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# HUMAN NEUROPSYCHOLOGY PSY 380C

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This 26 page Class Notes was uploaded by Marco Wolf on Monday September 7, 2015. The Class Notes belongs to PSY 380C at University of Texas at Austin taught by Staff in Fall. Since its upload, it has received 36 views. For similar materials see /class/181805/psy-380c-university-of-texas-at-austin in Psychlogy at University of Texas at Austin.

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Date Created: 09/07/15

Approaches to Understanding Vision Natural tasks Natural scene statistics Anatomy Responses of individual neurons Responses of neural populations Perceptualbehavioral performance Mathematical and computational modeling 26 Behavioral Tasks A B C description objective feedback identification subjective no feedback estimation Different perceptual tasks can be classi ed by picking one attribute from each column 27 2AFC Task interval 1 interval 2 warning response feedback warning Example of an objective identi cation task With feedback If there are just two alternatives then such tasks are often called a discrimination tasks if one of the alternative is uniform in some fashion then such tasks are often called a detection tasks The observer must decide Whether a target pattern is in the first temporal interval or the second temporal interval 28 STIMULI FEEDBACK TONE Igt m D m gt WARNING RESPONSE TONE INTERVAL 5 L0 30 E 2 20 39A E 09 k 3 E 15 Q 9 2 8 08 m m IO N n n 1 9 o 7 I o 0 O I z z I Q I 05 J 06 1 31 K A 025 g 2 m g l l I l l a o l i 3 4 5 THRESHOLD DIFFERENCE BETWEEN A AND B Illustration of the twointerval twoalternative forced choice task and the concept of the psychometric function Set of all indistinguishable stimulus pairs Set of all stimulus pairs Transition zone Threshold The logic of measuring discrimination thresholds The measurements are focused on the transition zone between the indistinguishable and the trivially easy to distinguish An objective identi cation task with no feedback The illusion in this example is called the MullerLyer illusion Such illusions have also been studied with descriptive methods the phenomenological approach 31 0 CO I 0 O I Proportion A quotLongerquot 0 1 I 0 J I O I I I I I 6 4 2 0 2 4 LengthA Length B mm point of subjective equality PSE Example psychometric function for an objective task with no feedback 32 An objective identi cation task with no feedback The illusion in this example is called the MullerLyer illusion 33 Comparison patch Example of an objective estimation task with no feedback The gray scale luminance is the same for the two squares in the checkerboard in fact it is exactly the same gray shown at the tails of the arrows One way to estimate the difference in apparent luminance is to adjust the luminance of a comparison patch against a same xed background to match the brightness of the squares in the two regions of the image The luminance of the lighter comparison patch has an apparent luminance more similar to the square in the shadow The difference in the physical luminance of the comparison gives a precise measure of the apparent psychological luminance difference 34 Proportion Correct I I I I I 0 01 02 03 04 05 06 Vernier Offset min of arc A real example of a psychometric function Each data point represents the proportion correct for a block of 30 trials 35 Cumulative normal function 1 2 X 1 zl 6 l Fxa q1qlJ2 e a Logistic function 1 Fxa Q1 LIW Weibull function Fxa ql ql exa Three different equations that have been used to describe psychometric functions Each has three parameters which are typically estimated with maximum likelihood methods Fitting a Psychometric or Neurometric Function to Data Stimulus levels x17x27x37 x49 Number corrects per stimulus level Number errors per stimulus level C17C27C37C4739 E17E27E37E4739 L FolMM 1 Fx1oc E Folam 1 FXzoc E2 721nLa 72 C1lnFx1oz E11nliFx1oz C2lnFx2oz E2lnliFxzoz vary a and 3 to minimize 72lnLa3 The likelihood of a particular sequence so correct and incorrect responses from an observer Although the function to be t to the data depends on the parameter q it is assumed to be known 37 Response a PCa PEa I 2 1 E 5 b PEtJ PCb Response n For Pfa fl 2 J E 5 sn Pm Ph Table of possible stimulus response outcomes in a 2AFC or a YesNo task 38 Ideal Bayesian Decision Rule Possible stirnulus categories ab Prior probability pu pb Stimulus likelihood pSa pSb Rational decision rule pick category b ifand only ifpSb pb gtpSa pa or equivalently pick category b ifand only ifpSbpSa gtpapb 39 PEG P01 5 puma pnLb d392 c ii2 Decision Axis lnL Representation of the decision process of a rational ideal observer 40 Signal Detection Theory Formulas d39lt1gt PCblt1gtquotPEa d39 quot1 h CDquot P z d39 d39 ltIgtquot PC 7 ltIgtquot P 7 c lt a 2 c a 2 PCACDiic PhCDiic 2 2 d y PEaCD ici P ziCD ici 2 2 41 Receiver Operating Characteristic ROC PY sn 0 PW 42 Approaches to Understanding Vision Natural tasks Natural scene statistics Anatomy Responses of individual neurons Responses of neural populations Perceptualbehavioral performance Mathematical and computational modeling 43 ComputationallMathematical Approaches Descriptive models Information processing models Physiological models Ideal observer analysis 44 Ideal Bayesian Observer Perception as Rational Inference Possible stimulus categories 0102 cn Prior probability 1701 Posterior probability 1701 IS Rational decision rule make response 139 ifpcl lS gtpc IS for all i 2 j 45 Ideal Bayesian Observer Perception as Rational Inference Possible stimulus categories 0102 c Prior probability 170l Stimulus likelihood 17Slcl Rational decision rule pick category 139 ifpScl pcl gtpScpcJ for all i 2 j 46 Ideal Bayesian Observer State of environment 0 Stimulus S Response r make the response r that maximizes WIS Zvlrawlplslwlplwn l utility cost function stimulus likelihood prior probability Ideal Bayesian observers provide a useful conceptual and modeling framework for understanding the design of perceptual systems 47 image dala I likelihoadr pSr narrows selection oonsisienr with proioerion Which object description generated the image data b Bayesian solution object descriptions s prior NS tunner narrows seieorion Cumml Opinion In Nsnrooroiogy Logic of Bayesian approach Sinha amp Adelson 1993 48 erehhnod Utility mncnon 112 surnums u Aspect lam error i Aspect ram Expected umin Prior prubabrmy Posterlar probabnny 0 Sam Siam I Aspect rano Aspect ratio Aspect ratio Bayesian statistical decision theory as a framework for studying perception From Geisler amp Kertsen 2002 49 Knill and Kersten demonstration of the effects of geometrical cues on perceived lightness Illusions may represent mtional inferences by the perceptual systems 21 b Subj ectiveillusory contours of the sort designed by Kanizsa 51

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