1. TOWARDS A CONTROL THEORY OF ATTENTION by John Taylor Department of Mathematics King’s College London, UK emails: john.g.taylor@kcl.ac.ukjohn.g.taylor@kcl.ac.uk EC GNOSYS/MATHESIS/HUMAINE; UK:EPSRC/BBSRC

2.  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?

3. 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)

4. CONTENTS 1) ATTENTION AS CONTROL 2) CONTROL MODEL FOR ATTENTION 3) EXECUTIVE FUNCTIONS BY ATTENTION 4) CONCLUSIONS

5. 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

6. ATTENTION MOVEMENT BY NETWORK OF BRAIN SITES:  POSTERIOR (sensory)  PARIETAL (control)  FRONTAL (control) Shifting Attention Network (Corbetta, PNAS 95:831, 1998)

7. 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)

8. 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)

9. Simulations of single cell (+) recordings in monkey (Desimone et al, J Nsci 1999) (with NT/MH): σπ Monkey attends away from RF of cell Plot SI = sensitivity index = (P+R) – R Against 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

10. 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) – R SI = P - R Feedback Input

11. 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 )

12. CONTROL MODEL FOR ATTENTION VISUAL ATTENTION CONTROL MODEL (Corollary Discharge of Attention Movement CODAM): Buffer WM

13. 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

14. 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

15. 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)

16. 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 ……..

17. Modelling Rehearsal (NK et l, NNs 2006) (as refreshing buffered material) Basic architecture (multiplicative feedback with recurrence): Results in terms of refreshing most decaying neurons  Fit recent brain imaging data on rehearsal

18. Modelling Value Map Learning for Goal Creation (NT/MH/JGT) (by TD from OFC-> IFG -> dorsal route) G-Brain Architecture: Before training (OFC) After training (OFC) IFG FEF/SPL/Dorsal (attach value)

19. Modelling limbic value map effects on attention guidance (NF/NK/JGT) Architecture:

20. 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

21. Modelling reasoning (MH/NT/JGT) (by FM/IMC/WM triplets + attention) Drives Goals IMC Rewards IMC’ IMC’’ Modify goal values to create subgoals Basic drives (hunger) Create actions (virtual if inhibited) GO (if successful) & inhibit goal NOGO (inhibit goal and next goal valid Present state FM used in IMC learning & in learning by copying

22. 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)