the selective tuning model of visual attentiontsotsos/homepage of john k_files/st...mdp mip mt v4t...

11

©J.K.Tsotsos ©J.K.Tsotsos 11

The Selective Tuning Model ofVisual Attention

The Selective Tuning Model ofThe Selective Tuning Model ofVisual AttentionVisual Attention

John K. TsotsosJohn K. Tsotsos

Dept. of Computer ScienceDept. of Computer Science

andandCentre for Vision ResearchCentre for Vision Research

York UniversityYork University


Sean CulhaneSean Culhane Neal DavisNeal DavisDavid DolsonDavid Dolson Joyce WongJoyce WongYuzhong LaiYuzhong Lai Winky WaiWinky WaiFernando NufloFernando Nuflo Pietro ParodiPietro ParodiFlorin CutzuFlorin Cutzu John MidgleyJohn MidgleyMarc PomplunMarc Pomplun

Correspondence to :Correspondence to :[email protected]@cs.yorku.cawww.cs.yorku.ca/~tsotsoswww.cs.yorku.ca/~tsotsos

ContributorsContributors

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Selective Tuning Concept (circa 1988)Selective Tuning Concept Selective Tuning Concept (circa 1988)(circa 1988)

processingpyramid

inhibited pathways

passpathways

unit of interestat top

input

Caputo & Guerra 1998Bahcall & Kowler 1999Vanduffel, Tootell, Orban (2000)

Kastner, De Weerd, Desimone, Ungerleider, 1998


top-down, coarse-to-finetop-down, coarse-to-fine WTA hierarchy for WTA hierarchy for incremental selection and incremental selection and localization localization

unselected connections are unselected connections are inhibited inhibited

WTA achieved WTA achieved through local through local gating networksgating networks

Hierarchical Winner-Take-AllHierarchical Winner-Take-AllHierarchical Winner-Take-All

Simulation

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unit and connectionin the interpretive network

unit and connectionin the gating network

unit and connectionin the top-down bias network

B l+1,k

U l+1, k

Il,k

l-1,j

l,k,jG

gl,kbl,k

M l,k

Il+1,x

}

layer l+1

layer l -1

layer l

I

SelectionSelectionCircuitsCircuits


Form:Form: based on implicit partial order of unit strengths, determined by based on implicit partial order of unit strengths, determined byresponse differences; weighted sums of contibutions rectified to avoidresponse differences; weighted sums of contibutions rectified to avoidoscillationsoscillations

Convergence Properties:Convergence Properties: provably convergent; provably finds units with provably convergent; provably finds units withmaximum support, multiple, non-contiguous winnersmaximum support, multiple, non-contiguous winners

Optimality:Optimality: Valiant (1975) showed optimal parallel max finding algorithm; Valiant (1975) showed optimal parallel max finding algorithm;our WTA has comparable valuesour WTA has comparable values

WTA propertiesWTA propertiesWTA properties

some of you may wish to take a short nap now......

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WTA RuleWTA Rule

is a gating unit, in layer l, linking withis a gating unit, in layer l, linking with

the real-valued response of gating unit , at time t, 0 ≤the real-valued response of gating unit , at time t, 0 ≤

the set of gating units for unit linking with the units inthe set of gating units for unit linking with the units in

is the gating control unit for the WTA over the inputs to , has valueis the gating control unit for the WTA over the inputs to , has value

where the sum is computed after where the sum is computed after the networks involved have convergedthe networks involved have converged

the set of gatings units in layer l+1 making efferent connections tothe set of gatings units in layer l+1 making efferent connections to

the contribution to the WTA competition from unit i to unit j such thatthe contribution to the WTA competition from unit i to unit j such that

the threshold within the WTA , at least one >the threshold within the WTA , at least one >

Z the maximum possible value of the weighted gated units in the particular WTA networkthe maximum possible value of the weighted gated units in the particular WTA networkR a rectifying functiona rectifying functionk maximummaximum allowable number of iterations for convergence allowable number of iterations for convergence

Gl,k,jt = R[Gl,k,j

t-1 - gl,k ∆i,j/ql,k∑i∈M l,k; i≠j

]

