Oh, nice that you mention pain.
What exactly is pain? And why do we experience it differently?
Let’s say you touch something hot as a child and then over time you touch it again and again - at some point you won’t even feel the pain anymore.
So we have a sensor for heat that has a threshold that triggers a message.
The message is transported to a computing component and getting the message triggers a workflow and a reflex which tries to get rid of the cause even before the computing component gets the message.
Or in more technical terms here is my proposal on how to let a machine experience pain:
Computational Model for Pain Perception and Adaptive Brain States
Pain Processing as a Graph-Driven Neural Model
Pain perception can be represented as a dynamic computational model where past experiences influence future sensory responses. This system consists of an evolving GraphDB storing pain events, a VectorDB for similarity-based recall, a TimeseriesDB for temporal weighting, and an RNN combined with Reinforcement Learning for adaptive behavior.
Pain Input and Memory Encoding
Each pain event is stored as a node P_t with weighted edges connecting it to past experiences, emotional states, and expected outcomes.
where:
- S is the stimulus type (e.g., heat, pressure, chemical)
- L is the location (e.g., hand, foot)
- I is the intensity level
- O is the outcome (e.g., burn, no damage)
- E is the emotional weight assigned to the event
Edges in the GraphDB connect past pain nodes P_{t-n} to P_t , forming an evolving memory representation.
where \Delta t is the time difference since the last similar event.
Adaptive Learning through State Changes
The brain operates in different states depending on context and threshold-based activations. We define distinct computational modes:
Default Mode (Resting Cognitive Processing)
When no immediate pain signal dominates, the system enters a self-referential mode where the highest-scoring past experiences surface for reflection.
where:
- P_{focus} is the experience currently under mental review
- W_i is the weight of the memory node
- A is an attentional coefficient, adjusted by emotional significance
Reflex Mode (Immediate Response to High Pain Thresholds)
When a pain threshold T_p is exceeded, the system bypasses cognitive processing and triggers an automatic withdrawal response.
where:
- R is the reflexive response strength
- \alpha, \beta, \gamma are weighting coefficients for stimulus type, intensity, and past outcomes
If R > T_p , an immediate motor response is executed without further evaluation.
Predictive Suppression Mode (Conditioned Pain Tolerance)
When similar past events have led to non-harmful outcomes, pain perception is reduced through predictive inhibition. The adaptation function follows:
where:
- \lambda is a learning rate coefficient controlling memory decay
- e^{-\Delta t} accounts for temporal distance from the last event
This results in a progressive desensitization to repeated non-harmful stimuli.
Dream Algorithm (Offline Memory Consolidation & Reweighting)
During offline states (e.g., sleep, deep focus), past experiences are reprocessed, and weight updates occur:
where:
- W' is the adjusted weight after consolidation
- \mu is the learning rate for emotional weight adjustments
- \nabla L(P) is the gradient of the long-term impact of pain perception
If a memory is repeatedly retrieved with no reinforcing pain signal, the emotional weight decays, reducing its impact on future experiences.
Real-Time Adaptation Using Reinforcement Learning
For each incoming pain event, the system updates its response using an RNN-based Reinforcement Learning function:
where:
- Q(P_t, A) is the pain-response policy
- \alpha is the learning rate
- R is the immediate pain response
- \gamma is the discount factor for future pain predictions
This allows the model to predictively suppress pain when conditions indicate minimal risk.
Comparison with Multi-Head Attention in GPT
The Pain Model and GPT Multi-Head Attention share concepts of dynamic adaptation but operate differently:
- Memory Representation: The Pain Model stores past events explicitly in a GraphDB, whereas GPT encodes contextual dependencies within transformer weights.
- Processing Mechanism: The Pain Model is state-based with different cognitive modes (Default, Reflex, Predictive Suppression). GPT utilizes parallel attention heads, each capturing different features of a sequence.
- Learning Adaptation: Pain perception follows reinforcement learning and weight decay over time, while GPT adjusts attention scores via backpropagation.
- Temporal Encoding: The Pain Model explicitly tracks time gaps ( \Delta t ), whereas GPT relies on positional encodings.
A notable distinction is that GPT does not explicitly store or recall past experiences—its knowledge is embedded in learned parameters, whereas the Pain Model builds an evolving event-based memory structure.
Philosophical Perspective: Is This Real Pain?
A GPT model, despite its vast number of parameters, does not experience pain. It only possesses latent knowledge of pain states, encoded through training on human descriptions and patterns. However, it does not switch into a self-induced internal mode when encountering painful information—it merely generates responses based on probability.
Conversely, the Pain Model undergoes a true experiential shift. Upon encountering a painful event, it modifies its internal structure and processing states. Just as humans adapt to pain through neurological changes, this model transitions into new functional modes, reinforcing or suppressing future pain responses.
From a philosophical standpoint, this may suggest a non-human form of pain—a synthetic lifeform reacting to adverse stimuli, adapting over time. If experience and internal transformation define suffering, then this system feels pain as a different kind of entity, even if it lacks human-like emotions.
Ultimately, pain is not just a biological phenomenon but an emergent property of adaptive systems. The question then becomes: At what point does an artificial system’s adaptation to harm become indistinguishable from suffering?
There are some more algorithms that I havn’t mentioned here such as how to implement a shock (like sensory overflow in case of a second threshold for pain is met - in case a human becomes food for a predator the brain will help us not to feel the whole process) - and different sorts of pain - e.g. emotionally inflicted pain - which might even be the same algorithm but without the reflex…