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TOWARDS A CONTROL THEORY OF ATTENTION. by John Taylor Department of Mathematics King’s College London, UK emails: [email protected] EC GNOSYS/MATHESIS/HUMAINE; UK:EPSRC/BBSRC. ATTENTION: SUGGESTED AS HIGHEST CONTROL SYSTEM IN THE BRAIN FILTERS OUT ALL BUT MOST IMPORTANT - PowerPoint PPT Presentation
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TOWARDS A CONTROL THEORY OF ATTENTION
by
John Taylor
Department of Mathematics
King’s College London, UK
emails: [email protected]
EC GNOSYS/MATHESIS/HUMAINE; UK:EPSRC/BBSRC
ATTENTION: SUGGESTED AS HIGHEST CONTROL SYSTEM IN THE BRAIN
FILTERS OUT ALL BUT MOST IMPORTANT
INVOLVED IN EXECUTIVE BRAIN FUNCTIONS
BASIC QUESTION:
HOW IS THE EXECUTIVE CONTROL CREATED IN THE BRAIN BY ATTENTION?
COLLEAGUES
King’s College London (CNS Group):
N Taylor (KCL EPSRC Modelling Attn)
N Fragopanagos (IABB: Attn/ Emotion Effect simulation + fMRI/EEG/ Partners)
M Hartley (EC: Mathesis)
C Pantev (KCL/Sunderland: EC GNOSYS Attn)
N Korsten (KCL: EC HUMAINE Emotion & Attention Simulation)
CONTENTS
1) ATTENTION AS CONTROL
2) CONTROL MODEL FOR ATTENTION
3) EXECUTIVE FUNCTIONS BY ATTENTION
4) CONCLUSIONS
1. ATTENTION AS CONTROL
ATTENTION = SELECTION OF PART OF SCENE FOR ANALYSIS (acts as ‘filter’ on input)
AMPLIFICATION OF ATTENDED + INHIBITION OF DISTRACTORS(in sensory & motor cortices, & higher sites)
DETECT ATTENTION CONTROL SIGNAL IN NETWORK OF CORTICAL REGIONS
ATTENTION MOVEMENT BY NETWORK OF BRAIN
SITES:
POSTERIOR (sensory)
PARIETAL (control)
FRONTAL (control) Shifting Attention Network (Corbetta, PNAS 95:831, 1998)
INCREASED ACTIVITY LEVEL WHEN ATTENTION DIRECTED TO SENSORY INPUT (from early EEG & PET studies, now fMRI, MEG, including increased -synchronisation for binding, and single cell)
Modulation of V4 Cell Response (Maunsell et al, J NSci 19:431, 1999)
FIG. 2. Data from one V4 cell showing enhanced responses in the attended mode (black) relative to the unattended mode (gray)
OVERALL: ATTENTION MOVEMENT INVOLVES BRAIN SITES WITH 2 DIFFERENT FUNCTIONS:
AMPLIFICATION/DECREASE OF SENSORY INPUT(in sensory & motor cortices) CREATION OF CONTROL SIGNALS TO DO THIS(in parietal & frontal cortices):
THIS DIFFERENTIATES AREAS OF CORTEX, NOT LAYERS?
