Chapter 3 Word Representation & Lexical Semantics
đź“– Required Reading
- Chapter 3 from Introduction to Psycholinguistics: Understanding Language Science (2nd ed.) by Matthew Traxler.
Link to Chapter 3
Chapter 3: Word Processing (Part I)
Overview
This chapter examines how the brain stores and processes word forms and meanings. We explore the hierarchical decomposition of words into smaller units (phonetic features → morphemes) and investigate competing theories of lexical semantics, from traditional feature-based approaches to modern embodied simulation models. The chapter explains real-world phenomena like why rare words are harder to process and how context resolves word ambiguity.
Learning Goals
After studying this chapter, you should be able to:
- Describe the hierarchical structure of word forms (phonetic features → phonemes → syllables → morphemes)
- Differentiate between free vs. bound morphemes and inflectional vs. derivational morphemes
- Explain the evidence for morpheme-based processing (root frequency effect, pseudo-affix effect)
- Distinguish between sense and reference in lexical semantics
- Evaluate the core features approach to meaning and its limitations
- Analyze how semantic networks use spreading activation to represent meaning relationships
- Compare associationist models (HAL, LSA) and their symbol-grounding problem
- Understand the embodied semantics approach and its evidence from perceptual/motor systems
- Apply semantic processing principles to explain real-world language phenomena
Chapter 3 Lecture Notes: Word Processing (Part I)
Key Focus: Word Form Representation & Lexical Semantics
Overview
This chapter explores how the brain stores and processes two core aspects of words: form (sound/spelling) and meaning (semantics). It begins with the “invisible work” of word processing (e.g., distinguishing “coffee” from “coffin”) and breaks down:
- How words are mentally decomposed into hierarchical units (phonetic features → morphemes).
- How word meanings are represented (debating “dictionary-like features” vs. networks/embodied simulation).
Real-world relevance: Explains why rare words (e.g., “philatelist”) are harder to process than common ones, and how context clarifies ambiguous words (e.g., “bank”).
1. Mental Representation of Word Form
1.1 Core Premise
Words are not stored as single chunks—they are decomposed into smaller, nested units (like molecules → atoms). This reflects how the brain actually processes words during reading/speech.
1.2 Hierarchy of Word Form Components
| Level | Description | Examples |
|---|---|---|
| Phonetic Features | Basic articulatory properties (how sounds are made). | /p/ = +labial (lips), -voiced (no vocal fold vibration); /b/ = +labial, +voiced (distinguishes pat/bat). |
| Phonemes | Smallest sound units that change meaning. | cat = /k/+/æ/+/t/; replacing /k/ with /b/ → bat (new meaning). |
| Syllables | Speech units (jaw open/close cycles). | cat = CVC (/kæt/); spam = onset (/spa/) + rime (/am/). |
| Morphemes | Smallest meaningful units. | cats = cat (root) + -s (plural); unhappy = un- (negative) + happy (root). |
1.3 Morpheme Types
| Category | Definition | Examples |
|---|---|---|
| Free vs. Bound | Free = stands alone; Bound = needs a root. | Free: dog, run; Bound: -s (plural), un- (negative). |
| Inflectional vs. Derivational | Inflectional = modifies meaning (tense/number) but not part of speech; Derivational = changes part of speech. | Inflectional: bake → baked (verb → verb); Derivational: confuse (verb) → confusion (noun). |
1.4 Evidence for Morpheme-Based Processing
| Effect | Description | Example |
|---|---|---|
| Root Frequency | Rare full words (low surface frequency) are fast to process if their root is common (high root frequency). | Dogpile (rare) is processed as fast as dog (common) → brain uses the root dog to shortcut recognition. |
| Pseudo-Affix | Words with fake affixes take longer to process (brain strips the affix, fails to find a root, then re-searches). | Sister (fake -er) is slower than grower (real -er = “someone who grows”). |
2. Lexical Semantics: What Is “Meaning”?
2.1 Key Distinction: Sense vs. Reference (Jackendoff, 1983)
| Type | Definition | Example |
|---|---|---|
| Sense | General, stable knowledge (dictionary-like). | Cat = “small furry mammal, purrs, hunts mice.” |
| Reference | Context-specific “target” (what the word points to). | My cat = your specific pet (e.g., Mittens, a tabby). |
2.2 Traditional Theory: Core Features Approach
- Claim: Meaning = list of “necessary features” (e.g., bachelor = human + adult + male + unmarried).
- Critiques:
- Exceptions: Monks meet “bachelor” features but are not called bachelors.
- Fuzzy categories: Game (board/sports/video) has no universal uniting feature.
- Prototypicality: “Fire engine red” is a “better” red than “red hair”—features treat all examples equally.
2.3 Modern Theory 1: Semantic Network (Collins & Loftus, 1975)
- Claim: Meaning = activation pattern in a network of nodes (concepts) and links (relationships).
- Components:
- Nodes = concepts (goose, bird); Links = “Is a” (goose→bird), “Has” (bird→feathers).
- Spreading Activation: Activating one node (e.g., goose) spreads to connected nodes (duck, waterfowl)—automatic, weakens with distance.
- Evidence: Semantic Priming: Faster response to duck if preceded by goose (related) than horse (unrelated).
2.4 Modern Theory 2: Associationist Models (HAL & LSA)
| Model | Corpus | Method | Meaning Representation | Limitation (Symbol-Grounding Problem) |
|---|---|---|---|---|
| HAL | 200M USENET words | Tracks word co-occurrence (proximity scores). | Word = vector in a 70kĂ—70k matrix. | No real-world grounding—only word-word links (like a Chinese Room: follows rules but doesn’t “understand”). |
| LSA | 5M encyclopedia words | Reduces “wordĂ—episode” matrix to 300 dimensions. | Word = vector across 300 dimensions. | Same as HAL—no connection to real objects/actions. |
2.5 Modern Theory 3: Embodied Semantics (Indexical Hypothesis)
- Claim: Meaning is tied to perceptual/motor systems (simulate real-world experiences).
- 3 Steps:
- Indexing: Chair → mental image of a chair (seat/back).
- Affordances: Infer actions (chair → “can sit”).
- Meshing: Phrase meaning = combining affordances (“sit on chair” = chair’s “affords sitting” + body’s “can sit”).
- Evidence: Participants judge “fill sweater with leaves” (plausible, leaves afford being a pillow) as better than “fill with water” (implausible)—LSA can’t predict this.
Quick Review Questions
- What is a morpheme? Give an example of a bound derivational morpheme.
- How does the root frequency effect support morpheme-based processing?
- What’s the difference between “sense” and “reference” for the word dog?
- Why does the core features approach fail to explain word meaning?
- What is spreading activation in the semantic network model?