words in the mind: an introduction to the mental...

Words in the mind:An introduction tothe mental lexicon

Mei-Shin Wu

Topics that we aregoing to covertoday

The major questions

Are words floatingaround in humanbrain?

How the outer worldconnect with words?

Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks

Summary ofsemantic models

Part of Speech

Semantic maps

Summary

1/45

Words in the mind: An introduction to themental lexicon

Mei-Shin Wu

2016


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

2/45

Outline

Topics that we are going to cover todayThe major questionsAre words floating around in human brain?How the outer world connect with words?

Fix-Fuzzy issuePrototype theory

Mental Modelsemantic networksSummary of semantic modelsPart of Speech

Semantic maps

Summary


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

3/45

Outline




Semantic maps

Summary


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

4/45

The major questions

The mental lexicon is the store of words in long-termmemory from which the grammar constructs phrases andsentences.1

The questions we would like to ask is as following :

I Are words floating around in human brain?

I How the outer world connect with words?

I How human brain retrieve words?

I The form of words in human brain.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

5/45

Outline




Semantic maps

Summary


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

6/45

Are the words floating around in the brain?

I Human brain structurally organized linguisticsinformation.

I The evidenceI Straightforward point of view : The word association

experiments.I Daily life observations : Slip of tongue phenomenon.I Medical point of view : fMRI evidence.3,2

I semantic-network in human brainhttp://gallantlab.org/huth2016/

http://gallantlab.org/huth2016/


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

7/45

Outline




Semantic maps

Summary


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

8/45

Fix-Fuzzy issue I

Fix meaning assumption

I The original point ofview.

I Each word has a basicmeaning, whichindividuals should striveto attain.

I In favor of philosophers.

Fuzzy meaning assumption

I An alternative point ofview.

I The natural languageconcepts have vagueboundaries and fuzzyedges.

I In favor of psychologists.

Unfortunately, these two assumptions aren’t perfect.This book assumes that people translate the real worldinto concepts. Second, this book treat the meaning of aword as overlapping with the concept to a large extent.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

9/45

Fix-Fuzzy issue II

Issues about Fix meaning assumptionLet’s assume that human’s memories are like a series ofsnapshots. Example : Think about the word ”cat”, what isthe image in your mind?


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

10/45

Fix-Fuzzy issue II

what about this one?

The episodic memory theory : When a new image occurs, humanbrain will search all the image in mind. The recognition process failwhen there is no image matched.The truth : you can still recognize the images, even they don’tmatch the pictures in human’s brain.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

11/45

Fix-Fuzzy issue III

Check list theory : Humans have an internal list of essentialcharacteristics in mind for each words. And we labelsomething if it possesses the criteria attributes.

essential meaning

additional meaning

Philosopher Aristotle : Words must have a hard core ofessential meaning which it is, in principle, possible toextract and specify.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

12/45

Fix-Fuzzy issue III

Essential meaning : It can be entered in linguistic dictionary.additional meaning : It is encyclopaedic knowledge. It can beadded and omit without alter the basic meaning of a word.

essential meaning

additional meaning

Problems of Checklist theory : It is hard to determine a firmsemantic core for some words.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

13/45

Fix-Fuzzy issue IV

Fuzzy meaning assumption :

I fuzzy edge phenomenon.

I family resemblance.

Wittgenstein : We are faced with a complicated network ofsimilarities overlapping and criss-crossing.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

14/45

Prototype theories

Eleanor Rosch, 1975 [4]

I Questionnaire study on200 psychologystudents.

I Seven-point scale with1 meant excellentexample for thecategory.

I The results wereconsistent. Forexample, students allagree that robin wasthe best example of abird.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

15/45

Prototype theory

The concept of Prototype theories :People categorize common objects, they do not expect themall to be on an equal footing.[5]

Checklist Theory

red

non-red non-red non-red

non-red non-red

Prototype Theory

red red red

red red red


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

16/45

Prototype theories- example 1

Degrees of lyingA prototype lie occurswhen a speaker :

I asserts somethingfalse

I which they know tobe false

I with the intentionof deceiving

case 1 :After a boring party,guests said to the host :I had a wonderful time.case 2 :

Prototype theory explains how words can be used withslightly different meanings.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

17/45

Prototype theories- example 2

Polysemy- multiple meanings

I goI The janitor goes from top to bottom of the building.I The staircase goes from top to bottom of the building.