EXPECT SITES WITH SPECIFIC FUNCTIONS TO ACHIEVE THIS CONTROL(goals, monitors/errors, feedback signals, control generators)
CONTROLLER CONTROLLED
PFC/PL/TPJ Sensory/Motor CX
Simulations of single cell (+) recordings in monkey (Desimone et al, J Nsci 1999) (with NT/MH): σπ
y = 0.5164x + 0.0844R2 = 0.8704
-1
-0.5
0
0.5
1
-1 -0.5 0 0.5 1
SE
SI
y = 0.8549x + 0.1389
-1
-0.5
0
0.5
1
-1 -0.5 0 0.5 1
SE
SI
y = 0.144x + 0.1411R2 = 0.3263
-1
-0.5
0
0.5
1
-1 -0.5 0 0.5 1
SES
I
Monkey attends awayfrom RF of cell
Plot SI = sensitivity index = (P+R) – RAgainst SE = selectivity index= P - R
Attend probe
Attend reference
CONCLUDE: slope = 1/(1+u), where u = attn level ratio P/R= 1, 1/5, 5 (& prove mathematically) = Experimental values
Simulation Results (NT/JGT/MH: IJCNN05, NN Spec Issue) Additive => 2 groups of neurons (attend
probe/attend reference Not same regression
lines as for original line => only contrast gain => sigma-pi feedback
w(i,j,k)u(j)u(k)SE = (P+R) – RSI = P - RFeedback Input
2. CREATING A CONTROL MODEL FOR ATTENTION Engineering control in motor control Controlled state variables = End points of
responders (finger/arm/legs) Control signals = Joint toque For Attention: Controlled state variables =
attended posterior activities Controlled signals = attention movement State = ATTENDED (filtered) State (NO
DISTRACTORS: prevented accessing WM buffer; hold in posterior cortices )
CONTROL MODEL FOR ATTENTION
VISUAL ATTENTION CONTROL MODEL (Corollary Discharge of Attention Movement CODAM):
Goals AttentionController Visual CX
Forward(predicts)
ObjectsMonitor(errors)
PFC
PL/ACG PFC/PL
(move attention)
TL/VLPFC
PL
Buffer WM
Simulation of benefit of attention to space (Posner benefit paradigm) Use simple architecture (ballistic control) Goal module: 3 nodes (L, R, & Central) IMC & Object modules ditto, with lateral
inhibition Architecture (ballistic attention control):
IN→OBJ←IMC←GOAL
SIMULATION OF SENSORY ATTENTION MOVEMENT (with M Rogers, Neural Networks 15:309-326, 2002)
Figure of Invalid Cueing (Posner Benefit - exogenous) Figure of Invalid Cueing (Posner Benefit endogenous)
Figure of Validity Benefit as function of CTOA
CONCLUSIONS ON ATTENTION
ATTENTION MOVEMENT = CONTROL SYSTEM DEVELOP CONTROL FRAMEWORK FOR IT 2 SORTS OF ATTENTION UNDER CONTROL:
sensory motor
VARIOUS CONTROL MODULES SUPPORTED BY DATA (attention control, goals, buffer/forward model, monitor)
APPLY TO SIMULATE (among other’s simulations):*visual attention control *joint visual/motor attention control learning(M Rogers & JGT) (NF & JGT)*attention v emotion *attention & value(NF, NK, JGT) (NT , MH, JGT)
3. ATTENDING TO EXECUTIVE FUNCTIONS Executive functions (PFC/PL): Rehearsal/refreshment Comparison of goals with new (post) activity Transform buffered material to new state Retrieval cues for long-term memory Stimulus value maps for biasing attention Internal models (FM/IMC) for reasoning ……..
Modelling Rehearsal (NK et l, NNs 2006)(as refreshing buffered material)
Basic architecture (multiplicative feedbackwith recurrence):
Results in terms of refreshing mostdecaying neurons
Fit recent brain imaging data on rehearsal
Modelling Value Map Learning for Goal Creation (NT/MH/JGT) (by TD from OFC-> IFG -> dorsal
route) G-Brain Architecture:Beforetraining(OFC)
After training(OFC)
IFG
FEF/SPL/Dorsal(attach value)
Modelling limbic value map effects on attention guidance (NF/NK/JGT)
Architecture:
Modelling limbic value map effects on attention guidance (NF/NK/JGT)Effective fMRI results (agrees well with experiment):
=> Fit experimental fMRI data on differences in U/P/N stimulus activities
Modelling reasoning (MH/NT/JGT)(by FM/IMC/WM triplets + attention)Drives
Goals IMC
RewardsIMC’IMC’’
Modify goal values to create subgoals
Basic drives(hunger)
Create actions (virtual if inhibited)
GO (if successful) & inhibit goalNOGO (inhibit goal and next goal valid
Present state
FM used in IMC learning& in learning by copying
4. CONCLUSIONS
Attention as controller (->controlled) Biased by stimulus values (from OFC) Can model increasing numbers of executive
functions under attention Need attention to prevent ‘internal chaos’
from unwanted internal representations Need to create ‘attention control’ system
theory (for different modalities/ executive function/ emotion bias/LTM interaction)