11/14/20111G89.2223 PerceptionSensory Cue CombinationLaurence T. MaloneySome examples of cues to depthor shapeRetinal projection depends on size and distanceMonocular depth cuesEpstein (1965) familiar size experimentHow far away is the coin?Cast ShadowsShading and contour11/14/20112Texture1. Density2. Foreshortening3. SizeLinear perspectiveBinocular depth cuesVergence Angle As One Binocular SourceVergence Angle As One Binocular SourceVergence Angle As One Binocular Source11/14/20113Wheatstone stereoscope (c. 1838)Sir Charles WheatstoneDual mirror stereoscopeUncrossed disparityZero retinal disparityCrossed disparityDisparityzero disparityuncrossed disparitycrossed disparityFixation pointCues in conflictWhat cues?11/14/20114Ames room Ames roomAmes roomCue CombinationRock & Victor (1964)Visually and haptically specified shapes differed.What shape is perceived?View object through distorting lens while exploring object hapticallyIrv RockRock & Victor (1964)Experimental Design11/14/20115Rock & Victor (1964)Results1.90 0.98 1.8513.4 23.1 14.1 mm14.1 20.5 14.5 mmRock & Victor (1964)Results1.90 0.98 1.8513.4 23.1 14.1 mm14.1 20.5 14.5 mmVisual CaptureHow should we combine cues?VHSShaptic size estimatevisual size estimaterandom variablesModeling Cue Combination,,HV VHS Gaussian sS Gaussian strue locationsVSHSs,,HV VHS Gaussian sS Gaussian sstandard deviationsVSHSs11/14/20116location-scale familiessVS,V VS Gaussian sVVES s10 ,110 ,2HVS Gaussian cm cmS Gaussian cm cmsVSHS$10 if you are within1 cm of sWhich cue?Chances of winning?sVSHSCan we do better by combining cues?1VHSwS wSweighted linear combinationsVSHS1(1 )VHES wES wESws w s s sVSHS 1(1 )VHES wES wESws w s s Gaussian,S Gaussian s wWhat is SD?sVSHS2222 221(1 )VHVHVarS wVarS w VarSwwa parabola in w11/14/2011722 22(1 )VHVar S w w01w2V2H22 22(1 )VHVar S w w01w2V2Hcould it be?sVSHS2222222(1)!0VHHVHVar Swwww 01w2V2HsVSHS222HVHwsome examples ….22 22(1 )VHVar S w w01w2V2Hoptimal cue combinationeffective cue combinationRock & Victor (1964)Visually and haptically specified shapes differed.What shape is perceived?View object through distorting lens while exploring object hapticallyIrv RockWhy visual capture?11/14/20118Visual/Haptic SetupVisual Capture ?Why should vision be the “gold standard”all other modalities are compared to?SVH wVSV wHSHVarianceWeightswVH2V2H21VH21V21H2Visual Capture ?Why should vision be the “gold standard”all other modalities are compared to?SVHwVSVwHSHVarianceWeightswVH2V2H21VH21V21H2Visual Capture ?Why should vision be the “gold standard”all other modalities are compared to?SVH wVSV wHSHVarianceWeightswVH2V2H21VH21V21H2Visual Capture ?Why should vision be the “gold standard”all other modalities are compared to?SVHwVSVwHSHVarianceWeightswVH2V2H21VH21V21H211/14/20119Experimental Outline1) determine (& manipulate) within-modality variances• discrimination thresholds (2-IFC, constant stimuli) 2) make predictions for combined performance • using MLE model to predict weights & combined variance.3) measure combined performance & compare to prediction• similar to within-modality 2-IFC discrimination task (get PSE and thresholds) StandardComparisonno feedback!2‐IFC TaskVisual-HapticVisual-alone Haptic-aloneThree ConditionsPredictionsDetermining Within‐Modality Variance Determining Within‐Modality VarianceThresholdDetermining Within‐Modality VarianceThresholdDetermining Within‐Modality VarianceThreshold11/14/201110Determining Within‐Modality VarianceThresholdDetermining Within‐Modality VarianceThresholdFrom Variance to Thresholdvisual-haptic varianceestimators weightsPredicted weights for combined performance from within-modal dataPredicted combined threshold from within-modal datawVH2V2H2visual-haptic thresholdestimators weightswVJNDH2JNDV2 JNDH21JNDVH21JNDV21JNDH21VH21V21H2JNDi 2 iJNDi 2 iVisual‐Haptic DiscriminationVisual‐Haptic Discrimination Visual‐Haptic Discrimination11/14/201111Visual‐Haptic Discrimination Visual‐Haptic DiscriminationEmpirical Thresholds and Weights“visual capture”“haptic capture”Weights & PSEsIndividual DifferencesLASLASRSB1RSBMOEEmpirical Visual WeightMOEHTEHTEJWWJWWKMLKML0% noise133% noisePredicted Visual Weight0.80.60.40.2010.80.60.40.2Conclusions Combination reduces variance. Linear weighting scheme for visual-haptic perception. Explains behavior like “visual capture” or visual dominance.i.e, vision is given a weight of ~ 1.0 if the variance of the visual estimate is less then the variances of the other
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