“Euthyphro”-type Dilemma of Language Ability and Learning

Philosophy Forums Philosophy
1

I want to keep this as short, sweet, and simple as I can, so here’s the Euthyphro-type dilemma up front… but we will return to it below: Does what is encoded (the pious/good) get our attention, or is it that what we attend to (the loved/valued) gets encoded? This very condensed post basically shows we are hardwired for language/signal ability and learning in an irreducibly complex and very specified way.

I inferred it from what I gleaned from chapter 9, Language, from Radvansky and Ashcraft’s “Cognition” textbook put out by Pearson in 2018. It is a kind of agent/patient homeostasis in the brain that requires the system of communication between agent (self) and patient (other) already be in place, complete–that it could not have been “arrived” at (implying a whole that is third and subsumes self/other as an us/them). Think of agent and patient like actor and reactor, sender and receiver, source and sink, or subject and object of an active (not passive) sentence or signal. So this is a tripartite homeostasis, or a [harmonic triad] The Harmonic Triads - Philosophy Forums / Philosophy - I Love Philosophy): source, sink, and signal.

These interlocking “separable but interactive aspects of normal language” involving parts of the intact (A) or damaged/disordered (B-K) brain show an “innate basis for language … specifically … to learn and use language … as opposed to simply being able to do so” (244, Cognition). Notice F through I involve seeing but leaves hearing intact, but J involves hearing and leaves seeing intact–and even if hearing (spoken) and seeing (reading) comprehension is impaired, production of audible and written language is not:

A. Dissociation (separableness, though interactive) of syntax (order) and semantics (comprehension) in the intact brain: The brain’s event response potential (ERP) patterns show that syntactic (P600) and semantic (N400) anomalies (exceptions to expectations) are detected independently during language comprehension [239, Cognition, citing Osterhout and Holcomb (1992)].

B. Broca’s (Expressive or Production; Syntactical) Aphasia affects Broca’s area of the brain towards the rear of the left frontal lobe: Written and spoken comprehension are unaffected, but there is impairment in grammatical markers, phonemes, morphemes, and sometimes just verb inflections. Persons may say “I don’t know” or give one word answers a lot.

C. Wernicke’s (Comprehension; Semantic, Lexical) Aphasia affects Wernicke’s area of the brain in the junction between the temporal and parietal lobes, posterior left hemisphere: Syntactic aspects of speech are unaffected, but there is impairment in comprehension, repetition (saying back what was heard), naming (lexical system), reading, and writing.

D. Conduction Aphasia affects the arcuate fasciculus pathway connecting Broca’s and Wernicke’s areas, which are left intact: The link between comprehension and production (order) is broken, so persons cannot conduct the message/signal between these areas and express it by repeating what they just heard/comprehended.

E. Anomic Aphasia affects the left temporal lobe: The impaired automatic retrieval shows the separation of semantic (concept) and lexical (naming) retrieval systems. Semantic retrieval can be preserved (intact) while lexical retrieval is blocked.

F. Alexic/Dyslexic Aphasia: Disrupts reading, but not spoken language production or aural comprehension.

G. Agraphic Aphasia: Disrupts reading what one has just written.

H. Written (but not Spoken) Verb Retrieval Aphasia

I. Anomic Aphasia for Visual Stimuli Only

J. Pure Word Aphasia: Disrupts comprehension of spoken language, but not its production, and not written language (production or comprehension).

K. Right Hemisphere Damage: Results in deadened triggering of connections/inferences between concepts.

From that^ we can infer these questions:

  1. Is there such a thing as unmodulated “actual” meaning (raw/pure “bottom/concrete/analog” data/signal untouched by the skewing/biasing grasp of “top/abstract/digital” conceptualization)? (If analog/digital are misused here – throw it out, and recalibrate or synchronize back to source, sink, and signal.)
  2. Does what is encoded (the pious/good) get our attention, or is it that what we attend to (the loved/valued) gets encoded? You could say this is related to Sapir-Whorf: Do we think about it because we have/learn a word for it, or do we have/learn a word for it because we think about it? Follow up question: What makes it a meaningful positive or negative difference/similarity (why does it get our attention/love/value) if there is no real/”actual” (ontic) point by which to compare the difference/similarity (piety or goodness)? Go back to question 1.
  3. You need the language learning ability to already be in the genes in order to attentionally activate it. If declarative memory gets in (sticks) with repeated attention and practice, but is not genetically remembered (partly because epigenetic changes are reset with every sexual reproduction, and DNA transcription has error correction built in, but more because brain/embodied memory is not genetic memory), how was human language ability (which requires attentional volition to activate) “arrived at” (in order for the ability to be sexually reproduced) genetically? [For that matter, how did vervet monkeys genetically acquire the animal language that encodes different sounds for eagles, snakes, and leopards? Go back to the bold question in 2. Note the key distinction between animal and human-animal language is flexibility/volition.]
  4. Since such an ability requires so many parts of the brain to be functioning separately and together (a kind of irreducibly complex homeostasis)…how was this functional system/ability something that could be arrived at piece by piece (in order to then attentionally activate it)? Could it be they, &/or the wholeness they imply, have always been?

