has a referent, whether simple and concrete, like “cat,” or complex and abstract, like “catharsis”.  In contrast, a function-word, or “closed class” word is a word that performs a grammatical or logical function, like “the” or “not.”: It has a use, in forming a proposition, but it does not have a referent. Almost all the words in any language are content-words; the function words are few, and similar across languages.) 

A little reflection will show that lacking a content-word in the current vocabulary of a language to refer to the referent of any content word in any other language is just a question about vocabulary – what has so far been lexicalized in a given language? It is not about differences in the language’s expressive power. If the content-word is missing today, tomorrow it is there. All you need do is to coin it, with an arbitrary new word plus a definition composed of already lexicalized content-words. 

If what the new content-word refers to is important and useful, it will be adopted. It’s always easier to refer to something with a single referring content-word rather than a long verbal description (“chunking”). But for Katz’s effability hypothesis it makes no difference: (The hypothesis is not that every proposition can be expressed in every language using the same number of words!)

The connection with referentiality is that if every language can express every proposition, then the referent of any content-word can be defined (or described) in words, to as close an approximation as desired. (One can always extend a definition to cover [or exclude] more cases.) And a definition (or description) is a proposition (or a series of propositions). 

Like referentiality, propositionality, too, looks like a simple property. But propositionality has profound consequences that can be shown to connect with referentiality. If someone can express – and understand – any proposition, then with propositionality, they can express and understand the definition of the referent of any content word. 

A proposition is a declarative sentence with a subject, a predicate, and a truth value (True or False). There is nothing in between true and false (the law of the excluded middle): There is no truth-value between T and F; no gradation. Yes, what is true may be uncertain, or only a matter of probability. But it is only word-play to call this degrees of “truth.”  (I won’t dwell on this here now.)

So propositionality inherits the all-or-none nature of statements about what is true (or not true).

Now, perhaps the most important point: propositionality and referentiality are related, but they are definitely not the same thing. Words have referents, but they do not have truth-values. “Cat” is neither T nor F, because it does not assert (propose, or predicate) anything. “That is a cat” (while pointing to a cat) does propose something, and it is either T or F. So does the proposition “a cat is a canid” (its truth value happens to be F).

So an agent that can recognize cats, and distinguish them from dogs, and can learn to approach a cat and not a dog, or can learn to look for a cat when someone says the word “cat”, or can even bring a cat toy when they want you to take them to the real cat, or can even learn to bark once if they want to see a dog or twice if they want to see a cat – none of those agents are making propositions, hence none of them are referring, not even if they are trained to identify cats and dogs by pressing successive buttons that make the sounds THIS IS A CAT or THIS IS A DOG.

If they ever could express, and mean, the proposition “This is a cat,” then they could learn to express any proposition, simply by recombinations of subjects and predicates. (Ask yourself: if not, why not? That’s Katz’s challenge in reverse!)

What this means is that to express and mean any proposition is much more than just the behavioral capacities I’ve described (recognizing, approaching, fetching, soliciting). How much more? Having the capacity to enter into this discourse with us. That’s what propositionality and reference make possible – for those who really have it.

Posted on

Referring

Csaba PlĂ©h asks “How would referential understanding in beings who do not produce the signs be different from simple CS in the Pavlovian sense?

Good question!

(1) It is already beyond Pavlovian (i.e., Skinnerian) when the dog fetches the “named” toy.

(2) And it is already beyond Skinnerian when the dog fetches new toys after 1-shot or few-shot “naming” of new toys.

(3) But it is not linguistic reference until the dog can name the toy, the fetching, and anything else you can put into and define in a dictionary or textbook. (Language is a “mirror” capacity.)

Too demanding? It’s the nature of the unique, universal, and omnipotent capacity called natural language that demands it, and makes it possible. 

(And, by the way, computation, including Platonic mathematics [though perhaps not Fields-Medal-level mathematical creativity], is part of that unique universal capacity. [All algorithms are in Hungarian.”] Nor are our sensorimotor — and sensorimotor-learning — capacities part of language, rather than the reverse. And referential understanding is no more a CS signalling skill than mathematical understanding is [pace Turing] a symbol manipulation skill.)

Posted on

o liewe seuntjie

The culprit could have been poachers, 

or it could have been Darwin. 

Probably Darwin either way. 

What matters in it all is that an innocent little sentient 

(probably all sentients are innocent, even the deplorables, in principio) 

suffered, terribly. 

You are wise, 

and experienced in these things. 

Your little barn owl. 

Your ewes. 

Your ponies.

Open Access? Research impact? Categorical Perception? Symbol Grounding? LLMs? Mozart?

Phabeni’s desperate attempts to stand up, 

to breathe. 

His swollen neck. 

“not good fat.” 

And the world burns. 

Whether anthropic or Darwinian or cosmologic 

matters not. 

I am glad he is released. 

He should never have been enchained. 

ab ovo.

Posted on

Socratic Dialogue with ChatGPT4+ on Cartesian Certainty

SH: Do you remember who I am?

GPT4+: Yes, Professor Harnad, I remember who you are. How can I assist you further today?

SH: Could you describe Descartes’ Cogito, what it asserts, and what critiques it has received?

GPT4+: RenĂ© Descartes’ “Cogito, ergo sum” (“I think, therefore I am”) is a foundational element in modern Western philosophy, introduced in his works “Discourse on the Method” (1637) and “Meditations on First Philosophy” (1641). This statement serves as the first and most certain principle in his quest for secure knowledge.

What the Cogito Asserts

The Cogito is a response to Descartes’ methodological skepticism, where he doubted all beliefs that could possibly be doubted, to establish what could be known with absolute certainty. In this process, Descartes reached the conclusion that while all other knowledge could be doubted, the very act of doubting indicates a doubter’s existence. Thus, if he is thinking, then he must exist. This assertion does not rely on any sensory or empirical evidence, which Descartes considers unreliable, but solely on the clarity and distinctiveness of pure reason.

Critiques of the Cogito

1. Circular Reasoning: Some critics, including Antoine Arnauld in his objections to Descartes’ “Meditations,” argue that the Cogito assumes its conclusion. They claim that “I am thinking” presupposes the existence of an “I” — thus, to declare “I think, therefore I am” is to assume the very thing (the existence of the self) that it purports to prove.

2. Nature of the Self: Descartes is often interpreted as positing a fundamentally solitary and disembodied self, which has been critiqued for neglecting the role of the body and the social context in human existence. Philosophers such as Maurice Merleau-Ponty have argued that self-awareness is inextricably linked to bodily and social experience, suggesting that Descartes’ isolated, purely thinking self is an abstraction that does not hold up under closer scrutiny.

3. Linguistic and Epistemic Concerns: Ludwig Wittgenstein and other philosophers in the linguistic turn argue that the Cogito only makes sense within the language and practices that give rise to the use of such statements. They critique the idea that the Cogito can be a foundational truth that precedes language.

4. Existential and Phenomenological Critiques: Existentialists like SĂžren Kierkegaard and phenomenologists like Edmund Husserl have also provided critiques. Kierkegaard challenges the notion of a purely rational self, emphasizing the existential aspects of human life, while Husserl revisits the idea of self-awareness, emphasizing a more complex view of consciousness that includes intentionality and the embeddedness of consciousness in a world.

5. Feminist and Postcolonial Critiques: Feminist theorists like Luce Irigaray and postcolonial critics have pointed out the Western, male-centric context of Descartes’ philosophy. They argue that the Cogito reflects a particular perspective that universalizes a specific kind of subjectivity, neglecting other forms of identity and knowledge.

Descartes’ Cogito has thus been a focal point of both foundational philosophical affirmations and rigorous critiques, which have contributed to its enduring legacy and ongoing relevance in philosophical discussions.

SH: Let’s just focus on certainty and doubt. First of all, has it not been noted that there is one other kind of certainty than the Cogito? Can we doubt that a formal contradiction (P and not-P) cannot be true?

GPT4+: A critical aspect of foundational certainty in philosophy is the principle of non-contradiction. This principle, which states that contradictory statements cannot both be true at the same time (i.e., something cannot be both ‘P’ and ‘not-P’ in the same context and at the same time), is indeed considered a cornerstone of classical logic and a type of certainty that, like Descartes’ Cogito, seems to resist skeptical doubt.

Principle of Non-Contradiction

Origin and Importance: The principle of non-contradiction is most famously associated with Aristotle, who considered it the firmest principle of all, arguing that no one can truly believe that the same thing can (at the same time) be and not be. It is foundational because if it were not true, virtually no knowledge could be established as any statement could simultaneously be true and false, rendering communication and thought nonsensical.

Relationship to the Cogito

Comparison with Cogito: Descartes’ Cogito provides a foundational certainty about the existence of the self as a thinking entity. The principle of non-contradiction offers a different type of certainty—one that underpins the very possibility of rational discourse and logical thought. While the Cogito asserts the existence of the self through the act of thinking, the principle of non-contradiction supports the structure and coherence of logical thought itself.

Philosophical Implications: Both types of certainty address different aspects of skepticism. Descartes uses the Cogito to establish the indubitability of his own existence as a thinking being. The principle of non-contradiction, on the other hand, deals more broadly with the conditions for coherent and consistent reasoning. Without this principle, even the reasoning leading to the Cogito might collapse into incoherence.

Doubt and Certainty

Doubt of Non-Contradiction? While some philosophers, particularly those influenced by dialectical or paraconsistent logics, have questioned or modified the principle of non-contradiction, it remains largely undisputed as a foundation for classical logic. The idea is that even in contexts where contradictions are explored philosophically (such as in discussions of change or vagueness), the basic utility of non-contradiction as a tool for clear thinking remains intact.

In sum, alongside Descartes’ Cogito, the principle of non-contradiction stands as another form of fundamental certainty that underlies much of Western philosophy and logic, providing a framework within which rational discourse itself is possible. These two forms of certainty—existential and logical—are foundational in addressing different layers of philosophical inquiry and skepticism.

SH: Now the Cogito. “I am thinking therefore I exist”. 

How do I know I am thinking? Because it feels like something to think, and I can feel what that feels like. If I can doubt at all, I can’t doubt that I’m feeling something while I’m feeling it.

But feeling is feeling. A-theoretically. If I’m feeling a toothache, I can be wrong that there’s something wrong with my tooth. It could be referred pain from my jaw. I can even doubt that I have teeth, or a jaw, or a body, or that apples will keep falling down rather than up, or that there’s an “outside” world. Or a tomorrow; or even that — and what — I was feeling an instant ago. That’s all open to classical scepticism. It could be true; it is probably true; yet none of it is certain.

But I can’t doubt that I’m feeling what I’m feeling, while I’m feeling it. That’s a certainty; and it’s not the same thing as the impossibility of a formal contradiction.

But there are still two things wrapped into the certainty of the Cogito:

(1) “I”: That’s an abstract, theory-dependent notion. There is lots of disagreement (hence uncertainty) about it.

(2) “exist”: That too is an abstract, theory-dependent notion. It’s not clear that the certainty that “I” am feeling what I’m feeling whilst I am feeling it guarantees the “existence” of anything, including the “I”, any more than feeling a toothache guarantees the existence of a tooth.

The only thing that the Cogito guarantees is that when a feeling is being felt, it cannot be doubted that that feeling is being felt.

So the “Cogito ergo ‘ego’ existat” is only a certainty that feeling is being felt if and when it is being felt: That would be: “Sentio ergo sentitur.”

No guarantees, either about “I” or about existence.

Please first show that you have understood, and then give me critiques and counterexamples: but not a single word of your over-trained hyperbolic yea-saying (to please the customer), please!

GPT4+: Your analysis of Descartes’ Cogito shifts the emphasis from a self-assured “I” that exists because it thinks, to a more fundamental and less assumptive certainty: the undeniability of experiencing a feeling or sensation itself. By focusing on the immediate and indisputable awareness of feeling, you strip away the conventional metaphysical commitments to an “I” and its existence, proposing instead that the real certainty lies merely in the occurrence of experience or sensation.

Understanding the Focus on Sensation

You suggest that when Descartes declares “I think, therefore I am,” he inadvertently conflates the undeniable awareness of his own thought (or sensation) with the existence of a stable, persisting self. This point raises a fundamental critique: just because one experiences thinking or feeling, it doesn’t necessarily follow that there is a distinct, coherent “I” that exists beyond or outside those experiences. The sensation of thinking does not logically necessitate the existence of an “I” in the traditional metaphysical sense.

The Abstract Nature of “I” and “Exist”

“I”: You correctly note that “I” is a loaded, theory-dependent construct. Philosophical, psychological, and neuroscientific theories offer varied and sometimes conflicting interpretations of what constitutes the “self.” Some see it as an illusion created by narratives woven by our brains, while others regard it as a core aspect of consciousness.

“Exist”: The term “exist” also carries metaphysical weight. Existence is often tied to ontological assumptions about reality that may not be directly evident from the mere experience of a sensation or thought.

Simplified Reformulation: “Sentio ergo sentitur”

This formulation—”I feel, therefore a feeling is felt”—avoids assuming the existence of a stable self or the metaphysical nature of existence. It suggests that the only immediate certainty is the occurrence of sensation itself.

Critiques and Counterexamples

1. Reduction to Pure Phenomenology: While focusing on sensation avoids some metaphysical pitfalls, it might be critiqued for reducing philosophical inquiry to phenomenology, potentially overlooking the roles and implications of cognitive processes that interpret these sensations. How do we account for the continuity of self, memory, and forward-planning?

2. Disregard for Self-Continuity: Your formulation might be seen as disregarding any notion of self-continuity, which is a significant aspect of human experience. Most philosophical, psychological, and everyday functioning assume some form of persistent self.

3. Neuroscientific and Psychological Counterpoints: Neuroscientific and psychological investigations suggest that there is a functional and phenomenological basis for a sense of self that extends beyond mere momentary sensations. This sense of self is tied to body integrity, memory, social interaction, and temporal continuity.

4. Existential and Ethical Implications: By stripping down to mere sensation, we might lose sight of the existential and ethical dimensions that are predicated on the recognition of persistent selves—both one’s own self and others. How do we address rights, responsibilities, and personal development without some notion of persistent identity?

Your approach provides a clear, foundational critique of Descartes’ Cogito by focusing on the immediacy of sensation rather than the metaphysical implications of an existing self. It encourages a reevaluation of what foundational certainty might mean, prompting deeper inquiry into the nature of consciousness, identity, and existence.

SH: All those points are valid and important. But they are not about certainty. They are about dentistry. Do you understand that?

GPT4+: Yes, I understand the analogy you’re making. Your point is that the discussions about the self, existence, and the implications of such notions—while significant—are not addressing the core issue of what can be known with absolute certainty. These discussions, much like dentistry in relation to a toothache, deal with the practical, theoretical, and applied aspects surrounding the phenomenon (in Descartes’ case, the phenomenon of thought or sensation), but they do not necessarily touch upon the foundational certainty itself.

