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Presentation on theme: "FROM THE BOOK: AITCHISON, JEAN (1987). AN INTRODUCTION TO THE MENTAL LEXICON PUBLISHER:OXFORD, UK ; BLACKWELL, NEW YORK, NY, USA Words in the mind."— Presentation transcript:


2 Main aims How do humans manage to store so many words? How do they find the ones they want? What is the nature of human MENTAL LEXICON?

3 KEY: ORGANIZATION Number of words known by an educated adult: between and In speech, around 6 syllables per sec are produced ½ sec needed to detect a non-word or find a real word FAST and EFFICIENT word-searching ability: The large number of words known and the speed with which they can be located point to the existence of a highly organized mental lexicon

4 Our mental dictionary Not alphabetical Fluid Flexible Ever-growing Detailed (lots of information stored in each entry) Interconnected

5 Clues to the mental lexicon organization Word-finding efforts “slip of the tongue” “tip of the tongue” normal speakers people with speech disorders

6 How can we define the meanings of words? The fixed-fuzzy issue The fixed meaning assumptionThe fuzzy meaning assumption Words meaning is: Basic Fixed Firm Words meaning is: Fuzzy Slippery Ever-changing For the MENTAL LEXICON: the answer will affect our view of HOW people represent meanings in their minds

7 The fixed meaning assumption Words meaning can be identified by defining a set of core criteria The ‘check-list’ theory: for each word we have an internal list of essential characteristics  IF something has these characteristics, we label it with the corresponding word Aristotle school: words have a hard core of ESSENTIAL MEANING, surrounded by other characteristics which are not essential (can be omitted) (ex: the ‘that’s impossible’ test, bachelor)

8 The fixed meaning assumption: problems How can we choose the criteria? Which attributes go on to the check list? Not every word can be easily defined:  Not obvious way to draw a dividing line between ESSENTIAL and NON-ESSENTIAL characteristics

9 The fuzzy meaning assumption Word meanings are inevitably fluid, for 2 reasons: 1. Fuzzy edges  there is no clear edge where one word ends and another begins. Ex: vase-cup-bowl 2. Family resemblances  a group of words can share some characteristics, but not all of them at the same time. Ex: GAMES: ring-a-rose/chess/tennis

10 Prototype theories How humans cope with this fuzziness? Does everyone operate the same way with meanings? How do we organize meanings? Humans tend to find some INSTANCES of a word more central/basic than others  We have in mind a prototype with specific characteristics  we label things IF they have a REASONABLE amount of those characteristics (≠ check list)

11 Some examples: Eleanor Rosch’s experiments Birds: canary – peacock – penguin Colours: red – orange red – purple red Experiments shown very consistent results, we use categories when we have to label a concept Tipicality ratingCategory verification

12 Conclusions When people categorize objects, they bear in mind an IDEAL EXEMPLAR  A PROTOTYPE MATCHING is the process used to decide whether something is a member of that category NOT every characteristic has to match, it has to be sufficiently similar to the prototype

13 Conclusions Prototypes help us coping with: 1. UNTYPICAL EXAMPLES  We still recognize penguins as birds 2. DAMAGED EXAMPLES  We still recognize a three legged tiger as a quadruped Prototype theory is a good way to interpret how humans deal with LARGE CATEGORIES

14 Some problems with prototypes A general problem: Interference between IDENTIFICATION CRITERIA and STORED KNOWLEDGE They often are the same or largely overlap, but sometimes not: the second influences our judgment Inevitable link PERCEPTION-CONCEPTION (ex: the story of the farmer: bull vs. cow)

15 1. The diversity of the characteristics The properties of a prototype are heterogeneous  result: prototypes differ one another in type! ANIMALS vs. COLOURS A prototypical BIRD is defined by some TYPICALITY CONDITIONS: -Has feathers, wings, beak -It flies -It lays eggs -It makes a nest A prototypical RED is defined by some CENTRALITY CONDITIONS: It occupies a central place within a range of red shades

16 2. The difficulty of arranging them in order of priority In which order can we list the necessary conditions which define a prototype? How do we choose which one is more crucial than another? Ex: BIRD - Has feathers - Has wings  bats have wings, they are not birds! - It flies  ducks and penguins don’t fly! - It makes a nest  other animals too

17 3. The problem of knowing where to stop We cannot put on to the list everything we know about an object/concept/etc. (i.e. our ENCYCLOPAEDIC KNOWLEDGE) Sometimes it’s difficult to separate out the meaning of a word from the FRAME where it occurs Ex: define the word zebra  someone will activate a whole zoo frame, others won’t. In conclusion: words cannot be dealt with in isolation, they are stored in relation to one another

18 WORDS INTERACTION Words cannot be treated as separate items. They are interdependent and we understand them as interdependent items: red  orange  yellow Hot  warm  cold Two broad viewpoints The “atomic globule” viewpoint The “cobweb” viewpoint How does the mind cope with these relationships?

