If the earlier posts have shown how meaning can appear without semiosis, and how “signals” can be re-described as value-mediated patterns of coordination, the remaining pressure point lies deeper in the system.
Not at the level of behaviour alone, but at the level of neural organisation itself.
Neural systems are often treated as if they implement representations:
encoding inputs
transmitting information
computing over internal symbols
On this view, the brain becomes a kind of interpreter of signals.
But if value alone can account for coordination at the behavioural level, we must ask whether neural activity itself requires meaning—or whether it too can be understood without invoking semiosis.
Neural activity as differential sensitivity
At a basic level, neurons exhibit:
sensitivity to inputs
thresholds for activation
patterns of firing that vary with conditions
plasticity shaped by prior activity
These properties allow neural systems to:
respond selectively
adapt over time
and stabilise certain patterns of activation
Crucially, none of this requires that neural states stand for anything.
They need only vary in ways that are differentially reinforced within the system.
Value at the neural scale
What guides the organisation of neural activity is not meaning, but value in the biological sense:
certain activity patterns lead to system-level stability
others lead to destabilisation or inefficiency
some patterns are reinforced through feedback mechanisms
others are suppressed
Over time, this produces structured neural dynamics in which:
some pathways become more probable
some responses become more readily activated
and some patterns of activity become stabilised
These dynamics can be described entirely in terms of:
differential reinforcement across patterns of neural activity
No representational content is required to explain this process.
Learning without representation
Learning is often framed as the acquisition of representations.
But from a value-based perspective, learning can be described as:
the reconfiguration of response tendencies
the adjustment of thresholds and connectivity
the stabilisation of certain patterns over others
Synaptic changes do not need to encode propositions about the world.
They need only adjust the system’s responsiveness in ways that improve its continued viability within its environment.
Patterns as outcomes, not carriers
A common temptation is to treat neural firing patterns as carriers of information.
But an alternative description is available:
patterns are not carriers of meaning
they are outcomes of dynamic interactions within the system
and their stability reflects the history of value-based selection within neural processes
In this view:
a firing pattern is not a message
it is a configuration that participates in ongoing system dynamics
Its significance lies in what it does within the system, not in what it represents.
The absence of an internal interpreter
Representational accounts often imply an internal interpreter:
some part of the system that “reads” neural states as meaningful
or decodes signals into actionable content
But if neural dynamics are already sufficient to account for behaviour through value-based processes, then:
no additional interpretive layer is required
and no internal homunculus is needed to assign meaning to neural states
The system operates through continuous interaction, not symbolic translation.
From coding to coupling
In computational metaphors, neural activity is often described as encoding and decoding.
In the value-based framing, this is replaced by:
coupling between neural populations
propagation of activity through interconnected networks
and modulation of responsiveness through feedback
What appears as “information flow” can be re-described as:
the structured propagation of activity shaped by differential sensitivity and reinforcement
This avoids the need to posit that neural states carry semantic content.
Why representation seems attractive
The representational description persists because it is intuitively appealing:
neural patterns correlate with external conditions
internal states change in ways that track environmental variables
and these changes support adaptive behaviour
From this, it is tempting to infer:
the neural system must be representing those variables internally
But as with the behavioural cases, correlation does not entail representation.
It indicates alignment between system dynamics and environmental structure, not necessarily symbolic encoding.
Value as the organising principle
Across levels, a consistent principle emerges:
at the behavioural level, value explains differential action
at the neural level, value explains differential activation and plasticity
across the system, value stabilises patterns that support continued functioning
Neural systems are organised around what works for the system, not around what is symbolised.
Meaning, in the semiotic sense, is not required to account for this organisation.
Reframing “neural content”
If neural states are not representations, what are they?
They are:
configurations within a dynamic system
shaped by constraints and feedback
and sustained by their role in maintaining system-level viability
Their “content,” if the term is used at all, is not semantic.
It is functional.
No translation required
In representational accounts, a chain is often implied:
external world
sensory input
neural encoding
internal representation
interpretation
action
In the value-based account, this chain collapses:
sensory inputs perturb the system
neural dynamics evolve under those perturbations
and actions emerge from the resulting configurations
There is no stage at which meaning must be inserted.
Closing the loop without meaning
Neural systems close loops through:
feedback
reinforcement
and recurrent connectivity
These loops allow the system to adapt and stabilise without requiring internal symbols that refer to external states.
The system does not need to “know” what is happening in the world in a semantic sense.
It needs to remain coordinated with it through continuous interaction.
Transition
If neural organisation itself can be accounted for without invoking meaning, then the explanatory scope of semiosis narrows further.
We have seen that:
behavioural coordination can be described without signals
hard cases do not compel a semiotic interpretation
and neural dynamics can be explained through value without representation
The remaining task is to bring these threads together.
The next post will address the conceptual consolidation:
what it means to consistently describe living systems without appealing to meaning—and what this does to the notion of “information” itself.
No comments:
Post a Comment