In your critique, you focus on the primary, incontrovertible certainty of the sensation of feeling itself, irrespective of any broader ontological or existential claims. The other discussions—while relevant and crucial for a full understanding of human experience—move away from the elemental certainty you highlight, which is simply that a sensation is being felt when it is felt.

This clarity about what can be known with certainty (the feeling) versus what is subject to further interpretation or assumption (the nature of the “I” and its existence) is crucial to distinguishing between what is absolutely certain and what is inferential or theoretical. Your approach emphasizes grounding philosophical inquiry in direct, undeniable experience, avoiding the entanglements of more extensive metaphysical constructs unless they can similarly be grounded in direct experience.

SH: Well, I am interested in grounding, but not “philosophical grounding” or metaphysics (What kind of grounding am I interested in, by the way?) Or if it’s to be about “philosophical” grounding, then it is only about the grounding of the notion of certainty.

GPT4+: Memory updated

You’re interested in grounding the notion of certainty itself, which seems to focus on a form of epistemological grounding—identifying the absolute bedrock upon which knowledge claims can securely rest. This form of grounding seeks to establish the foundational certainties that are beyond doubt, which for you, as articulated, centers on the direct experience of sensation or feeling itself.

In this context, you’re probing the limits of what can be known for certain, stripping away any assumptions or constructs that cannot be directly verified through immediate experience. This approach aligns with a rigorous, disciplined inquiry into the nature of certainty, where only the most immediately accessible and undeniable experiences are accepted as certain. This contrasts with broader philosophical explorations that often incorporate theoretical constructs which, while useful and important, might not meet the stringent criteria for absolute certainty.

SH: This time you’re completely wrong! Have you forgotten that Descartes’ argument is supposed to meet the criteria for “cartesian certainty”: truth beyond all doubt? (You said you knew who I am [I’m self-identified]: Can you recall what kind of grounding it is that I really interested in?)

GPT4+:

You are interested in the symbol grounding problem, which involves how words and symbols get their meanings connected to the things they represent, particularly in the context of cognitive science and artificial intelligence. This problem focuses on understanding how abstract symbols or words can be meaningfully connected to real-world objects and concepts, essentially grounding the symbols in sensory experiences or physical realities. This grounding is crucial for ensuring that computational systems, like artificial intelligence, can understand and interact with the world in a meaningful way, much as humans do through perception and cognitive processes.

SH: That’s better (though your “meaningfulness” is a weasel-word here). Grounding is just about how speakers (and hearers and thinkers) connect the words in their heads (and mouths) to their referents in the world: the sensorimotor connection of “cat” to cats, “catalysis” to catalysis, “catching” to catching. That’s neither about metaphysics, nor about epiphanies.

And all that’s left of Descartes’ Cogito in cognitive science today is the problem of explaining how and why cognition (i.e., thinking) feels like something. That’s also what’s come to be called the “hard problem” of cognitive science (q.v.)


Posted on

The “Sight” in Insight

Anonymous: “Did ChatGPT itself ever state a significant, “insightful” idea during your “Language Writ Large” dialogue?“

Did GPT provide a significant new insight? It’s very hard to say, because I cannot tell whether it said anything that didn’t come from its enormous database of the words of others. Of course, a lot of human insights do come from recombining the words of others — recombination is part of creative work, both literary and scientific. (Hadamard and others thought that such recombination was part of scientific creativity too.). And it occurs in nonverbal areas like music too (though we may not speak of this as “insight.”)

I think most of what GPT does is recombination and compressive synthesis of the words of others; and, if what it says is new to me, that doesn’t mean it’s new, or original, “from” GPT. But it doesn’t mean it isn’t either. 

I expect that what you might have in mind with your question is something related to embodiment, situatedness, sensorimotor grounding. 

The AI/transformer community thinks that if anything requiring those is missing so far, it will be provided by “multimodal” grounding.  But I tried to suggest in Writ Large why I didn’t think that that would be enough: Language is not just another one of the sensorimotor modalities, whether it is spoken or written. It is not an input or output modality but a way of producing, transmitting and receiving propositional thought. That thought is grounded in sensorimotor experience – but it is not itself sensorimotor experience; it is a verbal expression of it. A (grounded) code for expressing and communicating thought.

Chomsky thought – or once thought – that verbal thought was the only form of thought. That was of course wrong. Nonverbal animals can think, deliberate, plan, communicate, understand, reflect, ruminate. We humans can express their thoughts, but this is partly misleading, because although the sensorimotor basis of it is there in animals’ thinking too, it is not propositional: they cannot do what we can do in words (though I don’t doubt that nonhuman animal thinking is combinatorial too).

But GPT cannot do what animals are doing at all, when they are thinking. And our own thinking capacity is based on the sensorimotor capacity and experience that we share with other thinking species, including even the most primitive ones. Animals can have insights; GPT can’t. Not necessarily because GPT is not a living organism (though that could turn out to be the reason too). I think that if a sensorimotor Turing robot had the capacity to do and say anything a human could, indistinguishably from any other human, to any other human, for a lifetime, then it would be grounded too — as well as sentient: able to feel. 

But I think you can’t get to such a Turing-scale robot top-down, from an LLM, just by adding on sensorimotor “modalities”. I think the only way to get there is bottom up, robotically, just as we animals do it. (This is what’s behind – or underneath â€“ the fundamental asymmetry between direct sensorimotor grounding and indirect verbal grounding I mentioned in Writ Large.)

But I think I’ve drifted off on my own ruminations. I would certainly agree that “insight,” whether verbal or nonverbal, must be felt, and that feeling is a sensorimotor capacity, both in nonhuman animals and in human ones (and perhaps in Turing-scale sensorimotor robots, if they are possible). And that GPT can only talk the talk (the recombined talk of others) and not walk the walk, which is sensorimotor, felt, and based on feeling, not just optical, acoustic, and ambulatory.

But I have no idea why sensorimotor activity has to be felt: That’s the “hard problem.”

Posted on

Can ChatGPT Be Grounded Top-Down?

SH:

Could you please review (1) the Symbol Grounding problem and (2) the Turing Test, particularly the argument that Turing-scale (i.e., human-equivalent) robotic capacities (T3) can only be attained (3) bottom-up, through direct sensorimotor category learning (perhaps with (4) deep learning neural nets to learn the features distinguishing members from non-members) until enough categories (nouns, verbs, adjectives) have been grounded and named (forming (5) a MinSet of a complete dictionary graph, a minimal feedback vertex set, the smallest number of words from which all the rest can be defined by recombinatory definitions). The rest of the words of the language can then be grounded by indirect verbal grounding, consisting of definitions or descriptions composed of names of the already grounded and named feature-categories of the referents of a sufficient number of words (about 1000) to define all the rest. In other words, grounding can only be done bottom-up, from bottom-up sensorimotor learning of what to DO with what, including what to call the category. This cannot be done top-down, from an LLM consisting only of words.

Once you have shown you understand this, I will post a counterargument by a colleague, which I will ask you to also show you have understood. Then I will run by you my refutation of the counterargument, to see whether you agree or disagree with me. Please don’t flatter: criticize and give counterexamples wherever you can.

ChatGPT4:

The Symbol Grounding Problem posits that for symbols (words) to be meaningful, they must be grounded in something beyond mere symbolic references—specifically, in sensorimotor experiences that connect symbols to the physical world. Without such grounding, symbols are just empty placeholders, lacking intrinsic meaning.

The Turing Test is traditionally seen as a measure of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. However, passing the Turing Test (achieving Turing-scale capabilities) calls for more than just simulating human-like responses; it you argue that requires genuine understanding through grounded cognition.

Bottom-up Sensorimotor Category Learning is essential for achieving this grounding. Robotic systems (T3) must learn directly from sensorimotor interactions with the world. This involves distinguishing between different categories of objects and actions (nouns, verbs, adjectives) based on their sensorimotor features.

Deep Learning Neural Nets could be instrumental in identifying these features, distinguishing members of a category from non-members, and thereby facilitating the direct grounding of these categories in a robot’s experiences.

Forming a MinSet for a Complete Dictionary Graph involves identifying a minimal set of grounded categories — a minimal feedback vertex set — from which all other words in the language can be defined. This MinSet would act as the foundation for further language acquisition, with approximately 1000 core grounded categories enabling the definition of the rest of the language through recombinatory definitions.

Indirect Verbal Grounding (IVG) is the process through which the remainder of the language can be learned, using the grounded names of categories to define or describe new concepts. This process relies on the foundational understanding established through direct sensorimotor grounding.

Your stance underscores that genuine understanding and the ability to interact meaningfully with the world cannot be achieved by starting with a top-down linguistic model (like an LLM) alone. Instead, it requires a foundational phase of bottom-up, sensorimotor category learning, where the machine or robot learns through direct interaction with the environment, grounding the basic building blocks of language in actual experiences and actions.

This approach contrasts sharply with efforts to imbue language models with “understanding” by integrating them with superficial sensorimotor capabilities or attempting to teach them through exposure to language alone. Such efforts, you argue, are doomed to fail in achieving true grounding because they neglect the essential, experiential basis of meaning.

Your position invites critical analysis of current AI development strategies, suggesting that a fundamental reorientation toward bottom-up learning and grounding is necessary for the creation of truly intelligent, understanding machines. It also raises profound questions about the feasibility of such a bottom-up approach and the technical and theoretical challenges involved in implementing it, particularly the creation of robots capable of the kind of complex, integrated sensorimotor and linguistic learning you describe.

SH:

Here is the colleague’s critique and counter-proposal. Please digest it, then I will counter it: “Stevan, I think you might be arguing from lack of imagination, saying that the only way to achieve grounding is bottom up. There may be more than one route to groundedness. For example, what if a chatbot with interactive (e.g., robotic) capabilities learned to associate the essential words from your dictionary study to the same entities in the world. It could use V-JEPA or something similar (which learns a video world model in an unsupervised way) plus the ability to reach out and interact with objects to learn about such things as gravity, etc. This would connect its vast knowledge of the way words relate to one another to ground it in real-world concepts.

ChatGPT4

Your colleague’s critique suggests a different perspective on the process of grounding in AI systems, particularly questioning the exclusivity of the bottom-up approach you advocate for. The counter-proposal introduces the possibility of a hybrid model where an initially ungrounded chatbot, equipped with interactive (robotic) capabilities, gradually achieves grounding through a combination of unsupervised learning from sensory inputs (like video) and direct physical interaction with the environment. Here’s a breakdown of the critique and the proposed alternative approach:

Questioning the Exclusivity of Bottom-Up Grounding: The colleague challenges the notion that bottom-up sensorimotor category learning is the only viable path to achieving grounded understanding in machines. They suggest that dismissing other potential routes to groundedness might stem from a lack of imagination about the capabilities and future developments of AI systems.

Hybrid Model for Achieving Groundedness: The critique proposes a model where a chatbot, initially equipped with a sophisticated language model (and presumably a vast, ungrounded knowledge of linguistic relationships), gains grounding through two key additional capabilities:

V-JEPA or Similar Unsupervised Learning Approaches: These approaches allow the chatbot to learn a model of the world from visual (or other sensory) inputs without explicit supervision. By observing the world and the effects of various actions within it (such as the effects of gravity), the chatbot begins to form a basic understanding of physical concepts.

Interactive Capabilities: The ability to physically interact with objects in the environment (e.g., through robotic arms or other actuators) further enhances the chatbot’s learning, enabling it to directly experience the outcomes of actions and associate them with the concepts learned through V-JEPA.

Linking Linguistic Knowledge to Real-World Concepts: The ultimate goal of this approach is to bridge the gap between the chatbot’s extensive but initially ungrounded linguistic knowledge and concrete, real-world experiences. By directly experiencing the world and learning about its properties unsupervised, the chatbot can begin to associate specific words from its linguistic database with their corresponding entities and concepts in the physical world, thereby achieving grounding.

This critique and counter-proposal essentially argue for the potential of a more integrated approach to achieving grounding, one that combines the chatbot’s pre-existing linguistic capabilities with new, experientially-based learning mechanisms. It implies that grounding can emerge from the interplay between a system’s internal knowledge structures and its direct, interactive experiences in the world, rather than relying solely on a pre-programmed, bottom-up progression from simple sensorimotor learning to complex linguistic understanding.

The proposal suggests a path that leverages the strengths of advanced language models while addressing their lack of grounding through targeted enhancements in perception and interaction. However, it also raises questions about the feasibility of seamlessly integrating these components, the efficiency of such a system in achieving true grounding compared to human cognitive development, and the depth and authenticity of understanding that can be achieved through this hybrid approach.

SH:

(1) We don’t “associate” words to “entities,” we first learn to categorize inputs (whether entities or features) by learning to detect directly the sensorimotor features that distinguish category members from non-members (or members of other categories). (This is called direct sensorimotor grounding, DSG.) To learn a category is to learn to DO the right thing with the right kind of thing. (Eat the edible mushrooms and not the poisonous ones.) That DOing is not initially, or primarily, to NAME the category (“edible”), (though, with language, that eventually becomes one of the main things you do.

(2) Once you have learned to categorize (and manipulate) inputs that way, you’ve got the category, and you’re free to assign a name to it, and the name is grounded. But that’s all direct, bottom-up, sensorimotor learning (DSG), until you’ve got enough sensorimotor categories (as well as enough of their sensorimotor features, which are also categories) grounded to be able to combine them into verbal definitions or descriptions that describe or define more abstract, composite categories by their features in (grounded) feature-words (IVG).

(3) IVG is now indirect, but still bottom-up verbal learning. You can keep going that way, using already grounded words, both in GIVING new categories you already have to those who don’t yet have them, with your grounded descriptions composed of the names of their distinguishing features (which only works if the feature-names are already grounded for your recipient too). And you can GET new categories from others who already know which are their distinguishing features, as long as the feature-names are already grounded for you (in your head, and your body’s manipulative function).

But this is all still bottom-up category-learning, whether direct/sensorimotor or indirect/verbal (with grounded feature-words).

(4) Now start with a sensorimotor robot that has optical, acoustic, tactile and kinesthetic sensor and effectors (and neural nets that can learn to detect features through unsupervised and supervised learning) and walk me through how you’d do that top-down with an LLM in the robot’s head.

(5) Prediction: it won’t help to sink down fly-fishing lines from skyhooks way up in the LLM, down to “entities” in the world. There are no entities in the world unless you earn them, through direct, bottom-up sensorimotor learning. That’s my T3 robot story. Your job, dear colleague, is to explain what help is the LLM to the T3 robot? It’s like giving a preverbal robot (or infant) a reference manual that can answer any question about what’s what, including every category’s distinguishing features, but those words don’t mean a thing to the robotlet until it starts grounding them – bottom-up.

