TOWARDS A CONTROL THEORY OF ATTENTION

by

John Taylor

 

Department of Mathematics

King’s College London, UK

 

emails: john.g.taylor@kcl.ac.uk

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)