θ = Z2

k + 1

Il,xθ θ

Il,k Il-1,jGl,k,j

Gl,k,jt

Gl,k,j Gl,k,jt

Ml,k Il,k Il,k Al-1, k

gl,k Il,k

∆i,j

else ∆i,j

= 0then ∆i,j = Gl,k,it-1

- Gl,k,jt-1 if

0 <

θ <

Gl,k,i

t-1 - Gl,k,jt-1

gl,kUl+1,k

gl,k = 1 if Σ aa∈Ul+1,k

> 0 , else gl,k = 0


g1,-

g2,-

g3,-

g4,-

g5,-

time

one attentional fixation

time to computepyramid

time forWTA

time topropagatewinners

offon

winning gating units shut offfor one fixation period at thispoint

output layer

input layer

time forWTA

time forWTA

time forWTA

time forWTA

Circuit TimingCircuit Timing

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a positive real value representing response of interpretive unit x at level l, a positive real value representing response of interpretive unit x at level l, , , in the hierarchy in the hierarchy

thethe set of interpretive units in layer l-1 making afferent connections with set of interpretive units in layer l-1 making afferent connections with

is bias unit for with real-value ≥ 0 defined by is bias unit for with real-value ≥ 0 defined by

is set of bias units in layer l+1 making efferent connections to is set of bias units in layer l+1 making efferent connections to

thethe real-valued weight applied to in the computation of real-valued weight applied to in the computation of

aa scale normalization factor, where and is the scale of the scale normalization factor, where and is the scale of the operation operation performed by performed by

time at which a particular WTA network has converged time at which a particular WTA network has converged

timetime at which a particular WTA competition begins at which a particular WTA competition begins

Gl,k,jt0 = Il-1,j = bl-1,jnl-1,j ql-1,j,y∑

y∈Al-2,j

Gl-1,j,yt0

Il,x

Il,x

Al-1, k

Il,kbl,k

Il,k

bl,kbl,k = amin

a∈Bl+1,k

Bl+1,k bl,k

ql,j,i Il-1,i Il,j

nl,x nl,x = sl,xsl,x

Il,x

tc

t0


Thm.1:Thm.1: The WTA updating rule is guaranteed to converge The WTA updating rule is guaranteed to convergefor all non-negative inputs.for all non-negative inputs.

Thm. 2:Thm. 2: The WTA algorithm is guaranteed to find a path The WTA algorithm is guaranteed to find a path throughthrough a pyramid of L layers such that the path includes a pyramid of L layers such that the path includes thethe largest-valued interpretive node in the output layer (m largest-valued interpretive node in the output layer (mLL) ) andand interpretive nodes m interpretive nodes mkk, 1≤ k < L, such that m, 1≤ k < L, such that mkk, is the , is the largestlargest-valued node within the support set of m-valued node within the support set of mk+1k+1 and where and where mm11 must be within the central region of the input layer. must be within the central region of the input layer.

TheoremsTheorems

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©J.K.Tsotsos ©J.K.Tsotsos 1111Convergence PropertiesConvergence PropertiesProvable Convergence Provable Convergence to units with maximal feedforward support within pyramid central zone to units with maximal feedforward support within pyramid central zone

Constant Time ConvergenceConstant Time Convergence of all WTA processes no matter their size can be guaranteed of all WTA processes no matter their size can be guaranteedthroughout the hierarchy. This is possible only because the iterative update is based on differencesthroughout the hierarchy. This is possible only because the iterative update is based on differencesof units and thus only the largest and second largest values need be considered; a 2-unit networkof units and thus only the largest and second largest values need be considered; a 2-unit networkis easy to characterize.is easy to characterize.