19 How does the mind cope with these relationships? Two broad viewpoints Words are globules, made of “meaning atoms” Related words have atoms in common Words are considered as related because of the links that the speakers build between them

20 1. The “atomic globule” viewpoint There is a universal SET of basic atoms of meaning: SEMANTIC PRIMITIVES  Innate part of human mind  Biologically given notions  Organized differently in different languages (different segmentation of the semantic space)

21 1. The “atomic globule” viewpoint How can we define the basic atoms? Is there a definitive list? Do they suffice to describe every meaning? Schank (1972) on Verbs: MOVE, INGEST, CONC, MTRANS, ATRANS… Es: breathe, dink, eat  INGEST Buy, give, steal  ATRANS Miller&Johnson-Laird (1976) on Objects: Linking primitives to perception. PLACE, SIZE, HORIZONTAL, VERTICAL, BOTTOM, TOP… ! Not everything is PERCEIVABLE ! Es: to promise, to disagree

22 2. The “cobweb” viewpoint Words are linked together in a gigantic multi- dimensional cobweb  NETWORK THEORIES: A network is an interconnected system, made of nodes and links. Collins&Quillian model “Retrieval time from semantic memory» (1969)

23 HOW DO THESE INTERCONNECTIONS WORK? 1. Linguistic habits: pen +pencil, moon + stars. Strong ties, easily revealed by free association tests. DIFFERENT TYPES OF ASSOCIATIONS! 1.Co-ordination 2.Collocation 3.Super- ordination 4.Synonymy

24 HOW DO THESE INTERCONNECTIONS WORK? 1. Linguistic habits CO-ORDINATION seems to be the strongest link. Hypothesis confirmed by:  The most common slips of the tongue: left- right/today-tomorrow  Difficulties in distinguishing co-ordinate items in aphasic subjects: 1. lemon-orange vs. lemon-boot experiments 2. Recognition tasks experiments (squeezing a ball) Some CO-ORDINATES are so strongly linked that brain-damaged people tend to confuse them in production/comprehension, but, at the same time, their link is recognized faster then others

25 HOW DO THESE INTERCONNECTIONS WORK? 2. Learning from children Finding out how babies build up a store of words = Additional clues as to how interconnections are created. 3 essential steps 1 st step LABELLING: Symbolization, a string of sounds is used to name something 2 nd step PACKAGING: what can be packed together under the same label? 3 rd step NETWORK BUILDING: how do I link words one another?

26 LABELLING Some clarifications: To symbolize = to realize that a particular combination of sounds means od symbolize a certain object. It emerges around the ages of 1 and 2 Before: Babbling is an muscle exercise. At this stage, infants have not attach yet the actual meaning to the word. They are still experimenting, making noises.

27 PACKAGING Children package meanings in a different way They do not necessarily focus on the same characteristics/priorities Under- extension They assume a word refers to a NARROWER range of things Ex: white page/ white snow (Leopold) Over- extension They assume a word refers to a LARGER range of things Ex: quack=duck, cup of milk, eagle on a coin (Vygotsky)

28 NETWORK BUILDING A six year old child has a passive vocabulary of about words  they need to be put together into a semantic network Network building is a slow process: children tend to link a word only with the specific context where they learned it Children use COLLOCATION principles more than CO-ORDINATION principles in free association: Children associationsAdults associations TABLE - eatTABLE - chair DARK - nightDARK - light SEND - letterSEND - receive

29 Word production & Word recognition Language information processing: Semantic  syntactic  phonological info Production From the meaning to the sounds Recognition From the sounds to the meaning Production & Recognition = mirror images of one another

30 Words production: Hypothesis & Theories 1 st process: DECISION MAKING! Underneath process, even when we are not conscious.  blends: an evidence that alternative words are often considered during speech Blends examples Buggage  baggage + luggage Tummach  tummy + stomach Evious  evident + obvious Aphasic speech: “I forget (don’t remember) seeing you, sir. I remember the other document (doctor + gentlemen) and was plazed (pleased + glad) to see the other document.” (Butterworth, 1979)

31 Words production: Hypothesis & Theories Blends suggestion: we activate a number of words in the area of the required word and then suppress the words we don’t want. Spreading/Interactive activation principle: The initial input activates words spreading along the connections Relevant links get more and more activated Unwanted links tend to fade away connectionist models of language perception/ production connectionist models of language perception/ production

32 Words recognition Basic problems in normal speech: 1. Sounds are altered by their neighbours 2. Sound segments cannot be separated, they tend to merge 3. Noises cover the sounds. So, how do we understand these sounds? Guessing Matching the portion heard with their mental lexicon Choosing the best fit Filling in gapsGuessing Matching the portion heard with their mental lexicon Choosing the best fit Filling in gaps

33 Words recognition The role of context in detecting sounds:  Paint the fence and the ?ate.  GATE  Check the calendar and the ?ate.  DATE  Ho freddo, chiudi la ?orta.  L’ho fatta ieri, assaggia la ?orta.

34 Words recognition Summing up the process: 1. To split up the stream of sounds into words 2. To identify the words 1.SOUNDS  PHONEMES 2.PHONEMES  MORPHEMES 3.  Then fitted to words Words frequency: common words are recognized faster. Why?  Network theory preferential attachment. The role of context: same as guessing for phonemes

35 Connectionist model of spoken word recognition/production INTERACTIVE ACTIVATION among processing units unit = GUESSING about the input Three levels organization: 1. Feature/input 2. Phoneme 3. Words Three types of connectivity: 1. Bottom-up 2. Lateral (i.e., within-layer) 3. Top-down An example of connectionist model. TRACE (McClelland & Elman, 1986)


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