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o u t p u t y. {. 1 if net > 0 0 otherwise. w 0. i 0 =1. w 1. w 2. w n. . . . i 1. i 2. i n. i n p u t i. Abstract Neuron. Link to Vision: The Necker Cube. Constrained Best Fit in Nature. inanimate animate. Computing other relations.

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Abstract neuron

o u t p u t y

{

1 if net > 0

0 otherwise

w0

i0=1

w1

w2

wn

. . .

i1

i2

in

i n p u t i

Abstract Neuron




Computing other relations
Computing other relations

  • The 2/3 node is a useful function that activates its outputs (3) if any (2) of its 3 inputs are active

  • Such a node is also called a triangle node and will be useful for lots of representations.


Triangle nodes and mccullough pitts neurons
Triangle nodes and McCullough-Pitts Neurons?

Relation (A)

Object (B)

Value (C)

A

B

C


They all rose
“They all rose”

triangle nodes:

when two of the abstract neurons fire, the third also fires

model of spreading activation


Basic ideas
Basic Ideas

  • Parallel activation streams.

  • Top down and bottom up activation combine to determine the best matching structure.

  • Triangle nodes bind features of objects to values

  • Mutual inhibition and competition between structures

  • Mental connections are active neural connections


  • Behavioral Experiments

  • Identity – Mental activity is Structured Neural Activity

  • Spreading Activation— Psychological model/theory behind priming and interference experiments

  • Simulation — Necessary for meaningfulness and contextual inference

  • Parameters — Govern simulation, strict inference, link to language


Bottom up vs top down processes
Bottom-up vs. Top-down Processes

  • Bottom-up: When processing is driven by the stimulus

  • Top-down: When knowledge and context are used to assist and drive processing

  • Interaction: The stimulus is the basis of processing but almost immediately top-down processes are initiated


Stroop effect
Stroop Effect

  • Interference between form and meaning


Name the words
Name the words

BookCarTableBoxTrashManBed

CornSitPaperCoin Glass HouseJar

KeyRugCatDoll Letter BabyTomato

CheckPhone Soda DishLampWoman


Name the print color of the words
Name the print color of the words

BlueGreenRed YellowOrangeBlackRed

PurpleGreenRedBlueYellowBlackRed

GreenWhiteBlueYellow Red BlackBlue

WhiteRed Yellow GreenBlackPurple


Procedure for experiment that demonstrates the word-superiority effect. First the word is presented, then the XXXX’s, then the letters.


Word superiority effect reicher 1969
Word-Superiority Effect word-superiority effect. First the word is presented, then the XXXX’s, then the letters.Reicher (1969)

  • Which condition resulted in faster & more accurate recognition of the letter?

    • The word condition

    • Letters are recognized faster when they are part of a word then when they are alone

    • This rejects the completely bottom-up feature model

    • Also a challenge for serial processing


Connectionist model mcclelland rumelhart 1981
Connectionist Model word-superiority effect. First the word is presented, then the XXXX’s, then the letters.McClelland & Rumelhart (1981)

  • Knowledge is distributed and processing occurs in parallel, with both bottom-up and top-down influences

  • This model can explain the Word-Superiority Effect because it can account for context effects


Connectionist model of word recognition
Connectionist Model of word-superiority effect. First the word is presented, then the XXXX’s, then the letters.Word Recognition


Interaction in language processing pragmatic constraints on lexical access
Interaction in language processing: word-superiority effect. First the word is presented, then the XXXX’s, then the letters.Pragmatic constraints on lexical access

Jim Magnuson

Columbia University


Information integration
Information integration word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

  • A central issue in psycholinguistics and cognitive science:

    • When/how are such sources integrated?

  • Two views

    • Interaction

      • Use information as soon as it is available

      • Free flow between levels of representation

    • Modularity

      • Protect and optimize levels by encapsulation

      • Staged serial processing

      • Reanalyze / appeal to top-down information only when needed


Reaction times in milliseconds after they all rose
Reaction Times in Milliseconds after: “They all rose” word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

0 delay 200ms. delay


Example modularity and word recognition
Example: Modularity and word recognition word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

  • Tanenhaus et al. (1979) [also Swinney, 1979]

    • Given a homophone likerose, and a context biased towards one sense, when is context integrated?

      • Spoken sentence primes ending in homophones:

        • They all rose vs. They bought a rose

      • Secondary task: name a displayed orthographic word

        • Probe at offset of ambiguous word: priming for both“stood” and “flower”

        • 200 ms later: only priming for appropriate sense

  • Suggests encapsulation followed by rapid integration

  • But the constraint here is weak -- overestimates modularity?

  • How could we examine strong constraints in natural contexts?