Specifics:Specifics: The time to convergence depends on only 3 values The time to convergence depends on only 3 values the magnitude of the largest unit the magnitude of the largest unit the magnitude of the second largest unit the magnitude of the second largest unit the parameter the parameter θθ

If initially sIf initially s11 > s > s22, then the unit representing the value s, then the unit representing the value s22 will be forced to a value less than will be forced to a value less than θθwithinwithin

loglog22 (s (s11 - - θθ) / (s) / (s11 - s - s22) iterations) iterations

The more similar the responses to stimuli, the slower the convergence (not unlike what Duncan &The more similar the responses to stimuli, the slower the convergence (not unlike what Duncan &Humphreys 1989, Nothdurft 1993 report)Humphreys 1989, Nothdurft 1993 report)


Time Bounds on ConvergenceTime Bounds on Convergence

If the largest value any unit can have is Z, then the upper bound on the number of iterations toIf the largest value any unit can have is Z, then the upper bound on the number of iterations toconvergence is given byconvergence is given by

loglog22 (Z - (Z - θθ)/)/θθ

If convergence is required within k iterations, then settingIf convergence is required within k iterations, then setting

θ θ = Z / (2= Z / (2kk + 1) , + 1) , ss11 > > θ θ, , will suffice.will suffice.

If sIf s11 ≤ ≤ θ θ, add a “gain” parameter A ≥ 1 to the computation as a multiplier to the contribution term of the, add a “gain” parameter A ≥ 1 to the computation as a multiplier to the contribution term of theupdating rule, resulting inupdating rule, resulting in

θ θ = Z / ((1+A)= Z / ((1+A)kk + 1) + 1)

time to wake up!time to wake up!

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Well-known in computer vision and neural networksWell-known in computer vision and neural networksReview of solutions in (van der Wal & Burt 1992):Review of solutions in (van der Wal & Burt 1992):

extend image repeating annulus valuesextend image repeating annulus valueswrap-aroundwrap-aroundextend blank imageextend blank imageignore annulusignore annulus

None seem biologically plausibleNone seem biologically plausible

The Boundary ProblemThe Boundary ProblemThe Boundary Problem


eimpulse reponscurve

centralattentionalarea

gannulus needincompensation

compensateusing this difference

winning item inannulus

Saccades for Solving the Boundary ProblemSaccades for Solving the Boundary ProblemSaccades for Solving the Boundary Problem

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Foveating Saccades - example #1Foveating Saccades - example #1Foveating Saccades - example #1


Foveating Saccades - example #2Foveating Saccades - example #2Foveating Saccades - example #2

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Examples of the Modelin Simulation

Examples of the ModelExamples of the Modelin Simulationin Simulation


ExamplesExamplesbinocular, 5dof robot head control

1010


luminanceluminance edges edges colour opponency colour opponency optic flow optic flow abrupt onset and offset abrupt onset and offset peripheral winner peripheral winner (foveating saccades) (foveating saccades)

SaliencySaliency no single integrated saliency map;no single integrated saliency map;

rather, saliency is distributedrather, saliency is distributed

Feature mapsFeature mapsFeature maps


Search for luminanceSearch for luminance

1111


Search for blue regionsSearch for blue regionsSearch for blue regions


Optic flow - flow patternsOptic flow - flow patterns

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Optic-Flow - Input - WinnerOptic-Flow - Input - Winner

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location, feature, target cueslocation, feature, target cuesTask DirectionTask DirectionTask Direction


what task information can actually be used?what task information can actually be used?Task Direction - “Waldo”Task Direction - “Waldo”Task Direction - “Waldo”

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• changes in images may occur at any scale in space and/or time• changes in images may occur at any scale in space and/or time (not necessarily motion) (not necessarily motion)

Simple algorithm:Simple algorithm:convolve image with ‘on’ and ‘off’ DOG at several spatial scalesconvolve image with ‘on’ and ‘off’ DOG at several spatial scalescompute temporal differences over several scalescompute temporal differences over several scalesif there is sufficient change, signal an event (on or off)if there is sufficient change, signal an event (on or off)normalize responses for scalenormalize responses for scalechoose strongest via WTAchoose strongest via WTAprovide location to eye/head systemprovide location to eye/head system