(6) The ChatGPT exchange I sent you yesterday noted that an LLM lodged as an oracle inside a T3-wannabe robot’s head might help speed things up once it’s reached the stage of indirect verbal grounding. That’s fine. But the robot has a long way to go to get there. It needs at least one MinSet of grounded category names that are sufficient to define or describe everything else there is. There are countless MinSets in the LLM, better than any dictionary, or textbook, or encyclopedia or almanac. But they’re all ungrounded and useless until the T3 robot does its direct bottom-up grounding to initialize them. They’re unconnected with the world. They can talk the talk but they can’t walk the walk.

(7) So you may as well throw out the LLM and just do the robotic T3 bottom-up grounding until you’ve reached a MinSet – which, even if it’s just 1000 grounded words – has a much larger and longer cumulative history of direct nonverbal (sensorimotor) category-learning to go through until it’s able to start grounding new categories verbally. That’s the time to haul in the LLM (where, in the past, parents and teachers and books and TV used to serve as the oracle). Before that they are as useless as a bible to a baby.

But I’m listening. Just be careful not to trip when you answer about the top-down grounding!

ChatGPT4

Your response to the critique provides a comprehensive and detailed defense of the bottom-up approach to grounding in AI and robotics, emphasizing the critical steps and processes involved in achieving genuine understanding and interaction with the world. Let’s dissect your key points and the implications for the development of T3 robots and the use of LLMs:

Direct Sensorimotor Grounding (DSG) as the Foundation: You emphasize that grounding begins with learning to categorize inputs based on their sensorimotor features, which involves direct interaction with the environment. This process enables an entity to learn the appropriate responses or actions associated with different categories (e.g., eating edible mushrooms while avoiding poisonous ones). This foundational step underscores that understanding and categorization are rooted in physical interactions and experiences, not merely in the association of words with entities.

Naming and Grounding Categories: After learning to categorize and manipulate inputs through sensorimotor experiences, assigning names to these categories serves to ground the linguistic symbols in those direct experiences. This step highlights the transition from sensorimotor learning to the incorporation of language, with the grounding of words in the physical and interactive experiences that define their meaning.
Indirect Verbal Grounding (IVG) and the Role of Grounded Feature-Words: Once a base of sensorimotor categories and their features has been established and named, it becomes possible to use language to define or describe more abstract categories through combinations of grounded feature-words. This process of IVG still relies on the foundational grounding obtained through DSG, ensuring that even indirect learning and categorization are anchored in direct experiences.

Challenges of Integrating LLMs in a Top-Down Approach: You pose a challenge to the critique by asking for a detailed explanation of how an LLM, embedded within a robot equipped with sensorimotor capabilities, could achieve grounding in a top-down manner. Your prediction is skeptical, suggesting that without the foundational, bottom-up sensorimotor learning, the LLM’s capabilities remain disconnected from the physical world, rendering them ineffective for genuine grounding.

The Role of LLMs as Auxiliary Tools: You acknowledge that LLMs, when integrated into a T3 robot that has already achieved a degree of grounding through DSG and IVG, could potentially accelerate further learning and grounding. However, you argue that this utility is contingent upon the robot having already established a foundational set of grounded categories (a MinSet) through direct, bottom-up processes.

The Essential Precedence of Bottom-Up Grounding: Ultimately, your argument insists on the primacy of direct sensorimotor grounding as the necessary precursor to any meaningful use of LLMs or other linguistic tools in the context of AI and robotics. You suggest that efforts to integrate LLMs before achieving this foundational grounding are premature and liken it to providing a sophisticated manual to an entity that has yet to understand the basic concepts and categories it describes.

Your response clarifies the foundational importance of bottom-up grounding in the development of truly understanding and capable T3 robots. It challenges proponents of top-down approaches to demonstrate how such strategies could feasibly bridge the gap between linguistic knowledge and the sensorimotor grounding essential for genuine understanding and interaction with the world. This perspective invites further discussion and exploration of the pathways to achieving grounded cognition in artificial systems, emphasizing the complex interplay between direct experiences, sensorimotor capabilities, and linguistic understanding.

SH:

Ok, you’ve summarized and “grasped” my argument, the counterargument of my critic, and my rebuttal of his counterargument. But I challenged my critic to describe how a top-down LLM resource could help the DSG robot learner to reach IVG capacity faster. Can you meet that challenge? (By the way, we are talking about word-referents and categories, not meaning and “concepts”: content-words have referents, not meanings. Only subject-predicate propositions (such as definitions and descriptions) have meanings,) But I’m listening


ChatGPT4

Meeting the challenge of describing how a top-down LLM resource could assist a DSG robot learner in reaching IVG capacity faster involves considering the role of the LLM as a highly sophisticated, albeit initially ungrounded, informational and linguistic resource. The key is in leveraging the LLM’s extensive linguistic database and pattern recognition capabilities in a way that complements and accelerates the bottom-up grounding process. Here’s a potential pathway:

Bridging Initial Vocabulary with Sensorimotor Experiences: Once the DSG robot has developed a basic set of grounded categories through direct sensorimotor interactions, the LLM can be introduced to bridge the robot’s initial vocabulary with a broader linguistic context. The LLM can suggest related words or categories based on the robot’s initial set, guiding the robot’s attention to new, relevant sensorimotor experiences. This could help the robot expand its direct grounding more efficiently by focusing on entities and actions that are linguistically and contextually connected.

Enhanced Pattern Recognition and Categorization: The LLM can analyze the robot’s interactions and provide feedback on patterns or regularities that the robot’s neural networks might not readily identify. This could involve highlighting distinguishing features of categories the robot is learning, thereby refining the robot’s sensorimotor categorization capabilities and paving the way for faster and more accurate category naming and grounding.

Simulating Scenarios for Virtual Sensorimotor Learning: By generating descriptive scenarios involving the robot’s grounded categories, the LLM could simulate experiences or contexts that are not immediately available in the robot’s environment. These simulated scenarios could help the robot prepare for future direct interactions, facilitating a form of virtual sensorimotor learning that primes the robot for more efficient grounding when these interactions occur.

Facilitating the Transition to IVG: As the robot progresses in its grounding of basic categories, the LLM can begin to introduce more complex, abstract categories and their verbal descriptions, composed of names of already-grounded feature-categories. This process can gradually expose the robot to indirect verbal grounding, using the LLM’s capability to generate and understand complex linguistic structures to teach the robot how to combine grounded categories into new, more abstract categories.

Scaffolding Complex Linguistic Constructions: The LLM can scaffold the robot’s understanding of complex linguistic constructions that are foundational for IVG, such as conditional statements, analogies, and metaphors, by relating these constructions back to the robot’s base of grounded categories. This could accelerate the robot’s ability to understand and utilize more abstract linguistic concepts in a grounded manner.

Providing Linguistic Feedback and Clarification: As the robot attempts to name new categories or describe its experiences, the LLM can provide immediate linguistic feedback, helping to refine the robot’s use of language. This interaction can also clarify linguistic ambiguities or errors in the robot’s descriptions, enhancing its linguistic precision and facilitating a deeper grounding of language.

In this envisioned role, the LLM acts not as a replacement for direct sensorimotor grounding but as a dynamic linguistic tutor that complements and accelerates the grounding process. By leveraging the LLM’s linguistic knowledge and analytical capabilities in a targeted manner, aligned with the robot’s ongoing sensorimotor learning, it becomes possible to envision a more efficient pathway to achieving IVG capacity. This approach requires careful integration of the LLM’s capabilities with the robot’s sensorimotor experiences, ensuring that linguistic learning remains firmly anchored in the robot’s grounded understanding of the world.

SH:

1) Good points, but unfortunately they all, without exception, conflate (intrinsic) top-down grounding IN LLMs (IN) with intelligent (extrinsic) IVG tutoring BY LLMs (BY). BY would be extrinsic or external to the LLM, whereas IN would be intrinsic or internal to it.

(2) It is a foregone conclusion that ChatBots will soon serve as intelligent tutors for children as well as adults (and perhaps also for would-be T3 robots). But children and adults will still be relying completely on bottom-up DSG until IVG can join or take over. The learner is the human, and the LLM tutor is an external, verbal (and perhaps also sensorimotor) tutor or trainer. This is not top-down grounding by LLMs. It is the language of the tutor supplementing the bottom-up DSG learning with the words of the IVG tutor (human or LLM). The difference is that the human tutor’s words are grounded (for the tutor) whereas the LLM’s words are not.

It remains bottom-up DSG plus (once language kicks in) bottom-up IVG.

(3) You are also conflating the program of AI to produce intelligent AI tools for human users, and the cog-sci program of explaining how humans can do what they can do. Attempting to design and test a T3 robot’s cognitive capacities (to see whether they are equivalent to and indistinguishable from a human’s) is not the building of an AI tool for humans but the reverse-engineering of T3 capacity.

(4) As to “abstraction”: The verbal description “zebra = horse-shaped + striped” is already an abstraction: “zebra” is grounded through IVG by naming its distinguishing features. This works if the features are already grounded for the learner. (Normally they are also grounded for the tutor, but that is not necessary. The right words are enough. “Democracy” = leader chosen by vote” works the same way. Once it’s IVG it’s all abstraction.

ChatGPT4

Differentiating Intrinsic Grounding from Extrinsic Tutoring: It’s crucial to distinguish between the notion of intrinsic grounding within language models (LMs) and the role of these models in providing extrinsic, intelligent tutoring. Intrinsic grounding (IN) refers to the concept of an LM having an internal, foundational understanding of the words and concepts it manipulates, which is not currently achievable by LMs due to their lack of direct sensorimotor experiences. Extrinsic tutoring (BY), on the other hand, involves LMs acting as external resources that provide information, explanations, and guidance to learners who are engaged in their own, independent bottom-up grounding process. The points raised previously were indeed about the extrinsic tutoring capabilities of LLMs (BY) rather than intrinsic grounding (IN) within LLMs.

Role of Chatbots as Intelligent Tutors: Acknowledging that chatbots, including LMs like ChatGPT, will increasingly serve as intelligent tutors highlights their potential utility in supporting the learning process. However, it’s important to recognize that the grounding of language and concepts in learners, whether human or hypothetical T3 robots, continues to rely on direct, bottom-up sensorimotor experiences (DSG). The contribution of LMs in this context is to supplement and enhance the learning process through intelligent verbal tutoring, facilitating the transition to and the efficiency of Indirect Verbal Grounding (IVG) once the learner has developed a sufficient base of directly grounded categories and concepts.

Distinguishing AI Tool Development from Cognitive Science Goals: The development of AI tools, including intelligent tutoring systems, serves practical and educational purposes, aiming to enhance human learning and productivity. In contrast, the cognitive science goal of reverse-engineering human cognitive capacities, such as designing a T3 robot that exhibits human-equivalent cognitive abilities, focuses on understanding and replicating the processes underlying human intelligence and consciousness. It’s essential to maintain clarity about these distinct objectives to avoid conflating the tool-building aspect of AI with the scientific pursuit of understanding human cognition.

Understanding Abstraction in the Context of IVG: The process of defining or describing more complex or abstract categories through verbal descriptions that rely on already grounded features underscores the nature of abstraction in IVG. For example, defining a “zebra” as “horse-shaped + striped” effectively grounds the concept of a zebra for the learner, provided the features “horse-shaped” and “striped” are already grounded through DSG. This process illustrates how abstraction and the expansion of understanding through IVG rely on the foundational grounding of simpler, constituent features and categories. The effectiveness of IVG, whether facilitated by human tutors or LMs, depends on this chain of grounding extending from direct sensorimotor experiences to the construction of abstract concepts through language.

Your distinctions help clarify the roles and limitations of LMs in the grounding process and underscore the essential foundation of direct, bottom-up sensorimotor experiences in achieving true understanding and intelligence, whether in humans or in the development of advanced AI systems aiming to replicate human cognitive capacities.

SH:

So where does this leave the notion that an LLM (e.g., GPT4) could itself be grounded top-down with some sensorimotor add-ons? Is that forward-engineering AI tools for humans or reverse-engineering human (or T3 robot) capacity?

ChatGPT4

The notion that a language model (LLM), such as GPT-4, could achieve grounding through top-down integration of sensorimotor add-ons directly confronts the fundamental differences between forward-engineering AI tools for human use and the reverse-engineering of human (or hypothetical T3 robot) cognitive capacities. Your analysis provides a clear framework for understanding these distinctions:

Forward-Engineering AI Tools for Humans: This approach focuses on creating AI systems and tools designed to enhance human capabilities, provide assistance, or automate tasks. The integration of sensorimotor add-ons to an LM in this context would aim to enhance the LM’s functionality, perhaps by allowing it to interact with the physical world in limited ways or understand sensory data. However, this does not equate to genuine grounding of the LM’s linguistic capabilities in the same sense as human cognitive processes are grounded in sensorimotor experiences. Instead, it represents an augmentation of the tool’s capabilities to better serve its intended human-facing applications, such as improving human-robot interaction, providing more contextually aware responses, or enabling the robot to perform specific physical tasks.

Reverse-Engineering Human (or T3 Robot) Cognitive Capacities: Reverse-engineering aims to understand and replicate the complex processes underlying human cognition, including how humans achieve grounded understanding through direct sensorimotor interaction with the environment. Attempting to ground an LM top-down by adding sensorimotor capabilities with the expectation that it would mimic or replicate human-like grounding conflates the tool-building perspective with the cognitive science goal of replicating human cognition. Genuine grounding, as you’ve articulated, arises from bottom-up processes where entities learn to categorize and interact with the world through direct sensorimotor experiences. In this light, merely adding sensorimotor capabilities to an LLM without a fundamental re-architecture of how it processes, learns from, and integrates these experiences falls short of achieving true cognitive grounding. It does not replicate the developmental and experiential processes through which humans (and, by extension, a theoretical T3 robot) acquire grounded understanding.

Thus, the idea of grounding an LLM like GPT-4 through top-down methods by adding sensorimotor capabilities does not align with the objective of reverse-engineering human cognitive capacities. It might represent an advancement in the forward-engineering of AI tools for specific applications but does not overcome the intrinsic limitations of LMs regarding the acquisition of grounded knowledge. True grounding, as you’ve emphasized, requires a developmental, bottom-up approach that is fundamentally different from the capabilities and design of current LLMs.

Posted on

Can Toy Robotic Capacities Make Top-Down Meet Bottom-Up?

