Psych Of Language-Second Half

Front 1

Word Frequency Effect

Back 1

words that are more frequent are recognized faster, (you’re more likely to think someone said baby than Babylon) so Forster ordered words in the lists by frequency (but context and grammar make a difference)

Front 2

Cohort model

Back 2

Marslen-wilson, parallel (many words are active at the same time), “he packed the picnic in the ba_ _” /b/ cohort, /bas/ cohort, /bask/ cohort. Start building a cohort of potential sounds around the word. All words with /b/ are active until you hear ba then bike and box drop out. Bashful drops out because its not a noun (so words also drop out when they don’t match the semantic context) bottom-up; process continues until only one word is left; problems=look in notes!!

Front 3

Recognition Point

Back 3

in cohort model, when only one word is left-lexical access

Front 4

trace model

Back 4

parallel and connectionist, Mclelland & Elman, three different layers (features-phonemes-words) phonemes are sending positive excitatory connections to the words containing them, phonemes inhibit other phonemes, words inhibit other words. You hear the word, which starts sending signals to other words, over time only one word is left active.

Front 5

connectionism

Back 5

see trace model^

Front 6

node

Back 6

a point of intersection like in trace model. Phoneme nodes, linked together in connectionist and parallel processing

Front 7

• “look at that car/go on that tanker”

Back 7

segmentation problem; trace doesn’t account for this;

Front 8

Shigarette

Back 8

- we know its cigarette b/c no other word ends in igarette.

Front 9

Lexicon

Back 9

mental dictionary

Front 10

Lexical Access

Back 10

Stage of word recognition at which stored information about a word becomes available for further processing

Front 11

Word monitoring

Back 11

using same target words in different spoken sentence contexts, shows that context affects how fast we register the target word-people push button as soon as they hear the word they were shown in the beginning

Front 12

Lexical Decision Task

Back 12

people push a button whether it’s a real word or nonsense word-measures the time it takes to recognize words

Front 13

Morphological Representation

Back 13

idk...the word we use to represent a concept??

Front 14

Novel Words

Back 14

neologisms, new words like “meme”

Front 15

fixed word meanings

Back 15

does a word have one fixed meaning? (core semantics, necessary & sufficient) a way to figure out word meanings

Front 16

Necessary and Sufficient conditions

Back 16

fixed, ex table, flat-has 4 legs-says anything that is flat with four legs is a table, ex in order for john to be a bachelor it is necessary that he is unmarried, male, and adult. So it is sufficient to know that john is a bachelor to know that he is male

Front 17

Core Semantics

Back 17

- fixed, words have a basic core to them, a definition, but doesn’t say that anything with those qualities must be that thing, describes commonalities/core meanings, but it doesn’t always work (ex. A bird has feathers, wings, etc. but if a bird is plucked it’s still a bird.)-difficult to define core easily

Front 18

Fuzzy edges

Back 18

- words open to interpretation-ex of vase, cup, bowl depending on what’s in

Front 19

Family Resemblances

Back 19

Example of games, define games and we see much overlap in similarities between types of games. There is no clear cut boundary

Front 20

Prototype Theory

Back 20

fuzzy, we have a common, generic image of what a cat is. More of a concept, a common understanding, feature-based, but are not necessary & sufficient features, we are faster at identifying things that are closest to our prototype. Copes with damaged examples (plucked chicken) but how do you pin down the list of features? It goes by appearance only, not everything has a single prototype

Front 21

Exemplar Theory

Back 21

fuzzy, has multiple ideas about what it can look like, our experience dictates what exemplars are. Takes a while. Addresses prototype theory. Function matters, we compare an item to diff exemplars for similarities and dissimilarities (ex. Owl)

Front 22

Cognitive Economy

Back 22

how meaning is stored-a brain uses the least amount of effort possible, and is not big enough to store all meanings separately.

Front 23

Hierarchical Network Model

Back 23

- memory is organized hierarchically and on diff levels. Items on the same level will have the same response time (we will be quicker to say canary is yellow than canary eats b/c they are on the same level) Look in notes for advantages

Front 24

Semantic Net Model

Back 24

words are organized based on strength of past associations- poke, go, molasses=slow. The faster people identify words, the more they are interconnected. Interrelations-ex remote association test . feature based model

Front 25

Lexicalized

Back 25

when thoughts are put into words