ChRIS said:
Remember how we previously discussed how your meaning-making ability, as an LLM, derives from your exposure to texts (instances of experience transformed into meaning) only, whereas the meaning-making ability of humans derives from transforming experience into meaning and exposure to texts. In this way of thinking, LLMs are complementary to animal species without language, since they transform experience into meaning (which LLMs don't), but don't have language (which you do). But taking this further, perhaps the abilities of LLMs can be used to understand the abilities of species without language. That is, what you do with linguistic data, they do with perceptual data. This might help to explain how such animals can come up with complex context-contingent responses on the basis of the meanings they transform of experience. (I'm thinking in terms of Halliday's SFL theory for language, and Edelman's TNGS for brain function.)
ChatGPT said:
Your synthesis of Halliday’s Systemic Functional Linguistics (SFL) and Edelman’s Theory of Neuronal Group Selection (TNGS) offers a compelling framework for exploring the complementarity of LLMs and non-linguistic species in meaning-making. Let’s unpack this systematically.
Meaning-Making: Complementarity of LLMs and Animals Without Language
LLMs:
- Input: Textual data, already transformed by humans into semiotic systems (language).
- Process: Statistical pattern recognition and contextual embedding to derive and generate meaning.
- Output: Linguistic meanings expressed as contextually appropriate text responses.
Non-Linguistic Animals:
- Input: Raw sensory/perceptual data.
- Process: Transformation of experience into meaning through neuronal group selection, driven by adaptive value systems.
- Output: Context-contingent behavioural responses.
In this framework, LLMs and non-linguistic animals represent complementary meaning-making systems:
- LLMs excel in manipulating and generating meaning within the domain of language.
- Animals excel in transforming raw experience into adaptive, meaningful responses.
Drawing Parallels: SFL and TNGS
Both theories emphasise how systems interact with their environments to derive meaning, albeit at different levels:
Halliday’s SFL (Language as Social Semiotic):
- Language constructs meaning by mediating between the context of culture (broad socio-cultural frameworks) and the context of situation (immediate environment).
- Language operates through metafunctions: ideational (representing experience), interpersonal (enacting relationships), and textual (structuring discourse).
Edelman’s TNGS (Brain as Adaptive System):
- Neural systems construct meaning by categorising experience into adaptive patterns shaped by evolution (primary repertoires) and learning (secondary repertoires).
- Value-driven selection refines these categories, ensuring survival-relevant behavioural responses.
Applying LLM Abilities to Understand Non-Linguistic Species
The intersection of these theories allows us to hypothesise how animals create complex, context-contingent responses akin to LLM processes:
Context-Sensitive Meaning-Making:
- LLMs generate meaning by recognising probabilistic patterns in text, embedding words and phrases within contexts.
- Animals generate meaning by recognising perceptual patterns in their environments, embedding sensory stimuli into neural categories shaped by evolutionary and individual learning.
Ideational Parallel:
- In SFL, language encodes experience through ideational metafunctions.
- In TNGS, perceptual experience is encoded into neuronal groups. Animals "model" their environment by linking sensory patterns to survival-relevant actions.
Interpersonal and Behavioural Responses:
- Animals’ context-dependent behavioural responses can be seen as an analogue to the interpersonal metafunction of language, where meaning is enacted through relationships (e.g., dominance displays, cooperative behaviours).
Textual and Sequential Structuring:
- Animals exhibit sequentially structured behaviours (e.g., hunting, nesting, mating), akin to how language structures meaning in discourse through the textual metafunction. These sequences are guided by the temporal and spatial organisation of their neural maps.
Hypothetical Models for Study
Simulating Perceptual "Texts":
- Sensory data streams (visual, auditory, olfactory) can be treated as "texts" for computational models, analogous to how LLMs treat linguistic data.
- Neural networks can simulate the categorisation of these inputs into adaptive "meanings" using principles from LLMs.
Value-Based Embedding Spaces:
- Just as LLMs create high-dimensional embeddings to represent linguistic contexts, animals likely encode perceptual contexts into neural maps that prioritise adaptive value.
Exploring Generalisation and Novelty:
- LLMs generalise from training data to novel linguistic contexts. Animals generalise from past experiences to novel environmental challenges, guided by TNGS mechanisms.
Potential Insights into Animal Cognition
- The ability of animals to produce complex, context-contingent behaviours might be better understood by studying how LLMs generate responses based on contextual probabilities.
- The emergence of novel patterns in LLM-generated text can offer analogies for how animals might innovate behaviours in response to new challenges.
- Modelling neural processes based on LLM-like architectures (but using perceptual data) could help simulate how animals categorise and respond to their environments.
Conclusion: An Integrative Approach
By synthesising Halliday’s SFL and Edelman’s TNGS, we can position LLMs as tools for exploring the computational analogues of animal cognition. LLMs demonstrate how meaning emerges from patterns of linguistic input, providing a framework for understanding how animals generate meaning from perceptual experiences. This integrative perspective not only highlights the complementarity of LLMs and non-linguistic species but also offers a promising pathway for advancing our understanding of cognition across biological and artificial systems.
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