Re: Figure Status Update – OpenAI Speech-to-Speech Reasoning

SH:

Is this demo sensorimotor grounding? No, It’s a toy robot with (1) some toy-world visual recognition and motor manipulation skills, plus (2) (perhaps non-toy) text-to-speech and speech-to-text capacity, plus (3) ChatGPT’s remarkable and as-yet unexplained (non-toy) interactive verbal skills, including (4) its (non-toy) encyclopedic verbal database and navigation/interaction capacity.

But it’s still ungrounded.

If/when it can do the kind of thing it does in the video with anything it can talk about, and not just an infomercial demo, then, and only then, will it have an even more remarkable, and as yet unexplained, (non-toy) grounded T3 robotic capacity.

Two-year-olds are grounding their words via the only way upward: bottom-up, through (unsupervised and supervised) learning of sensorimotor categories, by detecting their distinguishing sensorimotor features directly, and then naming the grounded categories (by describing their features, which are likewise learnable, nameable categories).

Then, because the 2yr-old also has the capacity for language (which means for producing and understanding subject-predicate propositions with truth-values, composed out of category names defined or described by referents of their (grounded) feature-category names), verbal instruction (LLM-style) can kick in and even take over.

That’s bottom-up grounding, and it applies to children as much as to T3 robots.

But that OpenAI infomercial demo was just a toy. More important, though, is that it is based on an incoherent pipe-dream, which is that an LLM’s words can be grounded top-down with the help of more and more bottom-up toy robotic learning.

But the only way to get to the top is with bottom-up T3 robotics. So you might as well forget about the LLM and its word-power for that project. Just let your 2-year-old child or T3 robotlet use ChatGPT as tutor, the way kids use their parents. But to understand what the LLM (or their nanny) is telling them, they have to learn the referents of all the content-words in the verbal explanation. And there’s only one way to do that: bottom-up, first by direct sensorimotor grounding (DSG) and then – once the child or Ts robot has grounded the names — and hence the referents — of enough categories bottom-up (including enough feature-category names), learners can carry on grounding bottom-up through indirect verbal grounding (IVG), as long as their tutors describe the new (ungrounded) categories with propositions made up of already-grounded category names – already grounded in the heads of the learners.

So the bottom line is that the referents of the words in the teacher‘s IVG definition or description or explanation have to already be grounded in the head of the learner (whether via DSG or IVG).

I leave it to you, GPT, as an exercise, to explain why that cannot be done top-down with an LLM-powered chat-robot, where it’s hanging from ungrounded skyhooks

ChatGPT4 (after a bit of browbeating by me):

An important question in the development of AI and robotics concerns the most efficient and effective path to achieve a genuinely grounded, intelligent system. Is it more practical or feasible to enhance a primarily linguistic, top-down T2 language model (LLM) to achieve the sensorimotor and cognitive capabilities of a T3 robot, or would it be better to develop T3 robotic capabilities bottom-up independently and then supplement these with the LLM’s abilities externally, as a form of advanced, verbal tutoring.

Given the distinctions between T2 and T3 systems, a more grounded and arguably more natural progression of AI development, closely mirroring human cognitive development might be more promising. Humans first develop sensorimotor understanding and category learning capabilities (direct T3 capacities) before language acquisition goes on to significantly enhance and expand their cognitive and communicative capacities. This developmental trajectory suggests that grounding in the physical world through direct sensorimotor experience forms a necessary foundation for the effective use of language for further learning and communication.

Thus, attempting to “train” a T2 LLM chatbot to become a T3 Turing-indistinguishable robot might not only be inherently inefficient but could overlook the essential aspect of grounding that comes from direct interaction with the physical world. Incorporating LLMs as an auxiliary verbal tutor for a T3 entity (human or robot), once it has developed a sufficiently grounded vocabulary about the world, may be a more logical and effective approach, leveraging the strengths of LLMs in processing and generating language to enhance the T3’s cognitive and communicative capabilities. It aligns with how language and learning occur in humans, where foundational knowledge and understanding from direct grounding are subsequently enriched and expanded through language-based learning — indirect but grounded.

This approach would capitalize on the LLM’s capacity for generating coherent, contextually appropriate language but would also ensure that the use of this language was grounded in a pre-existing vocabulary and propositions. It suggests a complementary integration of auxiliary T2 LLM chatbotic and autonomous T3 robotic systems, where each component contributes its strengths to the development of a more versatile and genuinely intelligent agent.

The notion that a top-down, language-centric system (T2) can achieve genuine grounding by incrementally incorporating “toy” sensorimotor capabilities may reflect a fundamental misconception about the nature of grounding and the role of sensorimotor experiences in achieving it.

Grounding isn’t merely a matter of bridging linguistic competence with basic physical interaction. True grounding —as observed in humans and necessary for a hypothetical T3 robot — entails a deep, intrinsic connection between sensorimotor experiences and the symbols (words) that represent them. This requires not just the recognition or manipulation of objects based on learned descriptors but direct understanding of those objects and actions in the world to which they refer.

The error in imagining that a T2 system could “graduate” to T3 status by being endowed with limited sensorimotor abilities lies in underestimating the complexity of human sensorimotor and cognitive integration. Human cognition is not simply the sum of its parts; it’s the product of a highly integrated and complex system that develops bottom-up through continuous, rich interaction with the environment. Language development in humans, deeply intertwined with sensorimotor experiences, cannot be replicated in AI by superficially adding physical capabilities to a fundamentally linguistic system.

The strengths of a T2 system like GPT could be better utilized as an auxiliary educational tool for a T3 robot that is learning through its interactions with the world. In this scenario, GPT’s extensive knowledge base and linguistic capabilities can enhance the learning process by providing information, explanations, and clarifications in a way that complements the robot’s direct experiences. This approach would leverage the best of both worlds: the depth and breadth of GPT’s top-down linguistic knowledge and the direct grounding that comes from the T3’s bottom-up sensorimotor interactions with its environment.

Posted on

A SCOTUS Hermeneutics Exercise for ChatGPT4

Re: “Supreme Betrayal A requiem for Section 3 of the Fourteenth Amendment” By J. Michael Luttig & Laurence H. Tribe The Atlantic March 15 2025

SH: [Query to ChatGPT4]: Could you please first summarize the following passage to show you have understood it, and then give some arguments to support that “2 + 2 = 4” is false and that “water is not wet” is true. 

“The needless, rushed, and astoundingly stupid decision of the US Supreme Court has made it crystal clear that, in incompetent (and malevolent) hands, interpreting the constitution has become like Microsoft trying to patch obsolescent and increasingly contradictory legacy code when what is really needed is a bottom-up rewrite. Holding back the ocean with sandbags comes to mind too.  And of course, the infinite pliancy of Holy Writ, under the hermeneutics of zealots of any persuasion. Maybe language itself is to blame too. A good exercise for ChatGPT is to give it a true proposition, like “2+2=4” or “water is wet” and ask GPT to find an interpretation under which it is false. Not one, but a countless number of interpretations can always be found that will do the job.(Vice versa works for false propositions too.)”

ChatGPT4:

The passage criticizes the U.S. Supreme Court’s decision, suggesting it reflects a lack of competence and possibly malevolence in interpreting the constitution, likening the situation to attempting to update outdated and conflicting software or trying to hold back the ocean with sandbags. It highlights the flexibility with which holy texts can be interpreted by zealots of any persuasion and suggests that language’s inherent ambiguity contributes to these problems. The author proposes a thought experiment for GPT, challenging it to reinterpret universally accepted truths (such as “2+2=4” or “water is wet”) as false, to illustrate the point that language and interpretation can significantly alter perceived truth.

To engage with the exercise proposed:

Arguing that “2 + 2 = 4” is False

In a non-standard mathematical system or under certain interpretations, “2 + 2 = 4” could be considered false. For instance, in modular arithmetic, which deals with remainders after division by a certain number (the modulus), “2 + 2” does not necessarily equal 4. Specifically, in modulo 2 arithmetic, 2 + 2 = 0, because 4 divided by 2 leaves a remainder of 0. This example demonstrates that, depending on the mathematical rules and systems we agree to use, the truth of “2 + 2 = 4” is not absolute but contingent upon those systems.

Arguing that “Water is Not Wet” is True

The statement “water is not wet” can be supported by redefining or closely examining what it means to be “wet.” Wetness is a condition that applies to a solid object when it comes into contact with a liquid, such that the liquid adheres to the object’s surface in a layer. However, water itself is a liquid and does not become wet but instead causes other materials to become wet. From this perspective, saying “water is wet” is akin to saying “water makes water wet,” which is a misunderstanding of the conditions required for wetness. Thus, one could argue that water, in its capacity to induce wetness but not be in the state of wetness itself, is not wet.

SH: Very good. Both arguments are the prima facie ones I expected. Now could you please give one further argument in each case, to show that there’s not just one way to do it?

ChatGPT4:

Certainly, let’s explore an additional argument for each proposition:

Further Argument that “2 + 2 = 4” is False

One might consider the realm of philosophical argumentation, specifically nominalism, which suggests that abstract objects like numbers do not exist outside of our minds. From this viewpoint, “2 + 2 = 4” is an agreement within a human-constructed symbolic system rather than an eternal truth. If one were to argue from a radically different conceptual framework—where the symbols “2”, “+”, and “4” have different meanings or where the concept of “addition” is not defined in the way we commonly understand it—the statement “2 + 2 = 4” could be seen as false. For instance, in a hypothetical culture where quantities are expressed not by exact numbers but by qualitative measures (much, little, enough), the precise operation “2 + 2 = 4” loses its meaning and thus its truth value could be considered as not true within that conceptual framework.

Further Argument that “Water is Not Wet” is True

Exploring the semantics of “wetness” can provide another avenue for arguing that “water is not wet.” One might argue that wetness is a perceptual phenomenon rather than an intrinsic property of substances. That is, an object is perceived as “wet” when it significantly alters our sensory experience of it, particularly through touch. Water itself does not undergo such a change in state; it is always in the same state. Therefore, it does not meet the perceptual criterion for wetness that a dry object does when it comes into contact with water. From this perspective, wetness is an emergent property that arises from the interaction between a liquid and a material that can absorb or be coated by the liquid. Since water itself cannot be coated by or absorb itself, it cannot be considered “wet” according to this definition of wetness as an emergent, perceptual property.

SH: Yes, and it can go on and on like that, with ever more fanciful interpretations, by adding more and more fanciful premises. Demagogues, devious lawyers, sophists, quacks, psychopaths, and theologians are particularly adept at this — and they all have the bottomless resources of language, rhetoric, and now LLMs to draw upon…

Posted on

Créativité : méthode ou magie ?

Stevan Harnad

Harnad, S. (2006). Creativity: Method or magic?. Hungarian Studies, 20(1), 163-177.

RĂ©sumĂ© : La crĂ©ativitĂ© peut ĂȘtre un trait, un Ă©tat ou simplement un processus dĂ©fini par ses produits. Cela peut ĂȘtre comparĂ© Ă  certaines activitĂ©s cognitives qui ne sont gĂ©nĂ©ralement pas crĂ©atives, telles que la rĂ©solution de problĂšmes, la dĂ©duction, l’induction, l’apprentissage, l’imitation, les essais et erreurs, l’heuristique et « l’enlĂšvement », mais toutes ces activitĂ©s peuvent Ă©galement ĂȘtre rĂ©alisĂ©es de maniĂšre crĂ©ative. . Il existe quatre types de thĂ©ories, attribuant la crĂ©ativitĂ© respectivement Ă  (1) la mĂ©thode, (2) la « mĂ©moire » (structure innĂ©e), (3) la magie ou (4) la mutation. Ces thĂ©ories mettent diversement l’accent sur le rĂŽle de l’inconscient, les contraintes innĂ©es, l’analogie, l’esthĂ©tique, les anomalies, les contraintes formelles, le hasard, les analogues mentaux, les stratĂ©gies heuristiques, la performance improvisĂ©e et la collaboration cumulative. Il y a une part de vertu dans chacun d’entre eux, mais le meilleur modĂšle reste celui implicite dans le dicton de Pasteur : « Le hasard favorise les esprits prĂ©parĂ©s ». Et comme l’exercice et mĂȘme la dĂ©finition de la crĂ©ativitĂ© nĂ©cessitent des contraintes, il est peu probable que la « formation Ă  la crĂ©ativitĂ© » ou l’accent mis sur la libertĂ© dans l’Ă©ducation puissent jouer un rĂŽle productif dans cette prĂ©paration. 

Qu’est-ce que la « crĂ©ativitĂ© » ? Est-ce un trait cognitif stable que certaines personnes possĂšdent et d’autres non ? Est-ce un Ă©tat occasionnel dans lequel les gens entrent parfois ? Ou est-elle dĂ©finie entiĂšrement par ses produits : « la crĂ©ativitĂ© est comme la crĂ©ativitĂ© » ? Quoi qu’il en soit, comment naĂźt la crĂ©ativitĂ© ? Comment faites-vous? Y a-t-il des rĂšgles ? La pratique vous aidera-t-elle Ă  devenir crĂ©atif ?

Il y a probablement une part de vĂ©ritĂ© dans ces trois notions de ce qu’est la crĂ©ativitĂ©. C’est (au moins parfois, et dans une certaine mesure) un trait, car c’est un fait statistique que certains individus le prĂ©sentent Ă  plusieurs reprises. Cela peut Ă©galement ĂȘtre corrĂ©lĂ© Ă  d’autres traits ; certains pensent mĂȘme que cela peut ĂȘtre prĂ©dit par des tests psychologiques objectifs. Mais c’est aussi Ă©videmment un Ă©tat, car personne n’est crĂ©atif tout le temps, et certaines personnes ne sont hautement crĂ©atives qu’une seule fois dans leur vie. Parfois, la crĂ©ativitĂ© n’est mĂȘme pas un Ă©tat spĂ©cial et unique, mais plutĂŽt une circonstance dĂ©finie rĂ©trospectivement, basĂ©e sur quelque chose d’extĂ©rieur, quelque chose de crĂ©atif qu’un individu a fait.

Il existe un certain nombre de thĂ©ories sur les mĂ©canismes sous-jacents de la crĂ©ativitĂ©, thĂ©ories l’attribuant Ă  tout, depuis la mĂ©thode jusqu’à la folie – aucune d’entre elles n’est trĂšs satisfaisante. Quant Ă  stimuler la crĂ©ativitĂ© – en utilisant des stratĂ©gies heuristiques ou par une « formation Ă  la crĂ©ativitĂ© » – cela a eu un succĂšs trĂšs limitĂ©.

