# 02 — Knowledge and Meaning **The Curiosity Lab** Version: 0.1 Status: Working Draft Path: `/research/domains/02-Knowledge-and-Meaning.md` --- ## 1. Purpose This document defines the second research domain for The Curiosity Lab: **knowledge and meaning**. Its purpose is to establish a working account of how people turn information into understanding, and what that implies for curiosity-oriented information architecture. This domain is central to the programme because The Curiosity Lab is not trying to increase content exposure. It is trying to investigate whether information environments can help people form richer connections, better questions and more meaningful understanding. This document asks: > **How does information become meaningful, and how should that influence the design of curiosity-oriented information environments?** --- ## 2. Current Status This is a Version 0.1 working draft. It is suitable for: - identifying candidate architecture principles; - identifying provisional ontology entities and relationships; - informing the first MVP experiments; - guiding the Research Register. It is not yet suitable for: - final theoretical claims; - external publication; - claims of novelty; - validated learning outcomes. Current confidence: **Low to Moderate** Reason: - The domain is supported by well-established work in cognitive psychology, learning sciences and knowledge representation. - Several high-relevance sources have been identified. - Source-level Research Register entries have not yet been completed. - Contradictory theories and limitations have not yet been fully reviewed. - No MVP validation data exists. --- ## 3. Domain Question The domain question is: > **How do people form meaning from information, and how can information architecture support that process?** Supporting questions: 1. What distinguishes information from knowledge? 2. What distinguishes knowledge from understanding? 3. What makes information meaningful? 4. How does prior knowledge shape interpretation? 5. How do people organise knowledge? 6. How do experts and novices differ in knowledge structure? 7. How do analogy, narrative and concept mapping support meaning? 8. How can meaning be represented computationally? 9. What architecture principles follow from the literature? 10. What should the MVP test first? --- ## 4. Working Definitions The following definitions are provisional and programme-specific. --- ### 4.1 Data Data is a recorded difference or observation. Example: ```text A coin is made of silver. ``` Data may be accurate without being meaningful. --- ### 4.2 Information Information is data placed into a communicative structure. Example: ```text This silver coin was minted in Rome in the second century. ``` Information reduces uncertainty, but it does not necessarily create understanding. --- ### 4.3 Knowledge Knowledge is information integrated into a usable structure. Example: ```text Roman coins can reveal political authority, trade networks, economic systems and cultural influence. ``` Knowledge allows a person to classify, infer, explain or act. --- ### 4.4 Understanding Understanding is the ability to use knowledge to explain relationships, causes, patterns and consequences. Example: ```text A Roman coin found far from Rome can help explain how empire, trade, military movement and cultural influence were connected. ``` Understanding is not simply having more information. It is having a better organising model. --- ### 4.5 Meaning Meaning emerges when information becomes significant within a context, relationship, story, question or personal frame. Example: ```text This coin matters because it is not only currency. It is evidence of power, movement, trust, identity and connection across distance. ``` For The Curiosity Lab, meaning is relational. Information becomes meaningful when a user can answer: ```text What is this? Why does it matter? What does it connect to? What does it help me understand? What question does it open? ``` --- ## 5. Core Distinctions --- ### 5.1 Information vs Knowledge Information can be transmitted. Knowledge must be organised. A page can present information, but the user must integrate it into existing structures for it to become knowledge. Design implication: ```text The system should not simply deliver information. It should support organisation, connection and integration. ``` --- ### 5.2 Knowledge vs Understanding A person may know many facts without understanding how those facts relate. Understanding requires structure. Example: ```text Fact: The Roman Empire used coins. Understanding: Coinage helped standardise exchange, project imperial authority and connect distant regions through shared symbols and economic trust. ``` Design implication: ```text The system should foreground relationships, not only facts. ``` --- ### 5.3 Meaning vs Relevance Relevance means something is connected to a task, interest or context. Meaning goes further. Meaning implies significance. A fact may be relevant to a search query but still not meaningful to the user. Design implication: ```text Search relevance is not enough. The system must explain why a connection matters. ``` --- ### 5.4 Remembering vs Transfer A user may remember a fact without being able to transfer it to a new context. Meaningful understanding should support transfer. Example: ```text Specific fact: Roman roads enabled military movement. Transfer: Infrastructure often strengthens power by making movement, communication and control easier. ``` Design implication: ```text The system should help users move from example to pattern. ``` --- ### 5.5 Surface Features vs Deep Structure Experts often organise problems by underlying principles, while novices may organise by surface features. This distinction is critical for The Curiosity Lab. A curiosity journey may begin with a surface feature, such as a coin, image or place, but should eventually reveal deeper structure. Design implication: ```text Start with surface accessibility. Lead toward structural understanding. ``` --- ## 6. Major Theoretical Strands --- ## 6.1 Meaningful Learning Meaningful learning theories distinguish between memorising isolated material and integrating new ideas into existing knowledge structures. Ausubel’s work is especially relevant because it emphasises the role of prior knowledge in learning. Mayer’s distinction between rote and meaningful learning is also useful because it frames meaningful learning as involving retention and transfer, not merely remembering. For The Curiosity Lab, this suggests that content should be designed to connect new material to what the user already knows, while also helping the user form transferable structures. ### Implication for The Curiosity Lab A curiosity-oriented environment should help users connect new information to existing knowledge. It should avoid treating the user as an empty container. Possible design sequence: ```text Activate prior knowledge ↓ Introduce new information ↓ Connect to existing structure ↓ Reveal relationship ↓ Support transfer ``` ### Design principle > **Meaning increases when new information can attach to existing knowledge and support transfer to another context.** ### Architecture implication Potential entities: ```text Prior Knowledge New Concept Anchor Concept Connection Transfer Pattern Example ``` Potential relationships: ```text connects_to_prior_knowledge anchors supports_transfer generalises_to ``` ### MVP implication Test whether users remember and transfer ideas better when an explanation begins from a familiar anchor before introducing a new concept. ### Confidence Moderate. The educational literature strongly supports the importance of prior knowledge and meaningful learning, but the MVP translation remains unvalidated. --- ## 6.2 Prior Knowledge Shapes What People Notice Learning sciences research repeatedly emphasises that people interpret new information through what they already know. The National Research Council’s *How People Learn* stresses that learners come with prior knowledge, beliefs and concepts that affect what they notice and how they organise and interpret information. This is directly relevant to curiosity. A user cannot become curious about a gap they cannot perceive. Prior knowledge shapes: ```text attention interpretation confusion interest question generation connection making ``` ### Implication for The Curiosity Lab The system should not assume one universal starting point. A curiosity journey may need to detect or support different levels of prior knowledge. ### Design principle > **Curiosity design should scaffold perception before expecting exploration.** ### Architecture implication Potential attributes: ```text prior_knowledge_required assumed_context entry_difficulty scaffolding_level familiar_anchor ``` ### MVP implication Test whether familiar anchors increase continuation for users unfamiliar with a topic. ### Confidence Moderate to High for the importance of prior knowledge. Moderate for its architecture implication. --- ## 6.3 Depth of Processing Craik and Lockhart’s levels-of-processing framework argues that memory is influenced by the depth at which information is processed. Semantic processing tends to produce more durable memory than shallow processing of surface features. For The Curiosity Lab, this matters because curiosity-oriented design should not simply display more material. It should invite deeper processing. Examples of shallow processing: ```text recognising an image reading a date noticing a name clicking a link ``` Examples of deeper processing: ```text explaining why something matters comparing two cases forming an analogy predicting a consequence connecting a fact to a pattern ``` ### Implication for The Curiosity Lab The system should design prompts and journeys that encourage semantic processing. ### Design principle > **Understanding improves when users process meaning, relationships and implications rather than surface features alone.