Attention as a Controller

Nienke J.H. Korsten, Nikos Fragopanagos, Matthew Hartley, Neill Taylor, and John G. Taylor

Soft Computing Laboratory 김 희 택

Neural Networks, 2006 Special Issue

Motivation

• Attention is a crucial pre-requisite for awareness or consciousness

• There are already various models of attention which have been studied in the recent past

– Influential ‘biased competition’ model of attention (DeS-imone & Duncan, 1995)

– Neural network based models involving large scale simu-lations, such as those of Deco and Rolls (2005) or of Mozer and Sitton (1998)

• However these models of attention had not a clear functional model guiding their construction

• Develop a more detailed neural model framework to help understand the nature of networks involved in higher order cognitive processes 2

Engineering Control Theory

• Plant and control– A system able to provide a control signal to a given plant– Various observable values are assumed such as the tem-

perature or concentrations• These observables are used to determine how to control the

plant

• Components of engineering control systems

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<An overall engineering control model of a plant>

Control model of the plant (1)

• The observation process is made by a specific module– Provides either a partial or complete description of the

state of the plant

• Any delay in observation feedback can be overcome by a fast forward model

– The updated estimate of the plant state can then be used to correct the IMC response if it is in error

• The IMC functions is processed by using both a direct goal signal as well as an error signal

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Control model of the plant (2)

• A feed back error learning (FBEL) signal from the monitor can be used to train the IMC and the goals and forward modules

• IMC can be also controlled by feedback from the plant– Feedback can be used in directly without being combined

with the forward model

• If Observer feedback has little delay, then a forward model will not be needed

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CODAM Model

• CODAM model– Corollary Discharge of Attention Movement– Engineering control approach to attention– To help develop a more detailed neural framework to help

understand the nature of networks in cognitive process

• CODAM architecture

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CODAM modules (1)

• Object map (associative cortices)– The primary and associative cortices– Acting as the ‘plant’ in an engineering control approach

• IMC (parietal cortex)– ‘Inverse Model Controller’– The attention control signal generator– Control signal to move attention to a spatial position or

to object features

• Goals (prefrontal cortex)– Used to bias the attention movement control signal gen-

erated by the IMC– “Move attention to a particular place when a fixation

light is extinguished”

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CODAM modules (2)

• Working memory (WM) (parietal cortex)– Estimated state of attended lower level activity at

present time or as predictor for future use– ‘Attention state’ in engineering control terms

• Corollary discharge (CD)– Produce fast error correction by comparison between the

attention control signal and the goal in the monitor– Also plays an important role in self-actions

• Monitor (cingulated cortex)– Generate an error signal computed by subtracting pre-

dicted attended object signal from the goal signal

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Modeling by using CODAM

1. Rehearsal of desired inputs in working memory– Memorizing phone number

2. Replacement

3. Transformation of buffered material into a new, goal directed form

– Spatial rotation of an image held in the mind

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Rehearsal

• Rehearsal process is attentionally driven in working memory

• Applied the CODAM architecture to model WM main-tenance of multiple items through a rehearsal process

• Attentional focus causes the activation of nodes in WM to be boosted

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Experiment for Rehearsal

• Encoding phase

• Delay phase– 6 second for memorizing

• Test phase– Same or different?

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Extended CODAM model for Rehearsal (1)

• Corollary discharge – Be discarded from the original model, since its functional-

ity was obsolete in the current

• GOALS endogenous– Encode sample set of stimuli– Remember sample set of stimuli until test set appears– Compare sample set to test

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Extended CODAM model for Rehearsal (2)

• MONITOR maintain– Monitor the level of activations in WM– Trigger a change of attentional focus to a less activated

node– Thereby creating longer term maintenance

• WM– The internal maintenance system in this module has a de-

cay time of approximately 3.5 second

• IMC– During the encode and test phases, it amplifies represen-

tations in object map– during the delay phase, it amplifies recurrent connections

in WM

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Extended CODAM model for Rehearsal (3)

• MONITOR compare – Comparison between the sample set (in WM) and the test

set (in OBJ) and to generate an output (change/no change)