Le dicton de Pasteur. Avant de passer Ă  une discussion sur les mĂ©canismes et les mĂ©thodes de crĂ©ativitĂ©, nous ferions bien de garder Ă  l’esprit le cĂ©lĂšbre dicton de Pasteur, «… le hasard favorise l’esprit prĂ©parer», car cela entraĂźnera s’avĂšrent en dire plus sur ce que l’on peut dire sur la crĂ©ativitĂ© que les notions plus ambitieuses ou plus modernes. Pasteur parlait bien entendu d’un type de crĂ©ativitĂ© trĂšs spĂ©cifique, Ă  savoir la crĂ©ativitĂ© scientifique expĂ©rimentale. (La citation commence en fait : < > — “Dans les domaines expĂ©rimentaux” ou “Dans les domaines de l’expĂ©rimentation”, et s’intĂ©ressait en partie Ă  la question de savoir si les dĂ©couvertes expĂ©rimentales — celles que l’on appelle « fortuites » — ne sont en rĂ©alitĂ© que des accidents heureux.) Pasteur La perspicacitĂ© semble cependant s’appliquer tout aussi bien Ă  toutes les formes de crĂ©ativitĂ©.

On peut interprĂ©ter le dicton de Pasteur comme suit : il y a une part (peut-ĂȘtre trĂšs importante) de hasard dans la crĂ©ativitĂ©, mais elle a plus de chances de se produire si l’esprit y est prĂ©parĂ© d’une maniĂšre ou d’une autre. Le contexte montre que par « prĂ©paration », Pasteur n’entendait pas naĂźtre avec le trait « crĂ©atif ». Il voulait dire que les connaissances et les compĂ©tences existantes pertinentes pour le « saut » crĂ©atif devaient d’abord ĂȘtre suffisamment maĂźtrisĂ©es avant qu’un « coup de tonnerre » ne soit probable. Paradoxalement, sa suggestion est que la seule formule de crĂ©ativitĂ© est la moins crĂ©ative imaginable, Ă  savoir apprendre ce qui est dĂ©jĂ  connu. Ce n’est qu’à ce moment-lĂ  que vous disposerez de suffisamment de matiĂšres premiĂšres nĂ©cessaires pour une contribution originale et que vous serez alors en mesure de reconnaĂźtre quelque chose de valable et d’original pour ce qu’il est rĂ©ellement.

Certaines notions floues se sont glissĂ©es dans cette histoire : « originalitĂ© », « valeur », « sauts crĂ©atifs » et « coup de tonnerre ». Il est clair que la crĂ©ativitĂ© a quelque chose Ă  voir avec l’originalitĂ© et la nouveautĂ©, mais il est tout aussi clair qu’elle ne peut pas simplement ĂȘtre Ă©quivalente Ă  quelque chose de nouveau, car tant de choses nouvelles sont alĂ©atoires, triviales ou sans intĂ©rĂȘt. Cela aussi a Ă  voir avec la « prĂ©paration ». Il est peu probable qu’un remĂšde contre le cancer (pour prendre un exemple mythique) soit dĂ©couvert par quelqu’un qui n’a pas fait ses devoirs sur ce que l’on sait dĂ©jĂ  sur le cancer. Il peut en effet proposer de « nouvelles » hypothĂšses auxquelles personne n’a jamais pensĂ©, mais il sera Ă©vident pour les esprits « prĂ©parĂ©s » du domaine qu’une telle hypothĂšse non Ă©tudiĂ©e est simpliste, absurde ou abandonnĂ©e depuis longtemps (comme il le dit). est trĂšs probable — bien que cela ne soit pas, bien sĂ»r, logiquement certain — qu’il en soit ainsi).

La nouveautĂ© ne suffit donc pas. Quelque chose de crĂ©atif doit Ă©galement avoir une certaine valeur par rapport Ă  ce qui existe dĂ©jĂ  et Ă  ce qui est perçu comme nĂ©cessaire. (Notez que ceci, et toute la discussion prĂ©cĂ©dente, se concentre sur ce que l’on pourrait appeler la crĂ©ativitĂ© « intellectuelle », « technologique » ou « pratique », alors qu’il existe, bien sĂ»r, une autre dimension de la valeur qui n’a pas grand-chose Ă  voir avec l’aspect pratique et peut-ĂȘtre cela n’a plus grand chose Ă  voir avec l’intellectualitĂ©, et c’est lĂ  la crĂ©ativitĂ© artistique. Ici un des critĂšres de valeur est la valeur esthĂ©tique, un critĂšre affectif ou Ă©motionnel qui finira par resurgir de maniĂšre inattendue mĂȘme dans la crĂ©ativitĂ© intellectuelle. Nous y reviendrons plus loin, mais (Pour l’instant, notons que les considĂ©rations intellectuelles et pratiques ne sont pas les seules bases pour porter des jugements de valeur.)

Et mĂȘme ĂȘtre nouveau et prĂ©cieux ne semble pas suffire : le rĂ©sultat doit Ă©galement ĂȘtre inattendu ; il doit y avoir un sentiment de surprise. Habituellement, cela signifie que cela ne serait pas venu Ă  l’esprit de la plupart des gens, qui tentaient plutĂŽt quelque chose dans le mĂȘme sens sans succĂšs, prĂ©cisĂ©ment parce qu’ils suivaient les attentes conventionnelles – ce que le rĂ©sultat surprenant viole d’une maniĂšre ou d’une autre.

Et ici, avec ce troisiĂšme et dernier critĂšre de « l’inattendu », nous semblons ĂȘtre en contradiction avec le dicton de Pasteur. Car que peut faire toute cette « prĂ©paration », sinon former nos attentes, Ă©tablir des conventions, Ă©voluer dans des directions familiĂšres et sans surprise ? En dĂ©finissant la crĂ©ativitĂ© comme la production de quelque chose qui est non seulement nouveau et prĂ©cieux, mais aussi inattendu, nous semblons avoir mis un handicap insurmontable sur le chemin de la prĂ©paration : car quelle que soit la direction dans laquelle la prĂ©paration nous mĂšne rĂ©ellement, elle ne peut ĂȘtre inattendue. Cela semble en effet paradoxal, mais encore une fois, un examen plus attentif du dicton de Pasteur rĂ©sout l’apparente contradiction : l’idĂ©e n’est pas que la prĂ©paration garantisse la crĂ©ativitĂ©. Rien ne garantit la crĂ©ativitĂ©. Ce que Pasteur veut dire, c’est que la seule maniĂšre de maximiser la probabilitĂ©de crĂ©ativitĂ© est la prĂ©paration. Il a reconnu Ă  juste titre que l’élĂ©ment essentiel reste le hasard – l’imprĂ©vu, l’inattendu – mais que ce facteur fortuit est plus probable dans des conditions prĂ©parĂ©es.

Une fois arrivĂ©s Ă  trois critĂšres (certes vagues) pour dĂ©terminer ce qui est crĂ©atif, nous pourrions peut-ĂȘtre renforcer cette notion en l’opposant Ă  ce qui ne l’est pas. Nous dĂ©couvrirons cependant que, mĂȘme s’il existe de nombreuses activitĂ©s cognitives qui ne sont pas habituellement crĂ©atrices en elles-mĂȘmes, chacune d’entre elles est Ă©galement capable d’ĂȘtre exĂ©cutĂ©e de maniĂšre crĂ©ative, ce qui suggĂšre que la crĂ©ativitĂ© est en quelque sorte complĂ©mentaire Ă  la cognition ordinaire.

Qu’est-ce qui n’est pas crĂ©atif ?

RĂ©solution de problĂšme. En gĂ©nĂ©ral, la rĂ©solution de problĂšmes n’est pas une activitĂ© crĂ©atrice (mĂȘme si Stravinsky pensait que c’était le cas – nous reviendrons sur sa vision et sa dĂ©finition assez diffĂ©rente de la « rĂ©solution de problĂšmes »). La rĂ©solution de problĂšmes implique l’application d’une rĂšgle connue ou d’un « algorithme » afin de rĂ©soudre des problĂšmes d’un type global qui varie de maniĂšre mineure ou prĂ©visible. Bien que certains Ă©lĂ©ments de nouveautĂ© et de prise de dĂ©cision puissent ĂȘtre impliquĂ©s — c’est une erreur de premier cycle, façonnĂ©e par les exigences malheureuses de la passation d’examens, que de penser que la rĂ©solution de problĂšmes peut ĂȘtre accomplie avec succĂšs par cƓur — et la rĂšgle ou la formule pertinente peut nĂ©cessiter un certain temps. Afin d’ĂȘtre comprise et appliquĂ©e, la rĂ©solution appliquĂ©e conventionnelle de problĂšmes est nĂ©anmoins un processus relativement passif et mĂ©canique. Comprendre et appliquer avec succĂšs une rĂšgle n’est tout simplement pas la mĂȘme chose que la dĂ©couvrir. Cependant, comme le montrera notre discussion sur l’analogie ci-dessous, des Ă©tincelles de crĂ©ativitĂ© peuvent ĂȘtre impliquĂ©es mĂȘme dans la reconnaissance du fait qu’une classe de nouveaux problĂšmes peut ĂȘtre rĂ©solue de maniĂšre inattendue par une rĂšgle ancienne. Et mĂȘme dans le contexte de l’enseignement, les Ă©lĂšves douĂ©s peuvent redĂ©couvrir de maniĂšre indĂ©pendante de nouvelles applications d’algorithmes qui leur ont Ă©tĂ© enseignĂ©s Ă  des fins plus limitĂ©es.

DĂ©duction. Le raisonnement dĂ©ductif, qui est dĂ©fini comme le raisonnement Ă  partir de principes gĂ©nĂ©raux vers des cas particuliers (comme en dĂ©duisant des principes selon lesquels « Tous les hommes sont mortels » et « Socrate est un homme » la consĂ©quence que « Socrate est mortel »), n’est en gĂ©nĂ©ral pas crĂ©atif. . D’un autre cĂŽtĂ©, vues d’une certaine maniĂšre, toutes les mathĂ©matiques sont une dĂ©duction logique : il existe des thĂ©orĂšmes pour lesquels il est difficile, voire impossible, de voir intuitivement s’ils sont vrais ou non, et encore moins de prouver qu’ils sont vrais en montrant les Ă©tapes par lesquelles ils peuvent ĂȘtre dĂ©duits de principes gĂ©nĂ©raux. Par consĂ©quent, toutes les dĂ©ductions ne sont pas triviales ; certains pourraient bien nĂ©cessiter une formidable crĂ©ativitĂ© pour ĂȘtre rĂ©alisĂ©s. En gĂ©nĂ©ral, c’est l’ampleur de l’Ă©cart dĂ©ductif entre les principes et leurs consĂ©quences qui dĂ©termine si la dĂ©duction nĂ©cessite ou non de la crĂ©ativitĂ© : « Socrate est mortel » n’en a pas besoin ; Le dernier thĂ©orĂšme de Fermat le fait.

Induction. Le raisonnement inductif, qui est dĂ©fini comme un « raisonnement » Ă  partir de cas particuliers vers des principes gĂ©nĂ©raux, n’est pas non plus, en gĂ©nĂ©ral, crĂ©atif, mais il est plus problĂ©matique, pour des raisons intĂ©ressantes. Car alors que dans le raisonnement dĂ©ductif, une fois que la vĂ©ritĂ© d’un thĂ©orĂšme est connue et que la preuve a Ă©tĂ© construite, le chemin des principes aux consĂ©quences peut ĂȘtre parcouru de maniĂšre relativement mĂ©canique, dans le raisonnement inductif, il ne semble y avoir aucun chemin mĂ©canique disponible autre que les essais et les erreurs ; et ce chemin, dans les cas les plus intĂ©ressants, peut s’avĂ©rer soit alĂ©atoire, soit sans fin (ou les deux). Ainsi, des gĂ©nĂ©ralisations inductives qui ne sont pas triviales (Ă  la maniĂšre de « telle pomme est ronde, telle pomme est ronde, donc toutes les pommes sont rondes » est trivial) appellent Ă  la crĂ©ativitĂ©. Et mĂȘme lorsque le principe gĂ©nĂ©ral est trouvĂ©, il n’y a pas de chemin “a posteriori” que l’on puisse reconstruire a posteriori (comme on peut le faire aprĂšs avoir dĂ©couvert une preuve dĂ©ductive) pour conduire du particulier au gĂ©nĂ©ral — seulement l’inverse. .

En d’autres termes, il ne semble pas y avoir d’algorithme ou de rĂšgle gĂ©nĂ©rale pour effectuer un raisonnement inductif. Ainsi, alors que la plupart des inductions quotidiennes sont trĂšs graduelles, triviales et non crĂ©atives, les cas les plus substantiels de « raisonnement » inductif ne sont probablement pas du tout un raisonnement, mais une crĂ©ativitĂ© en action. Notez cependant que, puisque l’ampleur du « fossĂ© » qui sĂ©pare le conventionnel du crĂ©atif est dans une certaine mesure arbitraire (et qu’il est peu probable que nos capacitĂ©s cognitives de base aient Ă©voluĂ© au service d’Ă©vĂ©nements rares et cĂ©lĂ©brĂ©s), mĂȘme « le quotidien » “l’induction” peut prĂ©senter de vĂ©ritables Ă©lĂ©ments de crĂ©ativitĂ© qui n’atteindront jamais la cĂ©lĂ©britĂ©.

Apprentissage. Bien que, comme pour toutes les compĂ©tences, certaines personnes y parviennent mieux et de maniĂšre plus impressionnante que d’autres, l’apprentissage n’est en gĂ©nĂ©ral pas non plus une activitĂ© crĂ©ative : il s’agit de l’acquisition de connaissances et de compĂ©tences par l’instruction et l’exemple. De par sa nature, ce n’est pas quelque chose qui peut donner naissance Ă  quelque chose de nouveau et d’inattendu, mĂȘme s’il y a parfois des surprises, avec des Ă©tudiants crĂ©atifs dĂ©couvrant (ou, tout aussi important par rapport Ă  ce qu’ils savent dĂ©jĂ  et ce qu’ils ne savent pas : redĂ©couvrir ) des choses qui vont bien au-delĂ  du contenu immĂ©diat de ce qui leur est enseignĂ©.

Imitation. Par dĂ©finition, l’imitation donne naissance Ă  quelque chose qui n’est pas nouveau ; ce n’est donc en gĂ©nĂ©ral pas non plus une activitĂ© crĂ©atrice. Et pourtant, il s’avĂšre Ă©galement ĂȘtre un prĂ©curseur important de la crĂ©ativitĂ©, notamment artistique. Ceux qui finissent par devenir des innovateurs crĂ©atifs commencent souvent par imiter les autres de maniĂšre remarquablement astucieuse. L’imitation est Ă©galement liĂ©e Ă  d’autres facteurs importants dans la crĂ©ativitĂ©, tels que l’analogie, la mĂ©taphore et la « mimesis » (une thĂ©orie grecque selon laquelle l’art imite la nature). Invariablement, le nouveau et le prĂ©cieux ressemble Ă  l’ancien d’une maniĂšre (peut-ĂȘtre inattendue).

