the icsi/berkeley neural theory of language project learning early constructions (chang, mok) ecg
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The ICSI/Berkeley Neural Theory of Language Project
Learning early constructions (Chang, Mok)
ECG
Moving from Spatial Relations to Verbs
• Open class vs. closed class
– How do we represent verbs (say of hand motion)
• Can we build models of verbs based on motor control primitives?
• If so, how can models overcome central limitations of Regier’s system?
– Inference
– Abstract uses
Coordination of Pattern Generators
Coordination
• PATTERN GENERATORS, separate neural networks that control each limb, can interact in different ways to produce various gaits.
– In ambling (top) the animal must move the fore and hind leg of one flank in parallel.
– Trotting (middle) requires movement of diagonal limbs (front right and back left, or front left and back right) in unison.
– Galloping (bottom) involves the forelegs, and then the hind legs, acting together
Preshaping While Reaching to Grasp
Internal Model and Efference Copy
Many areas code for motion parameters
Multiple, chronically implanted, intracranial microelectrode arrays would be used to sample theactivity of large populations of single cortical neurons simultaneously. The combined activity ofthese neural ensembles would then be transformed by a mathematical algorithm into continuousthree-dimensional arm-trajectory signals that would be used to control the movements of arobotic prosthetic arm. A closed control loop would be established by providing the subject withboth visual and tactile feedback signals generated by movement of the robotic arm.
Rizzolatti et al. 1998
A New PictureA New Picture
The fronto-parietal networks
Rizzolatti et al. 1998
F5 Mirror NeuronsF5 Mirror Neurons
Gallese and Goldman, TICS 1998
Category Loosening in Mirror Neurons (~60%)
(Gallese et al. Brain 1996)
Observed: A is Precision Grip
B is Whole Hand Prehension
Action: C: precision grip
D: Whole Hand Prehension
Umiltà et al. Neuron 2001
A (Full vision)A (Full vision)
B (Hidden)B (Hidden)
C (Mimicking)C (Mimicking)
D (HiddenMimicking)D (HiddenMimicking)
F5 Audio-Visual Mirror NeuronsF5 Audio-Visual Mirror Neurons
Kohler et al. Science (2002)
Summary of Fronto-Parietal Circuits
Motor-Premotor/Parietal Circuits
PMv (F5ab) – AIP Circuit
“grasp” neurons – fire in relation to movements of hand prehension necessary to grasp object
F4 (PMC) (behind arcuate) – VIP Circuit
transforming peri-personal space coordinates so can move toward objects
PMv (F5c) – PF Circuit F5c
different mirror circuits for grasping, placing or manipulating object
Together suggest cognitive representation of the grasp, active in action imitation and action recognition
Evidence in Humans for Mirror, General Purpose, and Action-Location
Neurons
Mirror: Fadiga et al. 1995; Grafton et al. 1996;Rizzolatti et al. 1996; Cochin et al. 1998;
Decety et al. 1997; Decety and Grèzes 1999;Hari et al. 1999; Iacoboni et al. 1999;
Buccino et al. 2001.
General Purpose: Perani et al. 1995; Martin et al.1996; Grafton et al. 1996; Chao and Martin 2000.
Action-Location: Bremmer, et al., 2001.
Itti: CS564 - Brain Theory and Artificial Intelligence. FARS Model
FARS (Fagg-Arbib-Rizzolatti-Sakata) Model
AIP
F5
dorsal/ventral streams
Task Constraints (F6)
Working Memory (46)
Instruction Stimuli (F2)
Task Constraints (F6)Working Memory (46?)Instruction Stimuli (F2)
AIPDorsalStream:Affordances
IT
VentralStream:Recognition
Ways to grab this “thing”
“It’s a mug”PFC
AIP extracts the set of affordances for an attended object.These affordances highlight the features of the object relevant to physical interaction with it.
MULTI-MODAL INTEGRATION
The premotor and parietal areas, rather than havingseparate and independent functions, are neurally integratednot only to control action, but also to serve the function ofconstructing an integrated representation of:
(a) Actions, together with (b) objects acted on, and (c) locations toward which actions are directed.
In these circuits sensory inputs are transformed in order toaccomplish not only motor but also cognitive tasks, such asspace perception and action understanding.
Modeling Motor Schemas
• 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.
– Should model hierarchical control (higher level motor centers to muscle extensor/flexors.
• Computational model called x-schemas (http://www.icsi.berkeley.edu/NTL)
An Active Model of Events
• At the Computational level, actions and events are coded in active representations called x-schemas which are extensions to Stochastic Petri nets.
• x-schemas are fine-grained action and event representations that can be used for monitoring and control as well as for inference.