Allopenna magnuson tanenhaus 1998
Allopenna, Magnuson & Tanenhaus (1998) word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Eye

Eye camera

tracking

computer

Scene camera

‘Pick up the beaker’


Do rhymes compete

TRACE predictions word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Do rhymes compete?

  • Cohort (Marlsen-Wilson): onset similarity is primary because of the incremental nature of speech

    (serial/staged; Shortlist/Merge)

    • Cat activates cap, cast, cattle, camera, etc.

    • Rhymes won’t compete

  • NAM (Neighborhood Activation Model; Luce): global similarity is primary

    • Cat activatesbat, rat, cot, cast, etc.

    • Rhymes among set of strong competitors

  • TRACE (McClelland & Elman): global similarity constrained by incremental nature of speech

    • Cohorts and rhymes compete, but with different time course


Allopenna et al. Results word-superiority effect. First the word is presented, then the XXXX’s, then the letters.


Study 1 conclusions
Study 1 Conclusions word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

  • As predicted by interactive models, cohorts and rhymes are activated, with different time courses

  • Eye movement paradigm

    • More sensitive than conventional paradigms

    • More naturalistic

    • Simultaneous measures of multiple items

    • Transparently linkable to computational model

  • Time locked to speech at a fine grain


Theoretical conclusions
Theoretical conclusions word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

  • Natural contexts provide strong constraints that are used

  • When those constraints are extremely predictive, they are integrated as quickly as we can measure

  • Suggests rapid, continuous interaction among

    • Linguistic levels

    • Nonlinguistic context

  • Even for processes assumed to be low-level and automatic

  • Constrains processing theories, also has implications for, e.g., learnability


Producing words from pictures or from other words word-superiority effect. First the word is presented, then the XXXX’s, then the letters.: A comparison of aphasic lexical access from two different input modalities

Gary Dell

with

Myrna Schwartz, Dan Foygel, Nadine Martin, Eleanor Saffran, Deborah Gagnon, Rick Hanley, Janice Kay, Susanne Gahl, Rachel Baron, Stefanie Abel, Walter Huber


Boxes and arrows in the linguistic system
Boxes and arrows in the linguistic system word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Semantics

Syntax

Lexicon

Output

Phonology

Input

Phonology


Picture Naming Task word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Semantics

Say: “cat”

Syntax

Lexicon

Output

Phonology

Input

Phonology


A 2 step interactive model of lexical access in production
A 2-step Interactive Model of Lexical Access in Production word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Semantic Features

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Step 1 lemma access
Step 1 – Lemma Access word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Activate semantic features of CAT

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Step 1 lemma access1
Step 1 – Lemma Access word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Activation spreads through network

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Step 1 lemma access2
Step 1 – Lemma Access word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Most active word from proper category is selected and linked to syntactic frame

NP

N

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Step 2 phonological access
Step 2 – Phonological Access word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Jolt of activation is sent to selected word

NP

N

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Step 2 phonological access1
Step 2 – Phonological Access word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Activation spreads through network

NP

N

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Step 2 phonological access2
Step 2 – Phonological Access word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Most activated phonemes are selected

FOG

DOG

CAT

RAT

MAT

Syl

On Vo Co

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Semantic error dog
Semantic Error – “dog” word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Shared features activate semantic neighbors

NP

N

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Formal error mat
Formal Error – “mat” word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Phoneme-word feedback activates formal neighbors

NP

N

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Mixed error rat
Mixed Error – “rat” word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Mixed semantic-formal neighbors gain activation from both top-down and bottom-up sources

NP

N

FOG

DOG

CAT

RAT

MAT

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Errors of phonological access dat mat
Errors of Phonological Access- “dat” “mat” word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Selection of incorrect phonemes

FOG

DOG

CAT

RAT

MAT

Syl

On Vo Co

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


A test of the model picture naming errors in aphasia
A Test of the Model: word-superiority effect. First the word is presented, then the XXXX’s, then the letters.Picture-naming Errors in Aphasia

“cat”

175 pictures of concrete nouns–Philadelphia Naming Test

94 patients (Broca,Wernicke, anomic, conduction)

60 normal controls


Response categories
Response Categories word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

Correct Semantic Formal Mixed Unrelated Nonword

CATDOG MAT RAT LOG DAT

Continuity Thesis:

Normal Error Pattern: 97% Correct

Random Error Pattern: 80% Nonwords

cat dog mat rat log dat

cat dog mat rat log dat


Implementing the continuity thesis
Implementing the Continuity Thesis word-superiority effect. First the word is presented, then the XXXX’s, then the letters.

2.Set processing parameters of the model so that its error pattern matches the normal controls.

