TOWARDS A CONTROL THEORY OF ATTENTION

1. 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

2. ATTENTION: SUGGESTED AS HIGHEST CONTROL SYSTEM IN THE BRAIN • FILTERS OUT ALL BUT MOST IMPORTANT • INVOLVED IN EXECUTIVE BRAIN FUNCTIONS • BASIC QUESTION:

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 • ATTENTION AS CONTROL • CONTROL MODEL FOR ATTENTION • EXECUTIVE FUNCTIONS BY ATTENTION • 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 Attend probe Plot SI = sensitivity index = (P+R) – R Against SE = selectivity index = P - R 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) FEF/SPL/Dorsal (attach value) IFG After training (OFC)

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 Basic drives (hunger) Create actions (virtual if inhibited) IMC Goals GO (if successful) & inhibit goal NOGO (inhibit goal and next goal valid Modify goal values to create subgoals IMC’ IMC’’ Rewards FM used in IMC learning & in learning by copying Present state

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)