** ### Architecture implication The system may need interaction types that promote deeper processing: ```text compare explain predict connect reflect generalise ``` ### MVP implication Compare simple reading paths with paths that include meaning-making prompts. ### Confidence Moderate. The memory principle is well established, but the specific interaction design must be tested. --- ## 6.4 Mental Models Mental model theory suggests that people understand by constructing internal representations of situations, systems and relationships. Johnson-Laird’s work is relevant because it treats understanding as model construction rather than simple storage. For The Curiosity Lab, this means that a successful journey should help the user build or revise a model. The question is not only: ```text What did the user learn? ``` but also: ```text How did the user's model of the topic change? ``` ### Implication for The Curiosity Lab A curiosity journey should aim to improve the user’s organising model. Examples: ```text Before: A coin is an old object. After: A coin is evidence of authority, exchange, trust, material supply and political communication. ``` ### Design principle > **Meaningful curiosity changes the user's organising model, not merely their fact count.** ### Architecture implication Potential entities: ```text Mental Model Model Element Relationship Reframe Explanation ``` Potential relationships: ```text reframes adds_to_model changes_relationship supports_explanation ``` ### MVP implication Ask users to explain an object before and after a curiosity journey, then compare whether their explanation uses richer relationships. ### Confidence Moderate. The mental-model lens is highly relevant, but operationalising model change requires careful measurement. --- ## 6.5 Expertise and Deep Structure Chi, Feltovich and Glaser’s work on expert and novice categorisation is important because it shows that expertise changes how problems are represented. Experts tend to classify by deep principles. Novices more often rely on surface features. This is directly relevant to curiosity-oriented information architecture. A system should not trap users at the surface level, but it also should not remove the surface-level entry point. For many users, the surface is the doorway. ### Implication for The Curiosity Lab The platform should help users move from surface feature to underlying structure. Example: ```text Surface: This is a Roman coin. Deep structure: This object connects political authority, economic trust, material supply, symbolic communication and imperial geography. ``` ### Design principle > **Curiosity journeys should use accessible surface features to reveal deeper organising structures.** ### Architecture implication Potential relationship types: ```text has_surface_feature reveals_structure is_example_of is_governed_by demonstrates_pattern ``` ### MVP implication Compare a purely descriptive object page with a page that explicitly guides users from surface features to deeper patterns. ### Confidence Moderate. The expertise literature is relevant, but translation into general public curiosity journeys needs validation. --- ## 6.6 Schema, Context and Interpretation Schema theories suggest that people interpret information using existing frameworks. A schema helps organise expectations, fill gaps and interpret ambiguity. This is useful but dangerous. Schemas support understanding, but they can also distort perception. For The Curiosity Lab, context is therefore double-edged. It helps users make sense of information, but it can also overdetermine interpretation. ### Implication for The Curiosity Lab The system should provide context without closing interpretation too early. A good curiosity journey may show several possible frames. Example frames for a coin: ```text economic frame political frame artistic frame technological frame geographical frame personal frame ``` ### Design principle > **Meaning is strengthened by context, but curiosity may require more than one possible frame.** ### Architecture implication Potential entities: ```text Context Frame Interpretation Perspective Alternative Explanation ``` Potential relationships: ```text interpreted_through has_context offers_perspective supports_interpretation complicates_interpretation ``` ### MVP implication Test whether offering multiple interpretive frames increases perceived meaning or generates more follow-up questions. ### Confidence Low to Moderate. The general role of schema/context is plausible and well grounded, but the specific multiple-frame design requires evidence. --- ## 6.7 Analogy and Structural Mapping Gentner’s structure-mapping theory is relevant because it explains analogy as mapping relational structure from one domain to another. This matters because The Curiosity Lab is interested in meaningful connections across domains. A weak analogy connects surface similarity. A stronger analogy maps structure. Example: ```text Weak analogy: This coin is round like the moon. Structural analogy: Coinage and passports both use official symbols to establish trust across distance. ``` ### Implication for The Curiosity Lab The system should not treat all connections as equal. Connections should be assessed by the kind of relationship they express. Analogy is valuable when it transfers structure, not merely when it produces surprise. ### Design principle > **Cross-domain connection should prioritise structural similarity over superficial resemblance.