Detection of Image ChangesDetection of Image ChangesDetection of Image Changes


Image Change ExamplesImage Change Examples

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Image Change ExamplesImage Change Examples


if peripheral

centralWTA

onsetWTA

saccadeWTA

compareWTA

executesaccade

retinalimage

task

attendedregion

if central

peripheralWTA

Control Flow for Current Implementation

1616


Feature Levels and RecognitionFeature Levels and RecognitionFeature Levels and Recognition

hierarchical object representationhierarchical object representation

object-specific weightsobject-specific weights

gating unit cooperation to assist in bindinggating unit cooperation to assist in binding


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Model Predictions:Relevant Neurobiology and

Psychophysics

Model Predictions:Model Predictions:Relevant Neurobiology andRelevant Neurobiology and

PsychophysicsPsychophysics

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Predictions from 1990 BBS paper:Predictions from 1990 BBS paper:Predictions from 1990 BBS paper:

•• competition among units results in attentive choices competition among units results in attentive choices

•• competition is based on inhibition competition is based on inhibition

•• attention in all visual areas, down to earliest attention in all visual areas, down to earliest

•• competition can be biased by task competition can be biased by task

•• inhibition of unselected connections within beam inhibition of unselected connections within beam

•• inhibitory surround impairs perception around attended item inhibitory surround impairs perception around attended item

•• pre-attentive and attentive processes occur in the same neural substrate pre-attentive and attentive processes occur in the same neural substrate

•• latency of attentional modulations decreases from lower to higher latency of attentional modulations decreases from lower to higher visualvisual areas areas


•• gating networks gating networks

•• distractor effects depend on distractor-target separation distractor effects depend on distractor-target separation

•• attentional guidance and control are integrated into the visual attentional guidance and control are integrated into the visual processingprocessing hierarchy hierarchy

•• detail on structure around attended items in each processing detail on structure around attended items in each processing layerlayer (spatial and temporal) (spatial and temporal)

•• unattended stimuli reach output layer but their representation is unattended stimuli reach output layer but their representation is notnot reliable reliable

•• foveating saccade mechanism foveating saccade mechanism

More Recent PredictionsMore Recent PredictionsMore Recent Predictions

2020


Is there a Neurobiological Correlate to theWTA circuit?Is there a Neurobiological Correlate to theIs there a Neurobiological Correlate to theWTA circuit?WTA circuit?

pattern of feedforward pattern of feedforward convergence convergence feedbackfeedbackdivergence divergence

pattern of feedforward pattern of feedforward divergencedivergencefeedback feedback convergenceconvergence

pyramidal and spiny stellate neurons pyramidal and spiny stellate neurons in V1 form periodic clusters, in V1 form periodic clusters, connecting columns of like selectivity, connecting columns of like selectivity, with radius of several millimeterswith radius of several millimeters (Callaway 1998) (Callaway 1998)

‘central’ processing ‘distributed’ processing


The act of attending to a stimulus eliminates its interference from nearbystimuli, alters perceptual processes for nearby stimuli, and leaves distantstimuli in a state of mutual interference

Non-attended inputsNon-attended inputsDO reach output layerDO reach output layer

of pyramidof pyramid

Out of the Spotlight...Out of the Spotlight... but not out of Mind... but not out of Mind...

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©J.K.Tsotsos ©J.K.Tsotsos 4141 (Hallett 1978; Robinson & Lee 1981; Whittaker & Cummings 1990)(Hallett 1978; Robinson & Lee 1981; Whittaker & Cummings 1990)

- Foveating saccades are faster (about 4ms/degree)- Foveating saccades are faster (about 4ms/degree) than non-foveating (about 7ms/degree)or than non-foveating (about 7ms/degree)or saccades to remembered portions of a scene (about 6ms/degree) saccades to remembered portions of a scene (about 6ms/degree)

- saccades are elicited in response to a visual stimulus in the periphery - saccades are elicited in response to a visual stimulus in the periphery (difference most apparent if 10 degrees or more) (difference most apparent if 10 degrees or more)

- even if location is unpredictable, saccade results in approximate - even if location is unpredictable, saccade results in approximate foveation of the stimulus foveation of the stimulus

- all authors hypothesize a separate mechanism- all authors hypothesize a separate mechanism

