Untitled Flashcards Set

Distance in Semantic Networks

Shorter distance = stronger semantic relation.

Spreading Activation

When a word is activated, related words also receive activation.

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Morphology

Morphology is the study of the structure of words and how morphemes (the smallest units of meaning) combine to form words.

Free Morphemes

Free Morphemes can stand alone as words (e.g., 'cat,' 'run').

Bound Morphemes

Bound Morphemes must attach to another morpheme to convey meaning (e.g., '-s' in 'cats,' 'un-' in 'undo').

Inflectional Morphemes

Inflectional Morphemes modify grammatical features (tense, number, case) without changing word class (e.g., '-ing,' '-ed,' '-s').

Derivational Morphemes

Derivational Morphemes change word meaning and/or class (e.g., 'happy' → 'happiness,' 'develop' → 'development').

Semantic Network Approach

Words are stored in a network where related concepts are connected (e.g., 'dog' and 'bark' are closely linked).

Nodes

Nodes represent individual words or concepts.

Links

Links represent semantic relationships between nodes.

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Key Features of Spreading Activation

Fast and automatic; explains priming effects (why related words are recognized faster).

Repetition Priming

Re-exposure to a stimulus improves processing (e.g., seeing 'doctor' once makes you faster at recognizing it again).

Semantic Priming

Words that are semantically related facilitate faster recognition (e.g., 'doctor' primes 'nurse').

Mediated Priming

Indirect relationships (e.g., 'lion' primes 'stripes' through an intermediary word like 'tiger').

ERP N400 Effects

The N400 is a brain response that occurs when a word is unexpected or semantically incongruent.

Example of N400 Response

'I like my coffee with cream and... dog.' (The unexpected word triggers an N400 response).

Associationist Approaches

Approaches that model meaning based on statistical patterns in language exposure rather than structured networks.

HAL (Hyperspace Analogue to Language)

Captures word meaning based on co-occurrence patterns.

LSA (Latent Semantic Analysis)

Uses vector spaces to analyze word meanings across large text corpora.

Key Study: Rhodes & Donaldson (2007)

Provided evidence for these computational approaches.

Symbol Grounding Problem

How can words have meaning without direct sensory experience?

Embodied Semantics

Meaning is tied to sensorimotor experience.

Affordances

Objects suggest actions (e.g., a handle 'affords' grabbing).

Key Study: Glenberg & Robertson (2000)

Demonstrated that verbs activate motor representations.

Key Study: Chwilla et al. (2007)

Showed how action words engage motor regions in the brain.

Pandemonium Model

Word recognition involves multiple feature analyzers ('demons') that detect elements of letters.

Logogen Model

Words have activation thresholds; when enough features match, the word is recognized.

Frequency Ordered Serial Bin Search

Words are stored by frequency, with frequent words accessed faster.

TRACE Model

Interactive activation at phonemic and lexical levels allows both bottom-up and top-down influences.

Cohort Model

Words are recognized based on initial phonemes.

Cohort Model (continued)

The list of possible words (the cohort) narrows as more information is processed.

Simple Recurrent Network (SRN)

Uses context and prior inputs to predict next word.

Distributed Cohort Model

Incorporates both phonological and semantic factors for word recognition.

Prescriptivist Grammar

Follows strict grammar rules.

Descriptivist Grammar

Focuses on how language is actually used.

Phrase Structure Trees

Visual representation of hierarchical sentence structure.

Syntactic Ambiguity

Temporary (Garden Path Sentences): Mislead the reader initially but are later reinterpreted.

Global Ambiguity

The sentence remains ambiguous even after it is fully processed.

Minimal Attachment

Prefer the simplest possible structure.

Late Closure

Attach new words to the current phrase.

Constraint-Based Theories

Use multiple sources of information (syntax, semantics, context) simultaneously.

Good-Enough Parsing

Listeners/readers don't always fully analyze sentences, leading to partial understanding.

Example of Good-Enough Parsing

"While the man hunted the deer ran into the woods" might be misinterpreted due to shallow processing.

Lexical decision tasks

Word vs. non-word judgments.

Self-paced reading

Measuring reading times per word.

Eye-Tracking

Tracks where and for how long a person looks at words.

Advantages of Eye-Tracking

Reveals real-time sentence processing.

Disadvantages of Eye-Tracking

Expensive, requires specialized equipment.

ERP/EEG (Event-Related Potentials)

Measures electrical activity in response to stimuli.

N400

Semantic violations.

P600

Syntactic reanalysis.

Advantages of ERP/EEG

High temporal resolution (milliseconds).

Disadvantages of ERP/EEG

Poor spatial resolution (hard to localize activity).

fMRI (Functional Magnetic Resonance Imaging)

Measures brain activity based on blood flow.

Language processing in Broca's area

Syntax.

Language processing in Wernicke's area

Semantics.

Advantages of fMRI

Excellent spatial resolution.

Disadvantages of fMRI

Expensive, low temporal resolution.

• Limited availability in some regions due to high costs and specialized equipment requirements..

• Non-invasive, allowing for repeated measurements without risk to the patient.