NTL – Converging Constraints
• Basic concepts and words derive their meaning
from embodied experience.
• Abstract and theoretical concepts derive their
meaning from metaphorical maps to more basic
embodied concepts.
• Structured Connectionist Models can capture
both of these processes nicely.
• Grammar extends this by Constructions:
pairings of form with embodied meaning.
Simulation-based language understanding
“Harry walked to the cafe.”
Utterance
Constructions
Analysis Process
General
Knowledge
Belief State
Schema
walk
Trajector
Harry
Cafe
Goal
cafe
Simulation
Specification
Simulation
The ICSI/Berkeley
Neural Theory of Language Project
Background: Primate Motor Control
• Relevant requirements (Stromberg, Latash, Kandel,
Arbib, Jeannerod, Rizzolatti)
– Should model coordinated, distributed, parameterized
control programs required for motor action and
perception.
– Should be an active structure.
– Should be able to model concurrent actions and
interrupts.
• Model
– The NTL project has developed a computational model
based on that satisfies these requirements (x- schemas).
– Details, papers, etc. can be obtained on the web at
http://www.icsi.berkeley.edu/NTL
Active representations
• Many inferences about actions derive from what we know
about executing them
• Representation based on stochastic Petri nets captures
dynamic, parameterized nature of actions
walker at goal
energy
walker=Harry
goal=home
Walking:
bound to a specific walker with a
direction or goal
consumes resources (e.g., energy)
may have termination condition
(e.g., walker at goal)
ongoing, iterative action
Somatotopy of Action Observation
Foot Action
Hand Action
Mouth Action
Buccino et al. Eur J Neurosci 2001
Active Motion Model
Evolving Responses
of Competing
Models over Time.
Nigel Goddard
1989
Language Development in
Children
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0-3 mo: prefers sounds in native language
3-6 mo: imitation of vowel sounds only
6-8 mo: babbling in consonant-vowel segments
8-10 mo: word comprehension, starts to lose sensitivity to
consonants outside native language
12-13 mo: word production (naming)
16-20 mo: word combinations, relational words (verbs,
adj.)
24-36 mo: grammaticization, inflectional morphology
3 years – adulthood: vocab. growth, sentence-level
grammar for discourse purposes
cow
apple
ball
juice
bead
girl
bottle
truck
baby
w oof
yum
go
up
this
no
m ore
m ore
spoon
ham m er
shoe
d ad d y
m oo
w hee
get
out
there
bye
banana
box
eye
m om y
uhoh
sit
in
here
hi
cookie
horse
d oor boy
choochoo
boom
oh
open
on
that
no
food
toys
yes
misc.
people
d ow n
sound emotion action
prep.
demon. social
Words learned by most 2-year olds in a play school (Bloom 1993)
Learning Spatial Relation Words
Terry Regier
A model of children learning spatial relations.
Assumes child hears one word label of scene.
Program learns well enough to label novel scenes
correctly.
Extended to simple motion scenarios, like INTO.
System works across languages.
Mechanisms are neurally plausible.
Learning
System
dynamic relations
(e.g. into)
structured connectionist
network (based on
visual system)
We’ll look at the
details next lecture
Limitations
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Scale
Uniqueness/Plausibility
Grammar
Abstract Concepts
Inference
Representation
Biological Realism
Constrained Best Fit in Nature
inanimate
physics
chemistry
biology
vision
language
animate
lowest energy
state
molecular
minima
fitness, MEU
neuroeconomics
threats,
friends
errors,
NTL
Learning Verb Meanings
David Bailey
A model of children learning their first verbs.
Assumes parent labels child’s actions.
Child knows parameters of action, associates with
word
Program learns well enough to:
1) Label novel actions correctly
2) Obey commands using new words (simulation)
System works across languages
Mechanisms are neurally plausible.
Motor Control (X-schema) for SLIDE
Parameters for the SLIDE X-schema
Feature Structures for PUSH
System Overview
Learning Two Senses of PUSH
Model merging based on Bayesian MDL
Training Results
David Bailey
English
• 165 Training Examples, 18 verbs
• Learns optimal number of word senses (21)
• 32 Test examples : 78% recognition, 81% action
• All mistakes were close lift ~ yank, etc.
• Learned some particle CXN,e.g., pull up
Farsi
• With identical settings, learned senses not in
English
Learning Two Senses of PUSH
Model merging based on Bayesian MDL
Constrained Best Fit in Nature
inanimate
physics
chemistry
biology
vision
language
animate
lowest energy
state
molecular
minima
fitness, MEU
neuroeconomics
threats,
friends
errors,
NTL
Model Merging and Recruitment
Word Learning requires “fast mapping”.
Recruitment Learning is a Connectionist
Level model of this.
Model Merging is a practical Computational
Level method for fast mapping.
Bailey’s thesis outlines the reduction and some
versions have been built.
The full story requires Bayesian MDL, later.
The Idea of Recruitment Learning
K
Y
X
N
B
F = B/N
Pno link  (1  F )
BK
• Suppose we want to
link up node X to
node Y
• The idea is to pick
the two nodes in the
middle to link them
up
• Can we be sure that
we can find a path to
get from X to Y?
the point is, with a fan-out of 1000,
if we allow 2 intermediate layers,
we can almost always find a path
Recruiting triangle nodes
• Let’s say we are trying to remember a green circle
• currently weak connections between concepts (dotted lines)
has-color
blue
has-shape
green
round
oval
Strengthen these connections
• and you end up with this picture
has-color
has-shape
Green
circle
blue
green
round
oval