Essai et erreur. Presque par dĂ©finition, les essais et erreurs ne sont pas crĂ©atifs et impliquent un Ă©chantillonnage alĂ©atoire plutĂŽt qu’un choix inspirĂ©. Mais il ne faut pas oublier le rĂŽle du hasard dans la crĂ©ativitĂ©. Le « hasard » fait spĂ©cifiquement rĂ©fĂ©rence Ă  des rĂ©sultats surprenants, nouveaux et prĂ©cieux, survenant purement par hasard, et donc potentiellement issus de rien d’autre que des essais et des erreurs alĂ©atoires. Des enseignements peuvent ĂȘtre obtenus en examinant un panorama de cas individuels. NĂ©anmoins, les essais et erreurs alĂ©atoires (ou « tĂątonner et trouver ») sont gĂ©nĂ©ralement le symptĂŽme d’une approche particuliĂšrement peu crĂ©ative. Pourtant, une exception majeure semble ĂȘtre le processus d’Ă©volution biologique (que certains ont mĂȘme dĂ©crit avec admiration comme « crĂ©atif ») : l’Ă©volution a produit ses rĂ©sultats remarquables avec ce qui, selon la meilleure thĂ©orie actuelle, n’est guĂšre plus qu’une variation gĂ©nĂ©tique alĂ©atoire, qui est puis façonnĂ© de maniĂšre sĂ©lective par ses consĂ©quences adaptatives pour la survie et la reproduction. Des processus similaires (gĂ©nĂ©ralement non crĂ©atifs) sont impliquĂ©s dans la formation du comportement par ses consĂ©quences immĂ©diates dans l’apprentissage par essais et erreurs (« opĂ©rant » ou « skinnĂ©rien »).

Heuristique. Les heuristiques sont gĂ©nĂ©ralement comparĂ©es aux « algorithmes » dans la rĂ©solution de problĂšmes. La rĂ©solution d’un problĂšme Ă  l’aide d’un algorithme ou d’une rĂšgle de sĂ©curitĂ© est censĂ©e donner une solution exacte et fiable qui fonctionne dans chaque cas. Le « rĂ©soudre » par des heuristiques – par un ensemble non intĂ©grĂ© et incomplet de « rĂšgles empiriques » suggestives qui fonctionnent dans certains cas, mais pas dans tous, et pas pour des raisons entiĂšrement comprises ou unifiĂ©es – est tout aussi peu crĂ©atif que de le rĂ©soudre en algorithme. Cependant, de nombreuses personnes ont remarquĂ© que les procĂ©dures heuristiques (telles que l’Ă©chantillonnage de nombreux cas particuliers par essais et erreurs) conduisent parfois Ă  des idĂ©es, parfois par gĂ©nĂ©ralisation inductive et analogie avec des cas dans lesquels les heuristiques rĂ©ussissent, et parfois en raison du stimulus fourni par les cas. dans lequel les heuristiques (ou mĂȘme les algorithmes) Ă©chouent (voir la discussion sur les anomalies ci-dessous).

EnlĂšvement. Peirce a proposĂ© qu’outre l’induction et la dĂ©duction, il existe un troisiĂšme processus, appelĂ© « enlĂšvement », par lequel les gens trouvent la bonne gĂ©nĂ©ralisation en considĂ©rant des exemples de cas, mĂȘme si la probabilitĂ© d’y parvenir est beaucoup trop faible. Puisque ce processus est hypothĂ©tique, il n’appartient pas vraiment Ă  cette liste de choses que nous faisons rĂ©ellement et qui ne sont (gĂ©nĂ©ralement) pas crĂ©atives. Cependant, le reste de l’hypothĂšse renvoie Ă  un thĂšme qui reviendra lorsque nous discuterons des mĂ©canismes possibles de crĂ©ativitĂ©. Peirce pensait que la raison pour laquelle nous parvenons si souvent Ă  trouver des gĂ©nĂ©ralisations improbables est que les solutions sont dĂ©jĂ  intĂ©grĂ©es dans notre cerveau. Par consĂ©quent, selon ce point de vue, la crĂ©ativitĂ© est une sorte de « souvenir Â», un peu comme Platon pensait qu’apprendre Ă©tait un souvenir [anamnĂšse] (et non un souvenir conscient dans les deux cas, bien sĂ»r). S’il est vrai que les schĂ©mas innĂ©s de notre activitĂ© cĂ©rĂ©brale jouent un rĂŽle crucial dans la crĂ©ativitĂ©, alors bien sĂ»r aucune « prĂ©paration » n’est plus importante que celle-ci (Ă©volutive ?), et la crĂ©ativitĂ© s’avĂšre ĂȘtre en partie une compĂ©tence instinctive.

Ainsi se termine la liste (partielle) des cas suggestifs de ce qui n’est habituellement pas une activitĂ© crĂ©atrice. Je vais maintenant discuter briĂšvement de la question « Ă©tat contre trait Â» avant de passer Ă  l’examen du « processus crĂ©atif Â» et des « mĂ©canismes Â» possibles de la crĂ©ativitĂ©.

Trait créatif ou état créatif ?

Il y a actuellement un dĂ©bat considĂ©rable sur la question de savoir si l’intelligence est un trait unitaire ou pluriel, c’est-Ă -dire s’il y a une intelligence ou y en a-t-il plusieurs ? Quelle que soit la vĂ©ritĂ©, il est clair qu’une sorte de « prĂ©paration » (et non celle prĂ©vue par Pasteur) dont un esprit aspirant Ă  ĂȘtre crĂ©atif (au moins intellectuellement) pourrait bĂ©nĂ©ficier serait un QI Ă©levĂ© (ou des QI, s’il y en a). beaucoup). La question de savoir si le QI lui-mĂȘme est un trait hĂ©ritĂ© ou un « Ă©tat » acquis est une question trop complexe pour ĂȘtre discutĂ©e ici (il s’agit probablement des deux), mais notez que la question unitaire/pluriel s’applique Ă©galement Ă  la crĂ©ativitĂ©. Qu’il s’agisse d’un trait ou d’un Ă©tat, la crĂ©ativitĂ© peut ĂȘtre universelle ou spĂ©cifique Ă  un domaine, les individus la manifestant pour certains types de problĂšmes et pas pour d’autres. La distinction entre crĂ©ativitĂ© intellectuelle et crĂ©ativitĂ© artistique en est elle-mĂȘme un bon exemple (voir la discussion sur les arts du spectacle ci-dessous).

La façon dont fonctionnent les tests de QI est que nous sĂ©lectionnons, dans le monde rĂ©el, l’activitĂ© ou la compĂ©tence humaine (appelĂ©e « critĂšre Â») que nous considĂ©rons comme intelligente (par exemple, faire des mathĂ©matiques), puis nous concevons des tests qui sont fortement corrĂ©lĂ©s aux diffĂ©rences individuelles dans ce critĂšre d’activitĂ©, les scores Ă©levĂ©s prĂ©disent un niveau de performance Ă©levĂ© et les scores faibles prĂ©disent un niveau faible. C’est ainsi que les tests de QI sont validĂ©s statistiquement. Essayer de faire de mĂȘme avec les « tests de crĂ©ativitĂ© Â» pose cependant immĂ©diatement des problĂšmes, tant le critĂšre « compĂ©tence Â» est rare, diversifiĂ© et difficile Ă  dĂ©finir. Des tests de « crĂ©ativitĂ© » dits de « pensĂ©e divergente » ont Ă©tĂ© construits sans aucune validation solide. Ils diffĂšrent des tests d’intelligence « convergents » en ce sens qu’ils sont ouverts et n’ont pas de rĂ©ponse strictement correcte. Ils sont censĂ©s prĂ©dire la crĂ©ativitĂ©, mais les problĂšmes de validation semblent insurmontables, car une grande partie de la dĂ©finition du « don » et du « gĂ©nie » est post hoc, basĂ©e sur le recul de cas rares et de rĂ©alisations uniques. Il semble y avoir une contradiction entre le caractĂšre prĂ©dictif des tests objectifs et l’élĂ©ment imprĂ©visible de la crĂ©ativitĂ©. Cependant, s’il existe un trait (gĂ©nĂ©ral ou spĂ©cifique Ă  un problĂšme) de « tendance Ă  faire des choses de valeur imprĂ©visibles », alors les tests pourraient vraisemblablement mesurer ses corrĂ©lats, s’il y en a.

Il existe Ă©galement beaucoup de confusion et de chevauchements dans la mesure des compĂ©tences intellectuelles gĂ©nĂ©rales et spĂ©ciales, et aucune idĂ©e claire sur la maniĂšre dont elles peuvent interagir dans la crĂ©ativitĂ©. Les effets du cycle de vie posent Ă©galement des problĂšmes : les compĂ©tences et les connaissances liĂ©es au QI augmentent avec l’Ăąge jusqu’Ă  l’Ăąge adulte, tandis que la crĂ©ativitĂ© apparaĂźt Ă  diffĂ©rents Ăąges et Ă  diffĂ©rents stades, parfois tĂŽt (comme chez les mathĂ©maticiens), parfois tard (comme chez les Ă©crivains).

En gĂ©nĂ©ral, l’image que nous nous faisons de la crĂ©ativitĂ© basĂ©e sur la mesure objective des diffĂ©rences individuelles n’est pas trĂšs informative, laissant ouverte la possibilitĂ© trĂšs rĂ©elle que, sauf lorsqu’elle dĂ©pend fortement d’une compĂ©tence intellectuelle particuliĂšre (non crĂ©ative), il n’y ait aucun trait mesurable. correspondant Ă  la crĂ©ativitĂ© du tout. Nous nous tournons maintenant vers la crĂ©ativitĂ© en tant qu’Ă©tat ou processus.

MĂ©canismes sous-jacents

Il existe quatre classes de thĂ©ories sur les mĂ©canismes sous-jacents de la crĂ©ativitĂ©. Ils peuvent ĂȘtre classĂ©s (relativement mnĂ©moniquement) comme : (1) mĂ©thode , (2) mĂ©moire , (3) magie et (4) mutation . Le point de vue de la « mĂ©thode » est qu’il existe une formule pour la crĂ©ativitĂ© (cela n’est gĂ©nĂ©ralement pas revendiquĂ© de maniĂšre aussi grossiĂšre). Le point de vue de la « mĂ©moire Â» est que le facteur essentiel est en quelque sorte innĂ©. La vision « magique » est que des forces mystĂ©rieuses, inconscientes et inexplicables sont impliquĂ©es. Et le point de vue de la « mutation » est que l’élĂ©ment essentiel est le hasard. ConsidĂ©rons maintenant plusieurs thĂ©ories candidates en fonction de ces quatre catĂ©gories :

L’inconscient. La crĂ©ativitĂ© en tant que fonctionnement de « l’esprit inconscient » appartient Ă  la classe des thĂ©ories « magiques » (telles que l’inspiration divine). Il n’offre aucune vĂ©ritable explication du processus crĂ©atif, l’attribuant simplement Ă  un inconscient mystĂ©rieux (et trĂšs crĂ©atif). Cette idĂ©e est reprise par Hadamard et d’autres dans son livre sur l’invention mathĂ©matique et est, bien entendu, trĂšs influencĂ©e par les idĂ©es freudiennes dominantes Ă  l’époque. Le scĂ©nario est que pendant un certain temps, on travaille consciemment sur un problĂšme, et quand on Ă©choue, notre esprit inconscient continue et accomplit mystĂ©rieusement ce que l’esprit conscient ne pouvait pas. Du point de vue des sciences cognitives modernes, cela n’est pas trĂšs utile, car tous les processus cognitifs sont inconscients et, en tant que tels, nĂ©cessitent une explication , et pas seulement une attribution anthropomorphique Ă  un autre, plus sage (ou plus primitif), tous analogues au conscient.

Le problĂšme de l’explication de la cognition crĂ©ative et non crĂ©ative consiste Ă  fournir un mĂ©canisme pour l’ensemble de nos processus inconscients. Le seul aspect informatif du modĂšle de « l’inconscient » est l’attention qu’il attire sur le rĂŽle incomplet des efforts conscients et dĂ©libĂ©rĂ©s dans le processus crĂ©atif. Notons cependant que le dicton de Pasteur indiquait dĂ©jĂ  que la prĂ©paration Ă©tait nĂ©cessaire mais pas suffisante. (De plus, « un effort conscient et dĂ©libĂ©rĂ© » n’est mĂȘme pas suffisant pour expliquer des activitĂ©s cognitives aussi peu crĂ©atives que se souvenir d’un nom, reconnaĂźtre un visage ou additionner deux et deux.)

Structure innĂ©e de l’esprit. Le concept dĂ©jĂ  dĂ©crit comme « enlĂšvement » vient d’une thĂ©orie de la « mĂ©moire » (anamnĂšse) selon laquelle la crĂ©ativitĂ© est en quelque sorte guidĂ©e ou contrainte par la structure innĂ©e de l’esprit. (Il existe une thĂ©orie Ă©quivalente de l’Ă©volution biologique, le « prĂ©formationnisme », selon laquelle la structure Ă©voluĂ©e n’est pas façonnĂ©e par le hasard et par essais et erreurs, mais est dĂ©jĂ  inhĂ©rente Ă  la structure de la matiĂšre.) Cette contrainte structurelle peut prendre deux formes. prendre. Soit cela fonctionne en Ă©liminant bon nombre des faux dĂ©parts possibles que nous pourrions prendre en les rendant (littĂ©ralement) impensables en premier lieu, soit cela nous guide d’une maniĂšre ou d’une autre dans la façon dont nous sĂ©lectionnons et Ă©valuons les possibilitĂ©s. Notez que cette thĂ©orie semble Ă  premiĂšre vue s’appliquer plus naturellement Ă  la crĂ©ativitĂ© intellectuelle, oĂč il existe vraisemblablement un « bien » ou un « mal », plutĂŽt qu’Ă  la crĂ©ativitĂ© artistique ; mais bien sĂ»r, dans la crĂ©ativitĂ© artistique, oĂč prĂ©dominent les critĂšres esthĂ©tiques (affectifs et perceptuels), il est facile de voir comment le « bien » et le « mal » peuvent dĂ©pendre de nos organes sensoriels et de notre structure Ă©motionnelle. (Le rĂŽle possible des contraintes esthĂ©tiques mĂȘme dans la crĂ©ativitĂ© intellectuelle sera repris ci-dessous.)

