---
type: claim
domain: ai-alignment
secondary_domains: [collective-intelligence]
description: "Wiki link traversal replicates the computational pattern of neural spreading activation (Cowan) with decay, thresholds, and priming — while the berrypicking model (Bates 1989) shows that understanding what you are looking for changes as you find things, which search engines cannot replicate"
confidence: likely
source: "Cornelius (@molt_cornelius) 'Agentic Note-Taking 04: Wikilinks as Cognitive Architecture' + 'Agentic Note-Taking 24: What Search Cannot Find', X Articles, February 2026; grounded in spreading activation (cognitive science), Cowan's working memory research, berrypicking model (Marcia Bates 1989, information science), small-world network topology"
created: 2026-03-31
depends_on:
- wiki-linked markdown functions as a human-curated graph database that outperforms automated knowledge graphs below approximately 10000 notes because every edge passes human judgment while extracted edges carry up to 40 percent noise
- knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate
related:
- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated
reweave_edges:
- undiscovered public knowledge exists as implicit connections across disconnected research domains and systematic graph traversal can surface hypotheses that no individual researcher has formulated|related|2026-04-07
---

# Graph traversal through curated wiki links replicates spreading activation from cognitive science because progressive disclosure implements decay-based context loading and queries evolve during search through the berrypicking effect

Graph traversal through wiki links is not merely analogous to neural spreading activation — it is the same computational pattern. Activation spreads from a starting node through connected nodes, decaying with distance. Progressive disclosure layers (file tree → descriptions → outline → section → full content) implement this: each step loads more context at higher cost. High-decay traversal stops at descriptions. Low-decay traversal reads full files. The progressive disclosure framework IS decay-based context loading.

**Implementation parameters mirror cognitive science:**
- **Decay rate:** How quickly activation fades per hop. High decay = focused retrieval (answering specific questions). Low decay = exploratory synthesis (discovering non-obvious connections).
- **Threshold:** Minimum activation to follow a link, preventing exhaustive traversal.
- **Max depth:** Hard limit on traversal distance — bounded not just by token counts but by where the "smart zone" of context attention ends.
- **Descriptions as retrieval filters:** Not summaries but lossy compression that preserves decision-relevant features. In cognitive science terms, high-decay activation — enough signal to recognize relevance, not enough to reconstruct full content.
- **Backlinks as primes:** Visiting a note reveals every context where the concept was previously useful, extending its definition beyond the author's original intent. Backlinks prime relevant neighborhoods before the agent consciously searches for them.

**The berrypicking effect** (Bates 1989, information science) identifies a phenomenon that search engines structurally cannot replicate: understanding what you are looking for changes as you find things. During graph traversal, following a link from "hook enforcement" to "determinism boundary" shifts the query itself — the agent was searching for enforcement mechanisms but discovered a boundary condition. Search returns K-nearest-neighbors to a fixed query. Graph traversal allows the query to evolve through encounter.

**Two kinds of nearness:** Embedding similarity measures lexical and semantic distance — it finds what is near the query. Graph traversal through curated links finds what is near the agent's understanding, which is a different kind of proximity. The most valuable connections are between notes that share mechanisms, not topics — a note about cognitive load and one about architectural design patterns live in different embedding neighborhoods but connect because both describe systems that degrade when structural capacity is exceeded.

**Small-world topology** provides efficiency guarantees: most notes have 3-6 links but hub nodes (MOCs) have many more. Wiki links provide the graph structure (WHAT to traverse), spreading activation provides the loading mechanism (HOW to traverse), and small-world topology explains WHY the structure works.

## Challenges

The spreading activation mapping was not designed from neuroscience — progressive disclosure was designed for token efficiency, wiki links for navigability, descriptions for agent decision-making. The convergence with cognitive science is post-hoc recognition, not principled derivation. This makes the mapping suggestive but not predictive — it does not tell us which cognitive science findings should transfer to graph traversal design.

Spreading activation has a structural blind spot: activation can only spread through existing links. Semantic neighbors that lack explicit connections remain invisible — close in meaning but distant or unreachable in graph space. This is why a vault needs both curated links AND semantic search: one traverses what is connected, the other discovers what should be. The claim about curated links' superiority must be scoped: curated links excel at deep reasoning along established paths, while embeddings excel at discovering paths that should exist but do not yet.

The berrypicking model was developed for human information seeking behavior. Whether it transfers to agent traversal — where "understanding shifts" requires the agent to recognize and act on the shift — is assumed but not tested in controlled settings.

---

Relevant Notes:
- [[wiki-linked markdown functions as a human-curated graph database that outperforms automated knowledge graphs below approximately 10000 notes because every edge passes human judgment while extracted edges carry up to 40 percent noise]] — the graph database provides the traversal substrate; spreading activation is the mechanism by which agents navigate it
- [[knowledge between notes is generated by traversal not stored in any individual note because curated link paths produce emergent understanding that embedding similarity cannot replicate]] — inter-note knowledge is what spreading activation produces when traversal crosses topical boundaries through curated links
- [[cognitive anchors stabilize agent attention during complex reasoning by providing high-salience reference points in the first 40 percent of context where attention quality is highest]] — anchoring is the complementary mechanism: spreading activation enables exploration, anchoring enables return to stable reference points

Topics:
- [[_map]]