• OBJ– Constructed as a conjoined map between feature and ob-

ject for simplicity

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Simulation Result

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Replacement

• Replacing a given element in working memory by a new stimulus entering the perceptual system

• Replacement process– Activation by a suitable cue indicating that replacement

should occur– Annihilation of the previous contents of the WM buffer by

suitable feedback signals– Attention being moved to focus on the new stimulus– Amplification of the new stimulus to gain access to the

WM buffer site

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Extended CODAM model for Replace-ment

• Replacement goal– Activated when a replacement cue is detected– Activate an annihilator module– Bias the IMC for movement of the attention focus so that

attention is now directed to the new stimulus

• Annihilator module– Send an inhibitory signal to the WM buffer site to remove

any activity from previous stimuli

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Paradigm for Transformation

• A simple ‘2 sticks’ paradigm used on chimpanzees– 2 sticks, S1 and S2, are on the floor outside a chimp’s

cage– We take a button to press by S2, which leads to food– However, S2 is out of direct reach of the chimp and S2 is

within reach of S1

• How does the chimp proceed to reason to grasp S1, pull S2 towards it, release S1 and grasp S2, and then press the button to get the reward?

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The Reasoning Steps (1)

• Step I : – Try to press the button directly (or virtually)

• Use the IMC to generate an action signal with the desired state being the button to press

– NOGO signal is generated• Step II :

– Grasp S1 and try to press the button• By changing parameters in the IMC

– Find there is no possible action to achieve the desired goal

– The grasping FM/IMC pair parameters are set back to their default values

• Step III :– View S2 and decide virtually to grasp S2 and try to press

button• Needs a new FM/IMC pair, assumed already learnt

– A GO signal results, and S2 now acts as a subgoal, sited in the goal map 19

The Reasoning Steps (2)

• Step IV :– With subgoal of moving S2 to the side of the cage, virtu-

ally grasp SI and find this can be done• With increased parameters of the FM/IMC signal of grasping

S2– GO signal is generated

• Step V :– Make actual steps of grasp S1, use to move S2 close to

cage, release S1, grasp S2, press button– Obtain reward

• The above sequence of reasoning processes needs not only the trained FM/IMC pairs but also suitable WM buffer sites to hold the results of virtual processes, such as ‘sub goal’

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The Interaction of Attention & Emotion

• Emotional content can modify and update the goals and consequently alter the direction of attention

– There is ample evidence from psychology and neuro-science that show emotional objects can capture atten-tion

• Contruct extended CODAM by adding an amygdala module

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Extended CODAM model for Emotion

• Amygdala module– Gets activated fast from posterior sites and can therefore

feedback to those sites to enhance representations of emotional objects

– It can also interact with the OFC module thus it can influ-ence attention

• OFC module (Orbitofrontal Cortex)– Activated by the amygdal module– Can be used to interrupt or assist ongoing cognitive pro-

cessing that is controlled by the DLPFC

• Above extended model can explains interaction of at-tention and emotion reported in various experiments

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Experiment of Yamasaki

• Experimental design– Standards : consisted of squares of varying sizes and col-

ors– Targets : consisted of circles of varying sizes and colors– Emotional distracters : consisted of aversive pictures that

included unpleasant themes of human violence, mutila-tion, and disease

– Neutral distracters : consisted of pictures of ordinary ac-tivities

– Task• Press a button with the right index finger for any target (a

circle)• Press a button with the right middle finger for any other in-

put

• Result– Subjects took longer to respond to emotional distracters

than to the targets or neutral stimuli by about 50 ms– Unpleasant stimuli activating the AMYGDALA module,

which then inhibits the goal modules

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Summary and Conclusion

• Presented a computational account of attention, the CODAM model

• Extended CODAM model to simulate working memory– Applied extended model to rehearsal, replacement,

transformation

• CODAM also can be adapted to emotional paradigms– The influence of attention vs emotion

• CODAM is relevant to many different neural pro-cesses, including working memory and reasoning as well as emotional processes

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E.N.D