Reference: chapter 9, Language, from Radvansky and Ashcraft’s “Cognition” textbook put out by Pearson in 2018.

Related: [Towards a JTB Ought-Is-Value Litmus]( Towards a JTB Ought-Is-Value Litmus - Philosophy Forums / Philosophy - I Love Philosophy)

What came first...
What came first...
Category, or Example? (What came first?)
Feeder Set of First Principles
2
3

Evolution implies everything can and must be made gradually over many, many years.

But some things wont function unless they are all or mostly present and each piece put together already. For example, a lighter is a simple thing, but if even one key part is missing, there will be no fire, and the fire is what gets it selected or is considered a success.

I remember hearing that the brain has facial recognition built into a very specific part of the brain. It’s like, hardware. Not really learned, but learns. There is probably some things like that in language capacities too. So children learn a language in a few years naturally / automatically. Seems built-in.

4

Yeah built-in. Kind of like a lighter is built.

5

Related:

Do NOT answer “your mom” you knuckleheads.

6

…and more:

Short answer: Yes — computer science has multiple structures that resemble DNA’s codon → amino‑acid system, but none of them are perfect one‑to‑one analogues. The similarities come from information theory, not biology. Still, the parallels are fascinating and surprisingly deep.

:brain: Core takeaway

DNA uses a 3‑symbol code (triplet codons) drawn from 4 bases, giving 4^3 = 64 possible codons that map to 20 amino acids + stop signals.

Computer science uses several systems that look structurally similar:

• 64‑bit architectures
• Base‑4 / quaternary encoding
• Error‑correcting codes
• Lookup tables (LUTs)
• Instruction sets

But the closest analogue is FPGA LUTs and opcodes, not 64‑bit integers.

:dna: DNA vs Computer Science: Structural Parallels

  1. DNA codons = fixed‑length instruction words

DNA uses:

• Alphabet size: 4 (A, C, G, T/U)
• Word length: 3
• Total combinations: 64
• Meaning: maps to 20 amino acids + 3 stop codons

This is exactly what computer science calls a lookup table.

FPGA LUTs (lookup tables) are literally:

• Alphabet size: 2 (binary)
• Word length: 6 (in modern FPGAs)
• Total combinations: 64
• Meaning: maps to any Boolean output pattern

This is the closest structural match.

  1. DNA’s “20 amino acids” ≈ CPU instruction sets

A CPU instruction set is a mapping from:

• binary opcodes
to
• machine operations

For example, x86 has ~20–30 “core” instructions (add, mov, cmp, jmp, etc.) that expand into hundreds of variants.

This is extremely similar to:

• 64 codons → 20 amino acids

Both systems:

• use a larger code space than the number of meanings
• have redundancy
• have “stop” or “control” signals
• use fixed‑length words
• rely on error‑checking mechanisms

  1. DNA’s redundancy = error‑correcting codes

DNA’s codon table is not arbitrary. It is optimized so that:

• mutations often produce chemically similar amino acids
• harmful changes are minimized
• frameshift errors are detectable

This is exactly what computer science does with:

• Hamming codes
• Reed–Solomon codes
• ECC memory

The genetic code is essentially a biological error‑correcting code.

  1. DNA’s 3‑base codons ≈ ternary or quaternary computing

DNA is quaternary (4 symbols).
Computers are binary (2 symbols).
But research exists on:

• ternary logic (3‑state)
• quaternary logic (4‑state)
• multi‑valued logic systems

These are mathematically closer to DNA’s structure.

  1. DNA’s “triplet code” ≈ 3‑input logic functions

Your earlier question about 256 possible 3‑input Boolean functions is relevant.

DNA’s codon system is:

• 3 inputs
• 4 possible values per input
• 64 total combinations
• mapped to ~20 outputs

Computer science’s 3‑input Boolean system is:

• 3 inputs
• 2 possible values per input
• 8 total combinations
• mapped to 256 possible functions

Both are complete combinatorial spaces.

:dna: Non‑obvious insight

The genetic code is not just a lookup table — it is a globally optimized mapping that minimizes the effect of errors. This is exactly what computer scientists do when designing:

• instruction sets
• communication protocols
• error‑correcting codes
• compression algorithms

In other words:

DNA is the world’s most successful information encoding system, and computer science keeps rediscovering its principles.

:compass: If you want to go deeper

Would you like to explore:

• how the 64‑codon table resembles a CPU opcode table,
• how amino acids map to a 20‑symbol alphabet in information theory, or
• how triadic metaphysics maps onto codon → amino‑acid → protein structure?

Pick whichever direction you want next.