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😴 Habituation — Anti-Filter Bubble

Biological Analog: Habituation is the simplest form of learning — a decrease in response to a stimulus after repeated presentations. You stop hearing the ticking clock after a few minutes. The brain allocates attention to novel stimuli, not repeated ones. This prevents sensory overload and enables adaptation.

The Problem

Without habituation, an AI agent repeatedly recalls the same "most relevant" memories — creating a filter bubble. If memory A has the highest similarity score, it dominates every recall, crowding out potentially useful but slightly-less-similar memories.

Query 1: "database issues" → [A, B, C, D, E]     ← A dominates
Query 2: "database issues" → [A, B, C, D, E]     ← Same results!
Query 3: "database issues" → [A, B, C, D, E]     ← Filter bubble

With Habituation

Query 1: "database issues" → [A, B, C, D, E]     ← Fresh results
Query 2: "database issues" → [B, C, A, D, F]     ← A drops, F emerges
Query 3: "database issues" → [C, F, B, G, D]     ← New memories surface

How It Works

The habituation system tracks recall frequency per memory ID and applies an exponentially increasing penalty:

flowchart LR
    RECALL["Memory recalled"] --> TRACK["Track recall count<br/><i>per memory ID</i>"]
    TRACK --> COMPUTE["Compute penalty<br/><b>penalty = rate ^ (count - 1)</b>"]
    COMPUTE --> APPLY["Adjust score<br/><b>score × penalty</b>"]
    APPLY --> RESULT["Reduced score<br/>→ drops in ranking"]

    style RECALL fill:#4a90d9,color:white
    style APPLY fill:#e74c3c,color:white
    style RESULT fill:#f39c12,color:white
\[\text{penalty}(n) = \text{rate}^{(n-1)}\]

Where \(n\) is the number of times this memory has appeared in recall results and \(\text{rate}\) is the configurable decay rate (default: 0.85).

Penalty Curve

Recall # Penalty (rate=0.85) Effect
1st 1.00 Full score
2nd 0.85 15% reduction
3rd 0.72 28% reduction
5th 0.52 Half score
10th 0.20 80% reduction
20th 0.04 Nearly eliminated

Decay Rate Configuration

The default decay rate of 0.85 provides a balance between novelty and relevance. A higher rate (0.95) creates a gentler penalty — useful when the agent genuinely needs to recall the same memory frequently. A lower rate (0.70) aggressively surfaces new content.

Where It Fits in the Pipeline

Habituation is applied after the 6-phase scorer produces results, but before final ranking:

flowchart TD
    SCORER["6-Phase Scorer<br/><i>produces top-K candidates</i>"] --> SUPPRESS["Step 4: Suppression Filter<br/><i>remove explicitly blocked</i>"]
    SUPPRESS --> HAB["Step 5a: Habituation Penalty<br/><i>attenuate repeated results</i>"]
    HAB --> GRAPH["Steps 5c–5e: Graph Augmentation<br/><i>Hebbian, Temporal, Entity</i>"]
    GRAPH --> SORT["Final Sort → Top-K"]

    style HAB fill:#e74c3c,color:white
    style SORT fill:#00b894,color:white

Key: The penalty multiplies the score field — it doesn't modify the underlying memory. Habituation is a recall-time effect, not a storage-time effect.

Interaction with Other Systems

System Interaction
Reconsolidation Habituation reduces recall score, but reconsolidation increases the memory's durability. A frequently-recalled memory resists temporal decay (fewer buckets) but gets a lower score on repeated queries.
Surprise Detection New, surprising memories start with high importance and no habituation penalty — they naturally dominate initial queries.
Suppression If a memory is fully suppressed, habituation is irrelevant — it's excluded at Step 4 before habituation is applied.

Next Steps