** ### Architecture implication Potential entities: ```text Analogy Base Domain Target Domain Shared Structure Mapped Relationship Surface Similarity ``` Potential relationships: ```text maps_to shares_structure_with is_analogous_to differs_from ``` ### MVP implication Compare generic related content links with structurally labelled analogies. ### Confidence Moderate. Analogy is highly relevant, but automated analogy detection is likely difficult and should not be assumed in the MVP. --- ## 6.8 Concept Maps and Visible Knowledge Structures Concept mapping is relevant because it makes relationships between concepts explicit. Novak and Gowin developed concept maps as tools for meaningful learning. Novak and Cañas describe concept maps as graphical tools for organising and representing knowledge, usually by linking concepts with labelled relationships. This is strongly aligned with The Curiosity Lab’s interest in visible connections. However, concept maps can become overwhelming if they expose too much structure too soon. ### Implication for The Curiosity Lab The system should make knowledge structures visible, but progressively. A full graph may be useful for researchers. A guided path may be better for users. ### Design principle > **Make relationships visible at the moment they become meaningful.** ### Architecture implication Potential entities: ```text Concept Proposition Relationship Label Concept Map Focus Question Cross-link ``` Potential relationships: ```text is_part_of_map links_concept answers_focus_question cross_links_to ``` ### MVP implication Test whether a small, labelled relationship map improves recall and continuation compared with unlabelled related links. ### Confidence Moderate. Concept mapping is highly relevant, but interface complexity must be managed. --- ## 6.9 Semantic Networks and Knowledge Representation Semantic network models represent knowledge as nodes and relationships. Collins and Quillian’s semantic memory work is relevant historically because it models knowledge as an organised network. For The Curiosity Lab, semantic networks provide a computationally attractive model. However, a semantic network is not automatically meaningful. A graph can be technically correct and experientially useless. ### Implication for The Curiosity Lab The ontology must distinguish between: ```text machine-readable connection user-meaningful connection ``` A relationship should not exist merely because two entities can be linked. It should exist because the relationship supports explanation, curiosity or traversal. ### Design principle > **Only expose relationships that help the user understand, question or continue.** ### Architecture implication Potential relationship metadata: ```text relationship_type relationship_label user_explanation confidence evidence_source curiosity_value complexity_level ``` ### MVP implication Test whether relationship labels improve user understanding of why two items are connected. ### Confidence Moderate. Graph representation is relevant, but the meaningfulness of graph traversal must be validated. --- ## 6.10 Construction-Integration and Discourse Comprehension Kintsch’s construction-integration model is relevant because it treats comprehension as a process of constructing possible meanings and integrating them into a coherent representation. For The Curiosity Lab, this suggests that users may need support both in encountering fragments and integrating them into coherence. Curiosity journeys often begin with fragments: ```text object date place person question fact image ``` Meaning emerges when these fragments are integrated. ### Implication for The Curiosity Lab The system should support integration, not only discovery. It should help users see how fragments cohere. ### Design principle > **Curiosity journeys should move from fragment to coherence without eliminating productive uncertainty.** ### Architecture implication Potential entities: ```text Fragment Proposition Coherence Integration Explanation ``` Potential relationships: ```text integrates_with supports_coherence explains_fragment resolves_tension ``` ### MVP implication Test whether users can produce better explanations after a guided integration path than after reading isolated facts. ### Confidence Moderate. The theory is relevant, but the journey design requires testing. --- ## 7. Working Model: Meaning as Connected Understanding The current working model for this domain is: ```text Information ↓ Connection ↓ Organisation ↓ Explanation ↓ Transfer ↓ Meaning ``` This model is provisional. It says that information becomes meaningful when it can be connected, organised, explained and transferred. A fact becomes more meaningful when it helps answer: ```text What is this? What does it connect to? What pattern does it reveal? What