I overI The clouds floated over the city.I Sam walked over the bridge.

I oldI old womanI old boyfriend

Judging by the above example, It seems that allowing roughmatches to suffice is the way we understand a number ofdifferent words.Prototype theory cannot perfectly explain these examples.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

18/45

Summery of prototype theory

I Words (or categories) have fuzzy boundaries

I Words are defined based on a best exemplar.

I Some members are better examples of a certaincategory than others.

I The meaning of words/categories is culture andcontext dependent.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

19/45

Outline




Semantic maps

Summary


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

20/45

Mental Model-assumptions

There are assumptions:

I A prototype may be an unconscious attempt to reconcilenatural variability with a checklist approach to meaning.

I The features of an object enhance human brain and thusforming category. And the object is the best exemplar.

I The prototypes represent internal theories. Eachindividual build their own mental models. And it is aninextricable mixture of acute observation, culturalbrainwashing, fragments of memory and a dollop ofimagination.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

21/45

Mental Model

One of the central issues in developing a model of the mentallexicon is whether the form of a word in the lexicon (e.g.,phonological, /kat/, or orthographic, cat) is represented withits meaning (e.g., the idea or concept of a cat) in the samelexical entry or if they occupy separate entries.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

22/45

Semantic networks-psycholinguistics experiments

Lexicographer Samuel Johnson believed that words in mentallexicon are connected like a fishing net. Every edge isequal length. (18 century)

But is it true?

I Word association experiment.

I Tip of tongue phenomenon.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

23/45

Semantic networks- Word association experiment.

Give me the first word you think of when I say hammer.

Give me the first word you think of when I say wife.

Give me the first word you think of when I say car.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

24/45

Semantic networks- Word association experiment.

Give me the first word you think of when I say hammer.Out of 1000 people, 50% people will say nail. People selectitems from the same semantic field

Give me the first word you think of when I say wife.People often reply with husband. If items is one of the pair,people go for it’s partner.

Give me the first word you think of when I say car.truck, transportation, boat. People often response withwords in the same world class.

From the above observation, what have we found?Maybe the connections between words are not equal length?


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

25/45

Semantic network-Build a mental map

More examples about words association experiments.

BUTTERFLY HUNGRY RED SALT

1 moth food white pepper2 insect eat blue sugar3 wings(s) thirsty black water4 bird full green taste5 fly starved colour sea6 yellow stomach blood bitter7 net tired communist shaker8 pretty dog yellow food9 flower(s) pain flag ocean

10 bug man bright lake

Can we build a brief semantic map from the above examples?


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

26/45

Semantic network-Listing the links


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

27/45

Semantic network-Listing the links II

I Coordinates: The words which cluster together on thesame level of detail.

I Links between coordinates are strong.

I Topic areas are stored to some extent independently,and that some semantic fields can be damaged withoutaffecting others.

I But it’s difficult to be precise about the detailedorganization of coordinates within the mental lexicon.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

28/45

Semantic network-Cluster of coordinates


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

29/45

Semantic network-Collocation

The following material is extracted from Anna Vogel Sosa’s article. [6]

I They hypothesized units larger than the traditional word, such astwo word collocations and phrases, may be stored in the mentallexicon and accessed holistically.

I The mechanism determining this constituency is the frequencywith which items occur together in natural, connected speech: thecollocational frequency.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

30/45

Semantic network-Collocation

The experiment results

I High collocational frequency (Group 4 ) has the lowestpercentage of accurate responses at 37%.

I Low collocational frequency (Group 1 and Group 2) hada higher percentage of accurate responses at 47% and60%.

The experiment conclusion :

I Through frequent use, collocations and phrases maybecome chunked and stored as single processing units.

I Accessing of as a constituent of kind of might entail aprocess of morphological decomposition or require theuse of explicit language knowledge.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

31/45

Semantic network - Synonymy

I pursue < chase.You can pursue knowledge but you can’t chase it.

