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Semantic networks are the mental architecture behind how you store, organize, and retrieve meaning—and they're central to understanding language cognition. When you hear the word "dog" and instantly think of "bark," "pet," or "animal," you're experiencing your semantic network in action. These models explain everything from why some words come to mind faster than others to how we categorize the world around us. You'll be tested on the differences between hierarchical models, feature-based approaches, and connectionist frameworks, as well as the experimental evidence that supports each.
Don't just memorize the names of these models—know what problem each one solves and what cognitive phenomena it explains. The exam loves to ask you to compare approaches (Why does spreading activation explain priming better than a strict hierarchy?) or apply them to real scenarios (How would a connectionist model handle learning a new word?). Understanding the underlying mechanisms will serve you far better than rote recall.
These foundational models propose that knowledge is organized in tree-like structures, with general categories branching into specific instances. The key mechanism is inheritance—properties stored at higher nodes automatically apply to everything below them.
Compare: Collins and Quillian's Model vs. general Hierarchical Networks—both use tree structures and inheritance, but Collins and Quillian specifically predicts retrieval times based on link traversal. If an FRQ asks about cognitive economy or reaction time predictions, go with Collins and Quillian.
These models explain how accessing one concept triggers related concepts automatically. The core mechanism is spreading activation—energy flows outward from an activated node, priming connected concepts for faster retrieval.
Compare: Spreading Activation Theory vs. Semantic Priming—spreading activation is the mechanism, while semantic priming is the phenomenon it explains. Know this distinction for multiple-choice questions that ask you to match theories to evidence.
Rather than focusing on hierarchical links, these models represent concepts as bundles of features. Similarity between concepts depends on how many features they share.
Compare: Semantic Feature Comparison Model vs. Collins and Quillian—hierarchical models struggle to explain why some category members feel more "typical" than others, but feature comparison handles this easily through characteristic feature overlap. This is a classic exam contrast.
These models abandon discrete symbols entirely, representing knowledge as patterns of activation across distributed networks. Learning occurs through adjusting connection weights based on experience.
Compare: Connectionist Models vs. Hierarchical Networks—hierarchical models use explicit symbols and rules, while connectionist models learn implicit patterns. Connectionist approaches better explain how we handle exceptions and novel inputs, but hierarchical models are easier to interpret.
These frameworks emphasize that meaning isn't just about concepts—it's about the relationships between them. Knowledge is stored as structured propositions or relational graphs.
Compare: Propositional Networks vs. Conceptual Graphs—both capture relational structure, but conceptual graphs provide a visual formalism that's particularly useful for computational modeling. Propositional networks are more common in psychological theories of text comprehension.
These large-scale databases operationalize semantic network principles, providing structured representations of word meanings and relationships.
Compare: WordNet vs. FrameNet—WordNet focuses on paradigmatic relations (synonymy, hyponymy), while FrameNet emphasizes syntagmatic relations (what concepts co-occur in situations). For questions about word relationships, think WordNet; for questions about situational meaning, think FrameNet.
| Concept | Best Examples |
|---|---|
| Hierarchical organization | Collins and Quillian's Model, Hierarchical Networks |
| Activation-based retrieval | Spreading Activation Theory, Semantic Priming |
| Feature-based representation | Semantic Feature Comparison Model |
| Distributed/learned representations | Connectionist Models |
| Relational knowledge structures | Propositional Networks, Conceptual Graphs |
| Typicality effects | Semantic Feature Comparison Model |
| Cognitive economy | Collins and Quillian's Model |
| Computational applications | WordNet, FrameNet |
Which two models both use hierarchical structure but differ in whether they predict specific retrieval times? What additional mechanism does one include that the other lacks?
A participant responds faster to "butter" after seeing "bread" than after seeing "lamp." Which model best explains this result, and what mechanism does it propose?
Compare and contrast how the Semantic Feature Comparison Model and Collins and Quillian's Model would explain why people are faster to verify "a robin is a bird" than "a penguin is a bird."
If an FRQ asks you to explain how someone could still understand language after partial brain damage, which model type provides the best explanation and why?
You're designing a computer system to understand that "The teacher gave the student a book" and "The student received a book from the teacher" mean the same thing. Would WordNet or FrameNet be more useful, and what feature of that resource supports your answer?