Model Review: Stochastic Petri Nets
3
1
2
Basic Mechanism
[1]
Precondition arc
Resource arc
Inhibition arc
[1]Firing function -- conjunctive -- logistic -- exponential family
3
1
2
Firing Semantics
Model Review
1
11
1
2
Result of Firing
Model Review
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
• Generative model: action, recognition, planning , language
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
walker=Harry
goal=home
energy
walker at goal
Preshaping While Reaching to Grasp
The ICSI/Berkeley Neural Theory of Language Project
Learning early constructions (Chang, Mok)
ECG
Representing concepts using triangle nodes
triangle nodes:
when two of the neurons fire, the third also fires
Barrett Ham Container Push
dept~CS Color ~pink Inside ~region Schema ~slide
sid~001 Taste ~salty Outside ~region Posture ~palm
emp~GSI Bdy. ~curve Dir. ~ away
Chang Pea Purchase Stroll
dept~Ling Color ~green Buyer ~person Schema ~walk
sid~002 Taste ~sweet Seller ~person Speed ~slow
emp~Gra Cost ~money Dir. ~ ANY
Goods ~ thing
Feature Structures in Four Domains
Simulation hypothesis
We understand utterances by mentally simulating their content.
– Simulation exploits some of the same neural structures activated during performance, perception, imagining, memory…
– Linguistic structure parameterizes the simulation.
• Language gives us enough information to simulate
Simulation Semantics
• BASIC ASSUMPTION: SAME REPRESENTATION FOR PLANNING AND SIMULATIVE INFERENCE
– Evidence for common mechanisms for recognition and action (mirror neurons) in the F5 area (Rizzolatti et al (1996), Gallese 96, Buccino 2002, Tettamanti 2004) and from motor imagery (Jeannerod 1996)
• IMPLEMENTATION:
– x-schemas affect each other by enabling, disabling or modifying execution trajectories. Whenever the CONTROLLER schema makes a transition it may set, get, or modify state leading to triggering or modification of other x-schemas. State is completely distributed (a graph marking) over the network.
• RESULT: INTERPRETATION IS IMAGINATIVE SIMULATION!
Simulation-based language understanding
Analysis Process
SemanticSpecification
“Harry walked into the cafe.” Utterance
CAFE Simulation
Belief State
General Knowledge
Constructions
construction WALKEDform
selff.phon [wakt]meaning : Walk-Action constraints
selfm.time before Context.speech-time selfm..aspect encapsulated
Simulation specification
A simulation specification consists of:- schemas evoked by constructions- bindings between schemas
Language Development in Children
• 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 yes
juice bead girl down no more
bottle truck baby woof yum go up this more
spoon hammer shoe daddy moo whee get out there bye
banana box eye momy choo-choo
uhoh sit in here hi
cookie horse door boy boom oh open on that no
food toys misc. people sound emotion action prep. demon. social
Words learned by most 2-year olds in a play school (Bloom 1993)
Regier Model Limitations
• Scale
• Uniqueness/Plausibility
• Grammar
• Abstract Concepts
• Inference
• Representation
• Biological Realism
Learning Verb MeaningsDavid 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.
Reasoning about Actions in Artificial Intelligence (AI)
• The earliest work on actions in AI took a deductive approach
– designers hoped to represent all the system's `world knowledge' explicitly as axioms, and use ordinary logic - the predicate calculus - to deduce the effects of actions
• Envisaging a certain situation S was modeled by having the system entertain a set of axioms describing the situation
• To this set of axioms the system would apply an action - by postulating the occurrence of some action A in situation S - and then deduce the effect of A in S, producing a description of the outcome situation S'
Grasping: the action
• A set of pre-conditions in S
– free_top(y), free_hand(x), accessible(y)
• The grasp action (effect axiom):
– Result(Grasp(x,y, S), hold(x,y,S’))
• A set of effects describing the new situation S’
– Hold(x,y), not(free-hand(x))
Actions
• An action is described as an axiom linking preconditions (literals and terms true in the before situation) to effects (literals and terms true in the after situation).
• The action specification is called an effect axiom
Problems with action concepts
• Frame problem
• Qualification problem
• Ramification problem
The Frame Problem
• Which things don’t change in an action
– S1: blue(x), on_table(x), free_hand(y)
– Action grasp(y,x)
– S2: in_hand(x,y), hold(x,y), ?
Frame axioms are needed in logic
• Consider some typical frame axioms associated with the action-type:
• move x onto y.
– If z != x and I move x onto y, then if z was on w before, then z is on w after.
– If x is blue before, and I move x onto y, then x is blue after.
Active Representations don’t need frame axioms
• X-schemas directly model change, so no need for frame axioms. Also, they deal with concurrency, so no need to treat one action at a time.
• Based on x-schema type models there are a new set of logics called resource logics which attempt to model the frame problem directly.
Ramification Problem
How do I specify all the effects
– Direct (if I move, I change my location) and
– Indirect (things that were accessible before I moved may not be anymore)
• Central issue is to propagate changes of an action to all the connected knowledge that might be impacted.
• How might the brain do this?
• Spreading Activation