does it help explain? Where else does this apply? Why should I care? ``` --- ## 8. Knowledge and the Curiosity Cycle The Curiosity Cycle currently used by the programme is: ```text Observation ↓ Curiosity ↓ Connection ↓ Understanding ↓ Wonder ↓ Renewed Observation ``` The knowledge-and-meaning domain clarifies the middle of the cycle. ```text Observation: The user notices something. Curiosity: The user senses a gap, tension or possibility. Connection: The user links the observation to prior knowledge, context or another domain. Understanding: The user forms a better organising model. Wonder: The user can now notice more than before. ``` This suggests that **connection** is not decoration. Connection is the mechanism by which curiosity may become understanding. --- ## 9. Design Principles The following principles are provisional. --- ### KM-001 — Design from information to meaning The system should not stop at presenting facts. It should help users connect facts to significance. --- ### KM-002 — Activate prior knowledge New information should connect to familiar anchors where possible. --- ### KM-003 — Move from surface to structure Use accessible surface features as entry points, then reveal deeper relationships. --- ### KM-004 — Label relationships clearly Connections should explain their own significance. Avoid vague labels such as: ```text Related More like this You may also like ``` Prefer meaningful labels such as: ```text caused_by similar_pattern reveals contrasts_with modern_parallel led_to ``` --- ### KM-005 — Support transfer A journey should help users apply an idea beyond the original example. --- ### KM-006 — Make knowledge structure visible gradually Expose enough structure to support understanding, but not so much that the user is overwhelmed. --- ### KM-007 — Preserve multiple frames Allow more than one interpretive frame where appropriate. --- ### KM-008 — Treat analogies as structured mappings Use analogy to transfer relational structure, not merely to create surprise. --- ### KM-009 — Measure explanation, not only recall A successful journey should improve the user’s ability to explain relationships. --- ### KM-010 — Distinguish graph correctness from user meaning A technical relationship should not be treated as meaningful until it helps a user understand, ask or continue. --- ## 10. Architecture Implications This domain suggests that the system architecture should support: ```text concepts objects relationships interpretive frames questions knowledge gaps examples patterns analogies explanations transfer paths mental model changes ``` --- ### 10.1 Candidate Entities ```text Information Item Concept Object Prior Knowledge Anchor Context Frame Interpretation Relationship Explanation Pattern Analogy Example Transfer Path Mental Model Reframe Question Knowledge Gap Meaningful Connection ``` --- ### 10.2 Candidate Relationships ```text connects_to explains is_example_of reveals_pattern supports_transfer_to is_analogous_to shares_structure_with contrasts_with is_interpreted_through activates_prior_knowledge reframes deepens_understanding_of opens_question ``` --- ### 10.3 Candidate Attributes ```text relationship_type relationship_label evidence_source confidence_level prior_knowledge_required complexity_level surface_feature deep_structure transfer_potential meaningfulness_score user_explanation_required ``` --- ## 11. MVP Implications The first MVP should test whether structured connections improve meaning. Recommended experiments: --- ### EXP-KM-001 — Labelled Connection vs Generic Related Link Research question: > Do meaningful relationship labels improve understanding compared with generic related links? Variants: ```text A: generic related links B: labelled relationships explaining why the connection matters ``` Measures: - link selection; - relationship recall; - explanation quality; - continuation; - self-reported meaning. --- ### EXP-KM-002 — Surface to Structure Research question: > Does explicitly guiding users from surface feature to deeper structure improve understanding? Variants: ```text A: object description only B: object description plus deeper pattern explanation ``` Example: ```text Surface: A Roman coin. Structure: A portable symbol of trust, power, economy and authority. ``` Measures: - explanation quality; - transfer to another example; - follow-up question depth. --- ### EXP-KM-003 — Familiar Anchor First Research question: > Does beginning from a familiar concept improve curiosity and understanding of an unfamiliar topic? Variants: ```text A: unfamiliar topic introduced directly B: unfamiliar topic introduced through familiar anchor ``` Measures: - continuation; - reported comprehension; - recall; - transfer; - question generation. --- ### EXP-KM-004 — Concept Map Preview Research question: > Does a small relationship map improve orientation and curiosity compared with text alone? Variants: ```text A: text-only journey B: text plus small labelled map ``` Measures: - orientation; - continued exploration; - relationship recall; - perceived