Foveating SaccadesFoveating SaccadesFoveating Saccades


VP

PIP V3A

MDP MIP MT V4t

VIP LIP

7a

MSTd MSTl

FEF

M V2 P-B P - I

V3

PO

DP

M V1 P-B P - I

PO: Visual area in parieto-occipital sulcus of macaque PO: Visual area in parieto-occipital sulcus of macaque • receives retinotopically organized inputs from V1, V2, V3, V4, MT • receives retinotopically organized inputs from V1, V2, V3, V4, MT • in each of these, the projection arises from the periphery of the visual field • in each of these, the projection arises from the periphery of the visual field (outside central 10 degrees) (outside central 10 degrees) • additional projections from MST, FEF and other parts of parietal cortex • additional projections from MST, FEF and other parts of parietal cortex • receptive fields are large; order of magnitude larger than V2 • receptive fields are large; order of magnitude larger than V2 • similar areas in several species: cat, mink, squirrel monkey, owl monkey • similar areas in several species: cat, mink, squirrel monkey, owl monkey

(from Felleman & Van Essen 1991)(from Felleman & Van Essen 1991)

Area PO: Summary (Colby, Gattass, Olson, Gross 1988)Area PO: Summary (Colby, Gattass, Olson, Gross 1988)Area PO: Summary (Colby, Gattass, Olson, Gross 1988)

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There are many conflicting views on this. Model predicts the latencyThere are many conflicting views on this. Model predicts the latencydecreases from lower to higher visual areas. Reason is that the hierarchicaldecreases from lower to higher visual areas. Reason is that the hierarchicalWTA process operates in a top-down manner.WTA process operates in a top-down manner.

Experimental observations include:Experimental observations include:Luck et al. (1997) : neurons in area V4 show attentional modulation about 75ms after stimulusLuck et al. (1997) : neurons in area V4 show attentional modulation about 75ms after stimulusonset, while under the same conditions area V2 neurons show modulation after 100ms.onset, while under the same conditions area V2 neurons show modulation after 100ms.

Roelfsema et al. (1998) : attentional latency in area V1 of 200ms for a curve-tracing andRoelfsema et al. (1998) : attentional latency in area V1 of 200ms for a curve-tracing andsaccade task.saccade task.

Chelazzi et al. (1993) : attentional modulation in area IT with a latency of about 200msChelazzi et al. (1993) : attentional modulation in area IT with a latency of about 200ms

It is tricky to be definitive since there are so many pathways and differentIt is tricky to be definitive since there are so many pathways and differenttypes of stimuli may require different processing pathways.types of stimuli may require different processing pathways.

Time Course of Attentional ModulationTime Course of Attentional Modulation


How many differentHow many differentpathways are therepathways are therebetween these 4 areas?between these 4 areas?

Do all the pathwaysDo all the pathwaysconduct at the sameconduct at the samespeed?speed?

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A

‘record’ from this unit‘record’ from this unit

extent of receptive field (RF) extent of receptive field (RF) of unit under studyof unit under study

effective stimuluseffective stimulus is green; is green; ineffective is red ineffective is red

unit response is binary - unit takes colourunit response is binary - unit takes colourof stimulus it is affected by or is blank ifof stimulus it is affected by or is blank ifit has no inputit has no input

Moran & Desimone 1985Moran & Desimone 1985


B C

D E

B: both effective and ineffective stimuli within RF; attend to effectiveB: both effective and ineffective stimuli within RF; attend to effectiveC: effective stimulus is inside RF while ineffective stimuli is outside RF; C: effective stimulus is inside RF while ineffective stimuli is outside RF;

attend to effectiveattend to effectiveD: effective stimulus is inside RF while ineffective stimuli is outside RF; D: effective stimulus is inside RF while ineffective stimuli is outside RF;

attend to ineffectiveattend to ineffectiveE: both effective and ineffective stimuli within RF; attend to ineffectiveE: both effective and ineffective stimuli within RF; attend to ineffective

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(Motter 1993) (Motter 1993)

What is the Spatial and TemporalWhat is the Spatial and TemporalStructure of Attentional InfluenceStructure of Attentional Influencewithin the Pyramid?within the Pyramid?