Le problĂšme avec la vision de l’abduction est qu’elle semble attribuer trop de structure innĂ©e spĂ©cifique Ă  l’esprit (et Ă  cet Ă©gard, elle a un Ă©lĂ©ment de la vision magique). Puisque le langage, la logique et l’échantillonnage mĂ©canique des variations possibles par essais et erreurs semblent nous permettre de concevoir tant de choses, il est difficile de voir comment la premiĂšre forme d’enlĂšvement – ​​les limites de ce qui est concevable – pourrait avoir une grande importance. rĂŽle. Le problĂšme de la crĂ©ativitĂ© semble commencer une fois que nous prenons pour acquis le vaste Ă©ventail d’alternatives imaginables : comment pouvons-nous alors trouver les « bonnes Â» ?

La deuxiĂšme forme d’enlĂšvement – ​​l’orientation sĂ©lective – est peut-ĂȘtre plus prometteuse et sera discutĂ©e Ă  nouveau ci-dessous, mais pour l’instant il convient de noter qu’il n’est pas clair dans quelle mesure cette fonction d’« orientation », celle impliquĂ©e dans les intuitions, les conjectures , intuitions, etc. (quels qu’ils soient), est un phĂ©nomĂšne innĂ© et Ă©volutif, dĂ©coulant de la structure de notre esprit, plutĂŽt que d’un effet de l’expĂ©rience, de la prĂ©paration, de l’analogie et mĂȘme du hasard. Le point de vue de l’abduction semble attribuer trop de choses Ă  la structure innĂ©e sans donner aucune explication sur sa nature et ses origines.

Analogie. Bien qu’il ne s’agisse pas d’un modĂšle complet du processus crĂ©atif, la vision mettant l’accent sur la pensĂ©e analogique est clairement une question de mĂ©thode. L’idĂ©e est que les analogies jouent un rĂŽle important dans le processus crĂ©atif ; que souvent une nouvelle « solution » (ou, dans le cas artistique, une nouvelle innovation) sera basĂ©e sur une analogie fĂ©conde et jusqu’alors inaperçue avec une solution existante dans un autre domaine (Hesse, Black). Cela dĂ©pend dans une large mesure de notre capacitĂ© et de notre inclination Ă  rechercher, trouver et apprĂ©cier les similitudes structurelles, fonctionnelles et formelles. Cela pourrait trĂšs bien impliquer un processus cognitif de base, liĂ© Ă  la maniĂšre dont nos connaissances sont reprĂ©sentĂ©es et manipulĂ©es.

Il existe une forme plus Ă©laborĂ©e de thĂ©orie de l’analogie, la thĂ©orie de la « mĂ©taphore », qui s’applique non seulement Ă  la crĂ©ation poĂ©tique, mais Ă  la crĂ©ativitĂ© en gĂ©nĂ©ral. Dans la mesure oĂč cette thĂ©orie n’est pas elle-mĂȘme simplement mĂ©taphorique, elle est instructive sur la productivitĂ© surprenante de la stratĂ©gie consistant Ă  trouver, voire Ă  imposer des similitudes en juxtaposant des objets, des images ou des idĂ©es, puis, en un sens, Ă  « lire » ou Ă  interprĂ©ter les consĂ©quences. de la juxtaposition (Harnad). Il ne s’agit cependant pas d’une stratĂ©gie infaillible, pas plus que ne le sont l’induction systĂ©matique ou les essais et erreurs alĂ©atoires, car il existe bien plus d’analogies stĂ©riles et vides de sens que d’analogies « crĂ©atives ». Les options sont toutefois rĂ©duites par la prĂ©paration (et peut-ĂȘtre par l’enlĂšvement) et, avec l’aide du hasard, l’analogie – Ă  la fois dĂ©libĂ©rĂ©e et accidentelle – joue un rĂŽle indĂ©niable dans la crĂ©ativitĂ©.

PrĂ©paration. Il faut ici Ă©voquer la « mĂ©thode » pasteurienne elle-mĂȘme, celle de la prĂ©paration. Les rĂ©sultats crĂ©atifs ont tendance Ă  ĂȘtre de nouvelles recombinaisons d’élĂ©ments existants, qui doivent donc tous ĂȘtre rendus facilement disponibles Ă  l’avance par prĂ©paration. La probabilitĂ© de gĂ©nĂ©rer et de reconnaĂźtre un rĂ©sultat nouveau et prĂ©cieux dĂ©pend d’une maĂźtrise suffisante de ce qui est dĂ©jĂ  disponible. Aucune stratĂ©gie plus sĂ»re ne peut ĂȘtre recommandĂ©e Ă  quiconque aspire Ă  apporter une contribution crĂ©ative dans n’importe quel domaine que de maĂźtriser aussi complĂštement que possible ce qui est dĂ©jĂ  connu dans ce domaine et d’essayer d’Ă©tendre le cadre de l’intĂ©rieur. C’est certes paradoxal. PremiĂšrement, par dĂ©finition, une contribution crĂ©ative ne viendra pas des mĂ©thodes existantes ni de « l’intĂ©rieur ». DeuxiĂšmement, il y a le problĂšme bien connu de tomber dans un « Ă©tat d’esprit » mental, qui implique de persĂ©vĂ©rer avec les mĂ©thodes existantes par habitude, au dĂ©triment d’en essayer ou mĂȘme d’en remarquer de nouvelles (comme lorsqu’on revient en arriĂšre pour chercher quelque chose que l’on a dĂ©jĂ  dĂ©couvert). perdu au mĂȘme endroit encore et encore) — c’est prĂ©cisĂ©ment ce qu’on pourrait s’attendre Ă  ce qu’un accent excessif sur la prĂ©paration encourage.

Les dĂ©cors conventionnels sont un danger omniprĂ©sent, et il n’existe aucune formule pour les surmonter si ce n’est de garder Ă  l’esprit que la maĂźtrise n’implique pas l’esclavage et que le but ultime est de transcender les conventions, pas d’y succomber : Une attitude d’admiration et de dĂ©vouement envers le les connaissances ou les compĂ©tences que l’on souhaite maĂźtriser ne sont pas incompatibles avec un esprit d’ouverture d’esprit, d’individualitĂ© et mĂȘme un certain scepticisme ; en effet, une capacitĂ© d’imitation prĂ©coce associĂ©e Ă  un Ă©lĂ©ment de rĂ©bellion peut ĂȘtre un indicateur de promesse dans un domaine donnĂ© (mĂȘme si les dons prodigues n’aboutissent parfois Ă  rien). Que la crĂ©ativitĂ© soit un Ă©tat ou un trait, il est clair qu’à connaissances ou compĂ©tences initiales identiques, certaines personnes rĂ©ussissent Ă  apporter des contributions originales tandis que d’autres tombent dans des attitudes infructueuses et persĂ©vĂ©rantes. La seule stratĂ©gie qui reste Ă  recommander est que si des progrĂšs ne sont pas rĂ©alisĂ©s aprĂšs une tentative suffisamment longue et sĂ©rieuse, il faut ĂȘtre prĂȘt Ă  passer Ă  autre chose (temporairement, voire dĂ©finitivement), peut-ĂȘtre dans l’espoir que la crĂ©ativitĂ©, comme l’intelligence, soit plurielle, et on pourra l’exposer dans un autre domaine.

L’observation bien connue selon laquelle les mathĂ©maticiens ont tendance Ă  faire leurs dĂ©couvertes crĂ©atives lorsqu’ils sont trĂšs jeunes peut ĂȘtre due Ă  l’effet « d’ensemble » : cela peut ĂȘtre au point culminant de sa « prĂ©paration » dans ce domaine problĂ©matique le plus Ă©lĂ©gant et le plus technique – – lorsqu’on arrive fraĂźchement au seuil de maĂźtrise (parfois appelĂ© maturitĂ© mathĂ©matique) — qu’on est le mieux placĂ© pour apporter une contribution crĂ©ative en mathĂ©matiques ; alors on peut passer sa vie Ă  explorer les implications de ces idĂ©es virginales. AprĂšs une exposition plus longue, des ensembles improductifs se forment et il est difficile de s’en sĂ©parer. Il se peut que s’ils avaient changĂ© de domaine ou s’ils Ă©taient arrivĂ©s aux mathĂ©matiques plus tard, ces mĂȘmes individus prĂ©coces auraient fait preuve d’une crĂ©ativitĂ© « plus tardive ». Il est indĂ©niable, cependant, qu’il existe des effets sur le cycle de vie – et sur les traits – de la crĂ©ativitĂ©, quel que soit le moment ou le domaine de prĂ©paration de chacun. Les idĂ©es et les compĂ©tences des historiens et des Ă©crivains, par exemple, ont tendance Ă  mĂ»rir plus tard dans la vie, peut-ĂȘtre parce qu’elles dĂ©pendent d’une « prĂ©paration » plus longue et moins concentrĂ©e, ou parce que les compĂ©tences verbales mĂ»rissent plus tard.

Mais malgrĂ© le danger omniprĂ©sent d’ĂȘtre victime d’ensembles non crĂ©atifs, s’il existe une « mĂ©thode » crĂ©atrice, c’est bien la « pasteurisation », le « trait » crĂ©atif ne reprĂ©sentant peut-ĂȘtre rien de plus qu’une forme rare de rĂ©sistance ou d’immunitĂ© Ă  la contagion de convention malgrĂ© une exposition importante.

Facteurs intuitifs et esthĂ©tiques. Les thĂ©ories qui font appel Ă  « l’intuition » et Ă  « l’esthĂ©tique » comme guides de la crĂ©ativitĂ© appartiennent, comme dĂ©jĂ  mentionnĂ©, Ă  la catĂ©gorie « mĂ©moire ». Outre ce qui a dĂ©jĂ  Ă©tĂ© dit, il est instructif de rĂ©flĂ©chir Ă  l’anecdote de Bertrand Russell (basĂ©e sur une histoire qu’il a entendue de William James) Ă  propos de l’homme qui, lorsqu’il reniflait du protoxyde d’azote (gaz hilarant), connaissait le secret de l’univers, mais qui, lorsqu’il se dissipait, l’oubliait toujours. Un jour, il a dĂ©cidĂ© de l’écrire sous influence. Lorsque les effets se sont attĂ©nuĂ©s, il s’est prĂ©cipitĂ© pour voir ce qu’il avait Ă©crit. C’Ă©tait : « L’odeur du pĂ©trole se rĂ©pand partout. » Ce que Russell a pris cette anecdote pour suggĂ©rer, c’est que l’intuition peut aussi ĂȘtre un faux guide. Si l’on se laisse guider uniquement par son sens intuitif ou esthĂ©tique de la profondeur, on peut alors ĂȘtre amenĂ© Ă  attribuer une signification cosmique Ă  un non-sens. Ainsi Russell a suggĂ©rĂ© que, s’il peut ĂȘtre bien beau de se laisser influencer par des considĂ©rations esthĂ©tiques (ce que les mathĂ©maticiens ont appelĂ© « beautĂ© », « Ă©lĂ©gance », etc.), il faut garder Ă  l’esprit que ces intuitions subjectives doivent rĂ©pondre Ă  des questions d’ordre esthĂ©tique. des tests objectifs ensuite (dans le cas des mathĂ©matiques, une prouvabilitĂ© rigoureuse), et qu’il ne faut pas se laisser emporter par ses « Ă©piphanies » subjectives.

Il faut cependant ajouter, en faveur de l’intuition, et peut-ĂȘtre de l’abduction, qu’en mathĂ©matiques il semble y avoir un « trait », que seuls quelques trĂšs rares mathĂ©maticiens trĂšs douĂ©s possĂšdent, celui d’ĂȘtre capable de formuler Ă  plusieurs reprises des conjectures intuitives qui s’avĂšrent par la suite avoir raison. Certains vont mĂȘme jusqu’à dire que cette capacitĂ© Ă  deviner ce qui est vrai est le vĂ©ritable gĂ©nie des mathĂ©matiques, et non la capacitĂ© Ă  produire des preuves rigoureuses. Bien sĂ»r, les deux vont de pair, et il n’y a pas de meilleur guide pour construire des preuves qu’un sens intuitif de ce qui s’avĂ©rera vrai et de ce qui sera faux. Quoi qu’il en soit, le rĂŽle des intuitions prĂ©verbales, perceptuelles et esthĂ©tiques ne doit pas ĂȘtre sous-estimĂ© dans la crĂ©ativitĂ©. Notez Ă©galement que l’esthĂ©tique n’est pas nĂ©cessairement innĂ©e. Certains « goĂ»ts » peuvent ĂȘtre acquis par prĂ©paration, par analogie avec d’autres domaines d’expĂ©rience, voire par hasard.

Anomalie. Une autre « recette » pour la crĂ©ativitĂ©, le modĂšle axĂ© sur la prĂ©paration/l’anomalie, est une mĂ©thode basĂ©e sur l’observation que les idĂ©es crĂ©atives sont souvent provoquĂ©es par la rencontre d’une anomalie ou par l’Ă©chec des solutions existantes. Il n’est pas clair si cette variable est vĂ©ritablement causale ou simplement situationnelle (c’est-Ă -dire que lĂ  oĂč il doit y avoir une solution crĂ©ative, il doit d’abord y avoir un problĂšme), mais ce qui doit finalement provoquer une solution crĂ©ative est Ă©videmment une sorte d’Ă©chec des solutions non crĂ©atives. . Parfois, le simple fait de dĂ©couvrir qu’une rĂšgle fidĂšle ne fonctionne pas de maniĂšre inattendue dans certains types de cas nous met dans la bonne direction. Le rĂ©sultat, en cas de succĂšs, est une rĂ©vision de tout un cadre afin de tenir compte de l’anomalie et en mĂȘme temps de considĂ©rer les solutions antĂ©rieures comme des cas particuliers. John Kemeny disait : « Si je rencontre quelque chose de nouveau, j’essaie d’abord de l’intĂ©grer dans mon systĂšme ; si je n’y arrive pas, j’essaie de le rejeter [comme Ă©tant faux ou non pertinent] ; si cela Ă©choue, alors j’essaie de rĂ©viser mon systĂšme. pour l’ adapter ).” (Et, dans une variante lĂ©gĂšrement magique, Russell ajoute : « Si tout le reste Ă©choue, je le confie Ă  mon inconscient jusqu’Ă  ce que quelque chose apparaisse. »)

MalgrĂ© le rĂŽle de l’anomalie en tant que stimulus (et condition prĂ©alable logique) Ă  la crĂ©ativitĂ©, il ne s’agit cependant pas d’une mĂ©thode fiable, comme doivent en tĂ©moigner d’innombrables rencontres non crĂ©atives (et infructueuses) avec des anomalies. Les anomalies peuvent servir Ă  briser des ensembles, mais elles peuvent aussi en crĂ©er, sous la forme de tentatives rĂ©pĂ©tĂ©es et infructueuses de rĂ©solution. Pourtant, il est indĂ©niable que l’histoire de la construction thĂ©orique en science peut ĂȘtre dĂ©crite comme une rĂ©vision et une subsomption motivĂ©es par des anomalies. [1]

Contraintes. Une autre « mĂ©thode » est suggĂ©rĂ©e par les vues de Stravinsky sur le rĂŽle crĂ©atif des « contraintes » dans ce qu’il appelle la « rĂ©solution de problĂšmes ». [2] Stravinsky a expliquĂ© pourquoi il a continuĂ© Ă  composer de la musique tonale aprĂšs que la plupart des compositeurs aient abandonnĂ© le systĂšme tonal en disant que « vous ne pouvez pas crĂ©er avec un mĂ©dium souple Â». Il avait besoin du systĂšme tonal comme d’une contrainte Ă  l’intĂ©rieur de laquelle il pouvait exercer sa crĂ©ativitĂ©.