I wide ⇐⇒ broad.A wide choice (broad choice ) of sites can be madeavailable .

I wide < broad.The table is textcolormyredwide.

Summary :Speakers need to be permanently ”tuned in ” to the usagesof their language.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

32/45

Semantic network - Superordinates

I Mental lexicon might be organized in hierarchicalstructures.

I Links between hyponyms and their superordinates areoverall somewhat weaker.

I Some links are firmly than others. So human use thesefirm connections in conjunction with their reasoningability to make other as they are needed.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

33/45

Summary of semantic models

According to previous slides, we have introduced

I The Hierarchical Network Model (slides 32)

I The Semantic Feature Model (slides 14)

I The Spreading Activation Model (slides 38)

I The ACT and WordNet Models (slides 31)


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

34/45

Part of Speech


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

35/45

Strong Lexicalist View

I founded in 1970s

I Bsed on Lexical functional grammar theory (LFG)

I LFG views language as being made up of multipledimensions of structures.

I Among all of the structures, syntax is the base.

I The words in minds are listed as a dictionary, grammarclass, semantic, phonology and etc.

I Electroencephalography (EEG) studies indicates that insentence comprehension, the grammatical class of aword is accessed very early.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

36/45

Strong Lexicalist View-example


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

37/45

Combinatiorial View.

I Based on 1950s, Chomsky’s theory.

I A set of rules that generates an endless variety ofsentences that considered grammatically correct.

I Generativist approaches aim at finding universallyapplicable definitions of grammatical categories,including of nouns and verbs.

I Grammatical class (e.g. morpho-syntacticinformation) is part of combinatorial processes thatapply to words during the processing of sentences.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

38/45

Emergentist View

I Elman, 2014 ” An alternative view of the mentallexicon”

I Simple Recurrent Network to predict next word.

I grammatical class is a property emerging from acombination of constraints, semantic constraints is themost important.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

39/45

Part of Speech-neural models


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

40/45

Part of Speech-neural models summary

Neural systems : The processing of words from differentgrammatical classes will engage partial separable networks.

I Neural separability between the processing of object andaction words.

I Emergentist view : to specific word into one or theother class, pragmatic�semantic cues play the mostimportant role.

I Action knowledge : fronto-parietal networks.

I Object knowledge : inferotemporal networks.

I Reliability of distributional information : Left prefrontal.


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

41/45

Outline




Semantic maps

Summary


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

42/45

Semantic maps in brain

In this section, we are going to visualize human brainresponses to complex natural stimuli.We are going to visit a website to show you the semanticmaps.http://gallantlab.org/huth2016/

http://gallantlab.org/huth2016/


Mei-Shin Wu


The major questions



Fix-Fuzzy issue

Prototype theory

Mental Model

semantic networks


Part of Speech

Semantic maps

Summary

43/45

Summary

I Words are not randomly floating in human brain.

I Words have fuzzy boundaries.

I There are various Mental models have been proposed.

I Neuroscience provided us a semantic maps to visualizethe semantic system in human brain.


Mei-Shin Wu

References

44/45

References I

Christian J Fiebach and Angela D Friederici.“Processing concrete words: fMRI evidence against aspecific right-hemisphere involvement”. In:Neuropsychologia 42.1 (2004), pp. 62–70.

Alexander G Huth et al. “Natural speech reveals thesemantic maps that tile human cerebral cortex”. In:Nature 532.7600 (2016), pp. 453–458.

Ray Jackendoff and John C Marshall. “bookreviews-Foundations of Language: Brain, Meaning,Grammar, Evolution”. In: Nature 417.6888 (2002),pp. 488–488.

Eleanor Rosch. “Cognitive representations of semanticcategories.” In: Journal of experimental psychology:General 104.3 (1975), p. 192.


Mei-Shin Wu

References

45/45

References II

Eleanor Rosch et al. “Basic objects in naturalcategories”. In: Cognitive psychology 8.3 (1976),pp. 382–439.

Anna Vogel Sosa and James MacFarlane. “Evidence forfrequency-based constituents in the mental lexicon:Collocations involving the word of”. In: Brain andlanguage 83.2 (2002), pp. 227–236.

words in the mind: an introduction to the mental...

Documents