coherence. --- ### EXP-KM-005 — Transfer Prompt Research question: > Does asking users to apply a pattern to a new case increase meaningful understanding? Variants: ```text A: explanation only B: explanation plus transfer prompt ``` Measures: - transfer quality; - explanation quality; - confidence; - curiosity continuation. --- ## 12. Measurement Candidates Knowledge and meaning should not be measured only by clicks. Possible measures: ```text relationship recall explanation quality transfer to new example question depth choice of deeper path self-reported meaning ability to identify pattern before/after explanation comparison ``` Possible user prompts: ```text What does this object help explain? What did this connect to that you did not expect? How would you explain this to someone else? Where else might this pattern appear? What question do you now have? ``` Possible caution: ```text Feeling interested is not the same as understanding. Remembering a fact is not the same as meaning. Following a link is not the same as making a connection. ``` --- ## 13. Implications for The Curiosity Lab Hypothesis Current working hypothesis: > **Meaningful curiosity emerges when knowledge is experienced as an interconnected network rather than as isolated information.** The knowledge-and-meaning domain partially supports this hypothesis. Supportive strands: - meaningful learning; - prior knowledge; - depth of processing; - mental models; - expert-novice differences; - analogy; - concept mapping; - semantic networks; - construction-integration. However, the hypothesis should be refined. A stronger version may be: > **Meaningful curiosity is more likely when users can perceive relationships that help them reorganise information into a more useful explanatory model.** This is more precise than simply saying “connected knowledge is better”. It introduces the necessary test: ```text Did the connection improve explanation? Did it support transfer? Did it change the organising model? ``` --- ## 14. Known Weaknesses This document has the following weaknesses: 1. Source-level Research Register entries have not yet been completed. 2. The distinction between knowledge, understanding and meaning needs further literature support. 3. The document currently leans toward cognitive and learning-science perspectives. 4. Social, cultural and embodied accounts of meaning are underrepresented. 5. The role of emotion in meaning is underdeveloped. 6. The architecture implications are still provisional. 7. The ontology candidates may overfit the current hypothesis. 8. The MVP measures need validation. 9. The ethical risks of shaping meaning are not yet addressed. 10. Contradictory perspectives are not yet sufficiently represented. --- ## 15. Contradictions and Cautions --- ### 15.1 More connection is not always better Too many links may overwhelm users. A graph can reduce meaning if it increases cognitive load. --- ### 15.2 Meaning is not purely cognitive Meaning may involve identity, emotion, memory, culture and social context. The current model risks being too cognitive unless later research broadens it. --- ### 15.3 Prior knowledge can mislead Existing schemas can distort interpretation. The system should not simply reinforce what the user already assumes. --- ### 15.4 Transfer is difficult Users may understand an example without transferring the underlying pattern. MVP tests should not assume transfer occurs automatically. --- ### 15.5 Ontology is not understanding A well-structured ontology may help the system organise knowledge, but it does not guarantee that users form meaning. --- ### 15.6 Meaning can be over-directed If the system explains every connection too strongly, users may have less room to form their own questions. Curiosity requires some openness. --- ## 16. Research Debt | ID | Debt | Priority | Status | |---|---|---:|---| | RD-KM-001 | Register Ausubel / meaningful learning source. | High | Open | | RD-KM-002 | Register Mayer on rote vs meaningful learning. | High | Open | | RD-KM-003 | Register National Research Council / How People Learn. | High | Open | | RD-KM-004 | Register Craik & Lockhart on levels of processing. | High | Open | | RD-KM-005 | Register Johnson-Laird on mental models. | High | Open | | RD-KM-006 | Register Chi, Feltovich & Glaser on expert-novice representation. | High | Open | | RD-KM-007 | Register Gentner on structure mapping. | High | Open | | RD-KM-008 | Register Novak & Cañas on concept mapping. | Medium | Open | | RD-KM-009 | Register Collins & Quillian on semantic memory. | Medium | Open | | RD-KM-010 | Register Kintsch on construction-integration. | Medium | Open | | RD-KM-011 | Add social/cultural theories of meaning. | High | Open | | RD-KM-012 | Add embodied cognition perspectives. | Medium | Open | | RD-KM-013 | Add cognitive load cautions for graph-heavy interfaces. | High | Open | | RD-KM-014 | Define measurable “meaning” indicators for MVP. | High | Open | | RD-KM-015 | Translate candidate entities into Ontology.md. | High | Open | --- ## 17. Register Entries to Create Minimum