Two input stimuli; instructions to attend to the red location.Two input stimuli; instructions to attend to the red location.Follow changes in connections and units as attention is Follow changes in connections and units as attention is applied from output to input layer.applied from output to input layer.

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First layer of top-down First layer of top-down selectionselection

Second layer of top-down Second layer of top-down selectionselection

Third layer of top-down Third layer of top-down selectionselection


-+

+-This is aThis is a

summary of thesummary of thesign of changes tosign of changes tounits in theunits in thepyramidpyramidas attention isas attention isappliedapplied

Response Changes due to AttentionResponse Changes due to Attention

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without distractors, stays constant;with distractors, constant or increasedue to attention

decreases due to attentiveselection with or withoutdistractors

increases due to attentiveselection only with distractors;decreases without distractors

The same summaryas in the previous slide,with spatial structureexplicit

Spatial Structure of AttentionalInfluence within the pyramid

decreases only with distractors

©J.K.Tsotsos ©J.K.Tsotsos 5252inputlayer

intermediatelayer before attentiveselection

intermediate layer afterattentive selection reaches this layer

intermediate layer afterattentive selection fullydeployed

;

Layer predictions

2727


What Justifies the ‘Surround’ PredictionWhat Justifies the ‘Surround’ PredictionWhat Justifies the ‘Surround’ Prediction

Convergence of neural connections causes signal interference; to whatConvergence of neural connections causes signal interference; to whatsignal is neuron responding?signal is neuron responding?

neuron ‘sees’ thisreceptive field

subject ‘attends’single item


Are items near attended object inhibited?Are items near attended object inhibited?

Need to set up pass zone of attentional beam on aNeed to set up pass zone of attentional beam on areference object then compare reference with a reference object then compare reference with a

probe objectprobe object

Spatial predictionsSpatial predictions arising from: arising from:

Cutzu & Tsotsos (in progress)Cutzu & Tsotsos (in progress)

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Experiment 1. The basic trial sequenceExperiment 1. The basic trial sequence

The cue, a light grayThe cue, a light graydisk indicated the position ofdisk indicated the position ofthe reference target characterthe reference target characterin in the followingthe following, test, screen., test, screen.The cue was shown for 180The cue was shown for 180msec.msec.

The mask was shown until theThe mask was shown until thesubject responded.subject responded.

Test screen, shown for 100Test screen, shown for 100msec. The target charactersmsec. The target characterswere red (drawn in this figurewere red (drawn in this figurewith thick lines), thewith thick lines), thedistractors were black (drawndistractors were black (drawnwith thin lines).with thin lines).The task was to decideThe task was to decidewhether the two targets arewhether the two targets areidentical or different byidentical or different bypressing certain keys on thepressing certain keys on thekeyboard.keyboard.

©J.K.Tsotsos ©J.K.Tsotsos 5656Experiment 1.Experiment 1. Dependence of target discrimination accuracy on Dependence of target discrimination accuracy ontarget separation. Target separation is expressed in fractions of letter ring diameter. One of the twotarget separation. Target separation is expressed in fractions of letter ring diameter. One of the twotarget locations was pre-cued; the other target appeared at a surprise location on the the ring oftarget locations was pre-cued; the other target appeared at a surprise location on the the ring ofletters. Performance improved substantially with increasing inter-target distance.letters. Performance improved substantially with increasing inter-target distance.

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©J.K.Tsotsos ©J.K.Tsotsos 5757Experiment 2. Dependence of accuracy on inter-target separationExperiment 2. Dependence of accuracy on inter-target separationin the control condition when the ring center (the fixation location) was pre-cued. Inter-targetin the control condition when the ring center (the fixation location) was pre-cued. Inter-targetdistance is expressed as fractions of diameter of the letter ring. Performance did not changedistance is expressed as fractions of diameter of the letter ring. Performance did not changesystematically with inter-target distance.systematically with inter-target distance.

©J.K.Tsotsos ©J.K.Tsotsos 5858Experiment 3.Experiment 3. The test image. The distractor characters were The test image. The distractor characters wereblack Ls and Ts, equal in number, homogeneously distributed across the screen. The red, targetblack Ls and Ts, equal in number, homogeneously distributed across the screen. The red, targetforms, were restricted to a virtual circle centered on the fixation point.forms, were restricted to a virtual circle centered on the fixation point.

