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Arbitrary
language characteristic: made up of learned symbols
Structured
language characteristic: governed by a system of rules
Generative
language characteristic: expresses limitless meanings
Phonology (Phoneme)
language level: sounds and how to combine them
Morphology
language level: parts of words that have “meaning” (-s, -ed, -ing)
Syntax
language level: grammatical structure
Semantics
language level: understanding what words and sentences refer to in memory
Pragmatics
language level: undestanding what people mean by what they said
Serial Bottom-Up Solution
Type of Ambiguity: we process language using default rules (heuristics)
Interactive Solution
Type of Ambiguity: language is always processed using multiple sources of available information, including context and non-linguistic information
Phonemic Restoration Effect
Top-Down Processing of Sounds: people hear no gap sentence where sound has been replaced by a noise
Cohort
set of words consistent with first syllable
Cohort Model
words consistent with input become active
words in the cohort are eliminated when they become inconsistent with input
other words eliminated due to contextual incongruity
processing ends when there is one word left in cohort
Syntactic (Attachment) Ambiguity
sentences or phrases that have more than one interpretation or “parse”
"ex. “the woman in the chair with the broken leg” → who has a broken leg, the woman or the chair
Elaborative Inferences
filling in missing details using context
ex. “he pounded the nail in.” → with a hammer or a shoe?
Sound Exchanges
“rack pat”
occur mostly between nearby words
tend NOT to occur across phrases
do not respect grammatical category or function
Word Exchanges
“write a mother to my letter”
span some distance
respect grammatical category and function
Anticipation Errors
Perseveration Errors
Blends
Discrete
Two-Step Model
message (stimuli)
semantic/syntactic retrieval (what the word looks like)
phonological encoding (what the word sounds like)
articulation (spoken word)
Interactive Activation Model
PDP model with layers of nodes corresponding to semantic features, words, and phonemes
Lexical Bias
tendency for errors to create words rather than non-words
“hold card cash (cold hard cash)” v. “weautiful boman (beautiful woman)”
Mixed Errors
errors can be semantic, (non-)lexical, phonological, or any combination
Non-Lexical Error
error: speaking the wrong sounds that don’t form a word instead of a word
“gat” instead of “cat”
Lexical Phonological Error
error: saying another word instead of the correct word
“mat” instead of “cat”
Semantic Errors:
error: speaking the wrong word associated with memories of the correct word
“dog” instead of “cat”
Mixed Phonological and Semantic Errors
“rat” instead of “cat” - rhyming (activating the same phonemes) and associated (both pets)
Sound Error
Level 1 Slip: “rack pat v. pack rat”
Morpheme Error
Level 2 Slip: “self-destruction instruct v. self-destruct instruction”
Word Errors
Level 3 Slip: “writing a mother to my letter v. writing a letter to my mother”