Random Pattern

Model Random Pattern

cat dog mat rat log dat

Normal Controls

Model Normal Pattern

1.Set up the model lexicon so that when noise is very large, it creates an error pattern similar to the random pattern.

cat dog mat rat log dat


Lesioning the model the semantic phonological weight hypothesis
Lesioning the model: The semantic-phonological weight hypothesis

Semantic Features

Semantic-word weight: S

FOG

DOG

CAT

RAT

MAT

Phonological- word weight:

P

f

r

d

k

m

ae

o

t

g

Onsets

Vowels

Codas


Patient hypothesis CAT DOG MAT RAT LOG DAT

Correct Semantic Formal Mixed Unrelated Nonword

LH .71 .03 .07 .01 .02 .15

s=.024 p=.018.69 .06 .06 .01 .02 .17

IG .77 .10 .06 .03 .01 .03

s=.019 p=.032.77 .09 .06 .01 .04 .03

GL .29 .04 .22 .03 .10 .32

s=.010 p=.016.31 .10 .15 .01 .13 .30


Representing model patient deviations
Representing Model-Patient Deviations hypothesis

Root Mean Square Deviation (RMSD)

LH .016

IG .016

GL .043


94 new patients no exclusions
94 new patients—no exclusions hypothesis

94.5 % of variance accounted for


Conclusions
Conclusions hypothesis

The logic underlying box-and-arrow- models

is perfectly compatible with connectionist models.

Connectionist principles augment the boxes and arrows with

-- a mechanism for quantifying degreeof damage

-- mechanisms for error types and hence an explanation of the error patterns

Implications for recovery and rehabilitation


Behavioral and imaging experiments ben bergen and shweta narayan
Behavioral and Imaging Experiments hypothesisBen Bergen and Shweta Narayan

Do Words and Images Match?

  • Behavioral – Image First

    Does shared effector slow negative response?

  • Imaging – Simple sentence using verb first

    Does verb evoke activity in motor effector area?

  • Metaphor follow-on experiment

    Will “kick the idea around” evoke motor activity?


Structured Neural Computation in NTL hypothesis

The theory we are outlining uses the computational modeling mechanisms of the Neural Theory of Language (NTL).

NTL makes use of structured connectionism (Not PDP connectionism!).

NTL is ‘localist,’ with functional clusters as units.

Localism allows NTL to characterize precise computations, as needed in actions and in inferences.


  • To understand the meaning of the concept grasp, one must at least be able to imagine oneself or someone else grasping an object.

  • Imagination is mental simulation, carried out by the same functional clusters used in acting and perceiving.

  • The conceptualization of grasping via simulation therefore requires the use of the same functional clusters used in the action and perception of grasping.


  • Multi-Modal Integration hypothesis

    Cortical premotor areas are endowed with sensory properties.

    They contain neurons that respond to visual, somatosensory, and auditory stimuli.

    Posterior parietal areas, traditionally considered to process and associate purely sensory information, alsos play a major role in motor control.


    Somatotopy of Action Observation hypothesis

    Foot Action

    Hand Action

    Mouth Action

    Buccino et al. Eur J Neurosci 2001


    The simulation hypothesis
    The Simulation Hypothesis hypothesis

    How do mirror neurons work?

    By simulation.

    When the subject observes another individual doing an action, the subject is simulating the same action.

    Since action and simulation use some of the same neural substrate, that would explain why the same neurons are firing during action-observation as during action-execution.


    Conclusion 1 hypothesis

    The Sensory-Motor System Is Sufficient

    For at least one concept, grasp, functional clusters, as characterized in the sensory-motor system and as modeled using structured connectionist binding and inference mechanisms, have all the necessary conceptual properties.


    Conclusion 2 hypothesis

    The Neural Version of Ockham’s Razor

    Under the traditional theory, action concepts have to be disembodied, that is, to be characterized neurally entirely outside the sensory motor system.

    If true, that would duplicate all the apparatus for characterizing conceptual properties that we have discussed. Unnecessary duplication of this sort is highly unlikely in a brain that works by neural optimization.


    Behavioral and imaging experiments ben bergen and shweta narayan1
    Behavioral and Imaging Experiments hypothesisBen Bergen and Shweta Narayan

    Do Words and Images Match?

    Does shared effector slow negative response?

    • Imaging – Simple sentence using verb first

    • Behavioral – Image First

      Does verb evoke activity in motor effector area?


    WALK hypothesis


    GRASP hypothesis


    WALK hypothesis


    Preliminary behavior results
    Preliminary Behavior Results hypothesis

    Same Action Other Effector Same Effector

    40 Native Speakers

    Eliminate RT > 2 sec.


    5 levels of neural theory of language
    5 hypothesislevels of Neural Theory of Language

    Spatial Relation

    Motor Control

    Metaphor

    Grammar

    Cognition and Language

    Computation

    Structured Connectionism

    abstraction

    Neural Net

    SHRUTI

    Computational Neurobiology

    Triangle Nodes

    Biology

    Neural Development

    Quiz

    Midterm

    Finals


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