3030

©J.K.Tsotsos ©J.K.Tsotsos 5959Experiment 3.Experiment 3. Dependence of accuracy on inter-target separation. Dependence of accuracy on inter-target separation.The distractors were distributed homogeneously across the test image. The two targets assumed theThe distractors were distributed homogeneously across the test image. The two targets assumed thesame positions as in Experiment 1, moving from trial to trial on a virtual circle centered on thesame positions as in Experiment 1, moving from trial to trial on a virtual circle centered on thefixation point. One of the two target locations was pre-cued. Discrimination success rate increasedfixation point. One of the two target locations was pre-cued. Discrimination success rate increasedgradually inter-target distance.gradually inter-target distance.

©J.K.Tsotsos ©J.K.Tsotsos 6060Experiment 4.Experiment 4. A typical trial sequence. Left. The cue, a light A typical trial sequence. Left. The cue, a lightgray disk, was shown for 180 msec. Middle. Test screen, shown for 100msec. The characters weregray disk, was shown for 180 msec. Middle. Test screen, shown for 100msec. The characters wereoverlayed on light gray disks identical to the cue. In this case the target is present: there is an odd Loverlayed on light gray disks identical to the cue. In this case the target is present: there is an odd Lamong the Ts in the test image. However, the cue is invalid. The subjects' task was to detect the oddamong the Ts in the test image. However, the cue is invalid. The subjects' task was to detect the oddletter in the ring. Right. The mask was always removed after 2 seconds, whether the subject respondedletter in the ring. Right. The mask was always removed after 2 seconds, whether the subject respondedor not, and a new trial sequence was initiated.or not, and a new trial sequence was initiated.

3131

©J.K.Tsotsos ©J.K.Tsotsos 6161Experiment 4.Experiment 4. Dependence of accuracy on target-cue distance. Dependence of accuracy on target-cue distance.The horizontal dotted line indicates the false alarm rate level. The continuous line represents theThe horizontal dotted line indicates the false alarm rate level. The continuous line represents thetarget detection rate in the target present trials. The cue-target distance was defined as the length oftarget detection rate in the target present trials. The cue-target distance was defined as the length ofthe chord joining the cued location on the ring to the target location on the ring. It is expressed inthe chord joining the cued location on the ring to the target location on the ring. It is expressed inunits of diameter, and ranges from 0 (the odd letter is at the cued location) to one diameter (the oddunits of diameter, and ranges from 0 (the odd letter is at the cued location) to one diameter (the oddletter is diametrically opposite to the cued location).letter is diametrically opposite to the cued location).

©J.K.Tsotsos ©J.K.Tsotsos 6262Experiment 5.Experiment 5. Dependence of target discrimination accuracy Dependence of target discrimination accuracyon target separation. Target separation is expressed in fractions of letter ring diameter. One of theon target separation. Target separation is expressed in fractions of letter ring diameter. One of thetwo target locations was pre-cued; the other target appeared at a surprise location on the the ring oftwo target locations was pre-cued; the other target appeared at a surprise location on the the ring ofletters. Performance initially improves with inter-target distance, reaches a maximum, and thenletters. Performance initially improves with inter-target distance, reaches a maximum, and thenplateaus.plateaus.

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• Dual goals of computational utility and• Dual goals of computational utility andneurobiological explanatory powerneurobiological explanatory power

• Addressed visual attention from theoretical, modeling,• Addressed visual attention from theoretical, modeling,simulation and experimental perspectivessimulation and experimental perspectives

• Although computer simulation is in a real sense• Although computer simulation is in a real sense‘child-like’, it does seem to be the most‘child-like’, it does seem to be the mostcomprehensive of the modelscomprehensive of the models

• Several past predictions seem to be gaining evidence• Several past predictions seem to be gaining evidence

• Much more to do......• Much more to do......

ConclusionsConclusionsConclusions

the selective tuning model of visual attentiontsotsos/homepage of john k_files/st...mdp mip mt v4t...

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