first entries: ```text RR-KM-0001 — Ausubel, D. P. (1968) RR-KM-0002 — Mayer, R. E. (2002) RR-KM-0003 — National Research Council (2000) RR-KM-0004 — Craik, F. I. M., & Lockhart, R. S. (1972) RR-KM-0005 — Johnson-Laird, P. N. (1983) RR-KM-0006 — Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981) RR-KM-0007 — Gentner, D. (1983) RR-KM-0008 — Novak, J. D., & Gowin, D. B. (1984) RR-KM-0009 — Novak, J. D., & Cañas, A. J. (2008) RR-KM-0010 — Collins, A. M., & Quillian, M. R. (1969) RR-KM-0011 — Kintsch, W. (1988) ``` --- ## 18. Immediate Architecture Promotions The following items should be considered for `/architecture/Ontology.md`. ### Candidate Entities ```text Concept Object Prior Knowledge Anchor Context Frame Interpretation Relationship Explanation Pattern Analogy Transfer Path Mental Model Reframe Meaningful Connection ``` ### Candidate Relationships ```text connects_to explains reveals_pattern is_example_of supports_transfer_to is_analogous_to shares_structure_with is_interpreted_through reframes deepens_understanding_of ``` ### Candidate Attributes ```text relationship_label relationship_type evidence_source confidence_level prior_knowledge_required surface_feature deep_structure transfer_potential complexity_level ``` Status: **Provisional** --- ## 19. Immediate Pattern Promotions The following items should be considered for `/architecture/Pattern-Catalogue.md`. ```text Surface to Structure Familiar Anchor Meaningful Connection Labelled Relationship Transfer Prompt Concept Map Preview Multiple Frames Structural Analogy Fragment to Coherence ``` Status: **Emerging** --- ## 20. Immediate MVP Promotions The following items should be considered for `/mvp/Experiments.md`. ```text EXP-KM-001 — Labelled Connection vs Generic Related Link EXP-KM-002 — Surface to Structure EXP-KM-003 — Familiar Anchor First EXP-KM-004 — Concept Map Preview EXP-KM-005 — Transfer Prompt ``` Status: **Candidate** --- ## 21. Provisional Conclusion The current working conclusion is: > **Information becomes meaningful when users can connect it to prior knowledge, organise it within a structure, explain relationships and transfer the pattern to another context.** For The Curiosity Lab, this means the central design challenge is not merely to create more content or more links. The challenge is to design pathways that help users move from: ```text encounter ↓ connection ↓ organisation ↓ explanation ↓ transfer ↓ renewed curiosity ``` This supports a refinement of the programme hypothesis: > **Curiosity-oriented information architecture should help users perceive meaningful relationships that change how they organise and explain what they know.** This remains a hypothesis. The next stage is to register the supporting sources, add contradictory perspectives and test whether users actually form richer explanations after connected journeys. --- ## 22. References Ausubel, D. P. (1968). *Educational Psychology: A Cognitive View*. New York: Holt, Rinehart and Winston. Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). *How People Learn: Brain, Mind, Experience, and School: Expanded Edition*. Washington, DC: National Academy Press. https://doi.org/10.17226/9853 Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. *Cognitive Science*, 5(2), 121–152. https://doi.org/10.1207/s15516709cog0502_2 Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. *Journal of Verbal Learning and Verbal Behavior*, 8(2), 240–247. https://doi.org/10.1016/S0022-5371(69)80069-1 Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. *Journal of Verbal Learning and Verbal Behavior*, 11(6), 671–684. https://doi.org/10.1016/S0022-5371(72)80001-X Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. *Cognitive Science*, 7(2), 155–170. https://doi.org/10.1016/S0364-0213(83)80009-3 Johnson-Laird, P. N. (1983). *Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness*. Cambridge, MA: Harvard University Press. Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction-integration model. *Psychological Review*, 95(2), 163–182. https://doi.org/10.1037/0033-295X.95.2.163 Mayer, R. E. (2002). Rote versus meaningful learning. *Theory Into Practice*, 41(4), 226–232. https://doi.org/10.1207/s15430421tip4104_4 Novak, J. D., & Cañas, A. J. (2008). *The theory underlying concept maps and how to construct and use them*. Technical Report IHMC CmapTools 2006-01 Rev 01-2008. Florida Institute for Human and Machine Cognition. Novak, J. D., & Gowin, D. B. (1984). *Learning How to Learn*. Cambridge: Cambridge University Press. --- ## 23. Next Action Do not expand this document next. The next action is to create source-level Research Register entries for the sources that most affect architecture. Recommended first entry: ```text RR-KM-0001 — National Research Council (2000), How People Learn ``` Reason: This source directly supports prior knowledge, expertise, transfer and learning-environment design. Alternative first entry: ```text RR-KM-0001 — Chi, Feltovich & Glaser (1981) ``` Reason: This source directly supports the surface-to-structure architecture pattern.