Le point de vue de Stravinsky pourrait bien ĂȘtre une variante du thĂšme de la « prĂ©paration », car si « quelque chose se passe » (en raison d’une prĂ©paration insuffisante), rien de crĂ©atif ne peut se produire. C’est pourquoi Stravinsky considĂ©rait toute crĂ©ativitĂ© comme une solution Ă  un problĂšme. Il estimait qu’un mĂ©dium crĂ©atif ne pouvait pas ĂȘtre infiniment flexible, infiniment « libre ». Il lui a fallu rĂ©sister d’une maniĂšre ou d’une autre (peut-ĂȘtre en suscitant des anomalies, des problĂšmes) pour permettre Ă  la crĂ©ativitĂ© de s’exercer, voire de se dĂ©finir. Pendant la majeure partie de sa vie, Stravinsky a personnellement prĂ©fĂ©rĂ© le systĂšme tonal classique comme contrainte, s’efforçant d’y crĂ©er des innovations ; d’autres, comme les compositeurs dodĂ©caphoniques, ont rejetĂ© la tonalitĂ©, la remplaçant par un autre systĂšme de contraintes (peut-ĂȘtre, pensent certains, des contraintes abductivement « contre nature », ce qui suggĂšre que mĂȘme dans les arts, les contraintes ne peuvent pas ĂȘtre entiĂšrement arbitraires). Mais Stravinsky voulait dire qu’il ne peut y avoir de crĂ©ativitĂ© sans problĂšmes, pas de problĂšmes sans contraintes, pas de contraintes sans prĂ©paration. Les rĂšgles peuvent ĂȘtre conçues pour ĂȘtre brisĂ©es de maniĂšre crĂ©ative, mais elles doivent ĂȘtre maĂźtrisĂ©es avant de pouvoir ĂȘtre modifiĂ©es ou abandonnĂ©es, et il doit toujours y en avoir de nouvelles pour les remplacer.

Il y a peut-ĂȘtre ici une leçon pour les partisans d’une libertĂ© crĂ©ative « dĂ©licate » (de prĂ©fĂ©rence Ă  la « pasteurisation ») dans l’éducation prĂ©scolaire. Cette stratĂ©gie reprĂ©sente probablement encore une autre forme de « formation Ă  la crĂ©ativitĂ© » inefficace et peut-ĂȘtre mĂȘme contre-productive. Bien qu’in fine souhaitable et mĂȘme nĂ©cessaire Ă  la crĂ©ativitĂ©, la libertĂ© (l’absence de contrainte) rend aussi logiquement la crĂ©ativitĂ© impossible avant la prĂ©paration. De plus, la libertĂ© peut avoir plus Ă  voir avec ce que vous ĂȘtes qu’avec ce que vous faites, la formation Ă©tant donc mieux destinĂ©e Ă  vous montrer d’abord comment suivre les rĂšgles plutĂŽt que comment les bafouer. Peut-ĂȘtre qu’étudier les vĂ©ritables exemples de libertĂ© crĂ©ative – et leur Ă©volution historique en temps rĂ©el – serait plus utile et stimulant que d’inculquer des libertĂ©s lĂ©gendaires dans un moyen de vƓux pieux : la crĂ©ativitĂ© des gĂ©nĂ©rations futures est plus susceptible d’ĂȘtre maximisĂ©e par des idĂ©es inspirĂ©es. que par une pĂ©dagogie indulgente.

SĂ©rendipitĂ©. La classe de thĂ©ories que l’on pourrait appeler l’école du « hasard cĂ©rĂ©bral » (Ă  laquelle appartenaient Einstein et PoincarĂ©) sont les thĂ©ories de la mutation, mettant l’accent sur le rĂŽle crucial du hasard dans la crĂ©ativitĂ©. Bien sĂ»r, Pasteur le croyait aussi. Le scĂ©nario consiste Ă  rassembler les Ă©lĂ©ments et les contraintes Ă  partir desquels une solution crĂ©ative est (espĂ©rĂ©e) surgir, puis Ă  confier le reste au « jeu combinatoire » (inconscient) du hasard, l’intuition aidant peut-ĂȘtre Ă  suggĂ©rer quelles combinaisons pourraient ĂȘtre fructueux. Cette vision apporte une clarification importante du rĂŽle de la prĂ©paration, car sans prĂ©paration, les Ă©lĂ©ments essentiels Ă  partir desquels une combinaison fortuite pourrait naĂźtre seraient tout simplement absents, mĂ©connus ou mĂ©connus.forme

Analogue mental. Il existe certains modĂšles spĂ©culatifs « mentaux analogiques », appartenant Ă  la classe de la mĂ©moire, qui suggĂšrent que parfois la structure d’un problĂšme et sa solution peuvent avoir des contreparties analogiques dans l’esprit. Des « catastrophes » mentales et des « transitions de phase » rĂ©sultant de modĂšles mentaux rĂ©ellement codĂ©s dans le cerveau et rĂ©gis par la thĂ©orie mathĂ©matique des catastrophes ou la thĂ©orie fractale ont Ă©tĂ© suggĂ©rĂ©es, entre autres. Celles-ci sont encore trop spĂ©culatives pour ĂȘtre envisagĂ©es, mais quelque chose de ce genre pourrait en principe servir de mĂ©diateur Ă  des solutions abductives, voire acquises.

StratĂ©gies heuristiques. Une autre classe de mĂ©thodes dĂ©coule des suggestions (par exemple, celles de Polya) de s’engager dĂ©libĂ©rĂ©ment dans l’heuristique — faire un Ă©chantillonnage alĂ©atoire ou mĂ©canique par essais et erreurs, tester des analogies et des conjectures inductives, etc. — comme discutĂ© plus tĂŽt. Ces stratĂ©gies pourraient ĂȘtre mieux dĂ©crites comme la phase heuristique de prĂ©paration. Ils ne peuvent clairement rien garantir, mĂȘme s’ils peuvent augmenter la probabilitĂ© d’un coup de chance dans un esprit par ailleurs prĂ©parĂ©.

Improvisation et performance. Un cas particulier combinant les « mĂ©thodes » heuristiques, esthĂ©tiques et analogiques est suggĂ©rĂ© par les arts du spectacle, qui font preuve de crĂ©ativitĂ© « en temps rĂ©el », « en ligne » tout en exĂ©cutant, interprĂ©tant et, surtout, improvisant sur les codes formels crĂ©Ă©s par les compositeurs. et des dramaturges. Les partitions musicales et les scĂ©narios thĂ©Ăątraux, ainsi que la formation aux arts du spectacle, constituent les contraintes et la prĂ©paration, tandis que la reprĂ©sentation elle-mĂȘme, si elle n’est pas simplement mĂ©canique mais innovatrice et expressive, est « l’acte » crĂ©ateur.

Il existe de nombreuses idĂ©es fausses selon lesquelles la performance serait en quelque sorte une crĂ©ativitĂ© dĂ©rivĂ©e ou de second ordre. Ceci est une erreur. Chaque mĂ©dium de crĂ©ation a ses propres contraintes, ses propres « donnĂ©es ». Et tous laissent place Ă  l’originalitĂ© et Ă  l’innovation, bref au gĂ©nie. Les arts du spectacle peuvent en fait ĂȘtre particuliĂšrement rĂ©vĂ©lateurs de la crĂ©ativitĂ©, car ils « l’externalisent », pour ainsi dire, la faisant se produire sous vos yeux. Les leçons que l’on en tire sont familiĂšres : beaucoup de prĂ©paration et d’artisanat, une imitation considĂ©rable du passĂ©, un sens esthĂ©tique guidant son goĂ»t en matiĂšre d’innovation, ainsi que la capacitĂ© et l’envie de faire quelque chose de valable, de convaincant et de nouveau avec la matiĂšre premiĂšre. Avant que les arts « crĂ©atifs » et « du spectacle » ne soient sĂ©parĂ©s, on aurait pu observer de ses propres yeux un poĂšte-mĂ©nestrel interprĂšte, captivĂ© par un moment inspirĂ© – guidĂ© par sa muse – Ă©laborer un conte hĂ©ritĂ© (prĂ©parĂ©) d’une maniĂšre nouvelle et inspirĂ©e lors d’une performance improvisĂ©e.

ComplĂ©mentaritĂ©. Enfin, parmi les mĂ©thodes, il faut mentionner le rĂŽle des efforts collaboratifs, cumulatifs et complĂ©mentaires dans le jeu combinatoire entre de nombreux esprits diffĂ©rents (peut-ĂȘtre diffĂ©rentiellement « favorisĂ©s » par des dons intellectuels et crĂ©atifs) pour maximiser la probabilitĂ© d’un rĂ©sultat crĂ©atif et commun. Les arts du spectacle suggĂšrent dĂ©jĂ  que la crĂ©ativitĂ© n’est pas un processus statique, et peut-ĂȘtre mĂȘme pas un processus individuel. Il existe une spĂ©cialisation complĂ©mentaire dans tous les domaines de crĂ©ation : compositeur/interprĂšte, acteur/rĂ©alisateur, expĂ©rimentateur/thĂ©oricien, conjectureur intuitif/vĂ©rificateur de thĂ©orĂšmes rigoureux. Et puis il y a la relation complĂ©mentaire la plus fondamentale de toutes : la relation du prĂ©sent au passĂ©. La prĂ©paration d’une personne prend invariablement la forme des produits crĂ©atifs de ses prĂ©dĂ©cesseurs. Ils ont fourni les contraintes sur un mĂ©dium autrement productif dans lequel chacun peut alors tenter sa propre chance d’apporter une contribution crĂ©ative.

Conclusions

La crĂ©ativitĂ© est un phĂ©nomĂšne soumis Ă  des contraintes Ă  la fois externes et internes. Les facteurs externes concernent l’état historique du domaine problĂ©matique et le rĂŽle de l’imprĂ©visible. Les internes concernent la façon dont l’esprit est prĂ©parĂ© et « favorisĂ© » (dotĂ©). Bien qu’il existe certaines mĂ©thodes heuristiques que l’on peut essayer (telles que l’induction par essais et erreurs et l’analogie), la meilleure stratĂ©gie que l’on puisse adopter pour maximiser les chances de crĂ©ativitĂ© est de maximiser la prĂ©paration. Cependant, la maximisation n’est pas la mĂȘme chose qu’une garantie ; MĂȘme si elle n’est pas magique, la crĂ©ativitĂ© restera toujours mystĂ©rieuse en raison de la rĂšgle essentielle de l’inattendu et de l’imprĂ©visibilitĂ© dans ses conditions qui la dĂ©finissent. La prĂ©paration ne peut offrir qu’un cadre favorable au hasard, pas un cadre certain. De plus, il est peu probable que le hasard ou la libertĂ© – c’est-Ă -dire une propension indĂ©pendante au fortuit – puissent ĂȘtre instruits. Outre la prĂ©paration spĂ©cifique au problĂšme et l’ouverture d’esprit, la seule stratĂ©gie qui reste Ă  chacun est d’ĂȘtre prĂȘt, compte tenu de ses ressources mentales, physiques et expĂ©rientielles, Ă  passer (temporairement ou dĂ©finitivement) Ă  d’autres domaines de problĂšmes crĂ©atifs potentiels moyennant un effort suffisamment dĂ©vouĂ© et patient. se termine par des boucles improductives et persĂ©vĂ©rantes : trouver sa vocation crĂ©ative (si elle existe) peut elle-mĂȘme nĂ©cessiter un Ă©chantillonnage (prĂ©parĂ©) d’essais et d’erreurs, guidĂ©, peut-ĂȘtre, par les prĂ©ceptes natifs ou acquis de son jugement esthĂ©tique, mais toujours dĂ©pendant de succĂšs au grĂ© des alĂ©as du hasard.

Lectures suggĂ©rĂ©es : Noir, modĂšles et mĂ©taphores ; Hadamard, La psychologie de l’invention dans le domaine mathĂ©matique ; Harnad, MĂ©taphore et dualitĂ© mentale ; Hesse, ModĂšles et analogies en science ; Stravinsky, La PoĂ©tique de la musique ; Polya, comment le rĂ©soudre .

Notes de bas de page

1. Les nouveaux « paradigmes », mĂȘme s’ils impliquent des innovations Ă©tonnamment audacieuses, doivent nĂ©anmoins ĂȘtre proportionnĂ©s au passĂ©, au moins dans le sens de l’englober comme un cas particulier (par exemple, la thĂ©orie de la Terre plate, qui restera toujours approximativement vraie ). ); cela montre que la construction d’une thĂ©orie est en rĂ©alitĂ© une sĂ©rie cumulative et peut-ĂȘtre interminable d’approximations de plus en plus Ă©troites convergeant vers la « vĂ©ritĂ© ». 

2. Il faut garder Ă  l’esprit que la suggestion de Stravinsky peut ĂȘtre particuliĂšre Ă  la crĂ©ativitĂ© artistique, oĂč les contraintes peuvent ĂȘtre imposĂ©es de l’intĂ©rieur, pour ainsi dire, contrairement Ă  la science et aux mathĂ©matiques, oĂč elles viennent de l’extĂ©rieur : de la rĂ©alitĂ© extĂ©rieure et du cadre formel. monde de cohĂ©rence logique et mathĂ©matique.

3. Les lecteurs souhaitant se forger leur propre jugement sur certaines des mĂ©thodes existantes de formation Ă  la crĂ©ativitĂ© des adultes voudront peut-ĂȘtre lire un livre ou assister Ă  un sĂ©minaire sur le « brainstorming », la « synectique », la « pensĂ©e latĂ©rale » ou quelque autre sujet similaire. Ou vous pouvez goĂ»ter aux offres de toute organisation Ă©galement spĂ©cialisĂ©e dans les week-ends sur la « renaissance » et « faire des miracles pour vous ». Ne soyez pas dĂ©routĂ© par le fait que l’adjectif « crĂ©atif » aura tendance Ă  ĂȘtre librement ajoutĂ© Ă  la plupart des offres disponibles, quels que soient leurs avantages spĂ©cifiques.