CATEGORIES AND CONCEPTS
CATEGORIES AND CONCEPTS PSY 341K
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180 aflmuih i I i Extra distance i sound must ivavei li Path oisound to i i to reach ngnl ear near left ear 0quot azwmuln Path oi sound lo tar r gm ear Sound source at 45 lei aZIth Two cues for sound localization intensity differences at the two ears temporal differences at the two ears a 77 H E t g 20 C E 10 1 g o 6000 Hz 5 G C lt1 4 E E r 3 1o LN 200 Hz I B 0 x A A 1 E 0 30 60 90 120 150 180 Straight Direction of sound source Directly ahead behind Intensity differences at the two ears for a high frequency tone and a low frequency tone as a function of the direction of the sound source Interaural time difference msec 0 20 40 60 80 100 120 140 160 180 Straight Direction of sound source Directly ahead behind Difference in time of arrival at the two ears as a function of the direction of the sound source Spikes second i lihillt39r Ur L Mi Ii EL WWW Periodic firing of eighth nerve bers Post stimulus time histogram PSTH measured for repeated presentations of a lowfrequency sinusoidal waveform Cerebral annex Tlulamue nih mls Midbmin Medulla Cathie Major auditory pathways up to primary auditory cortex Loudspeaker 1 Left ear r Longer path a neuron E O Acllnn pummial 1 from right ear Cochlea 1 begins traveling loward MSG L I d cochlear uuclcu 2 Action pnleulml begins traveling toward M50 Cuclulca and Acmm polenlials converge cochlear nuclcua 5 on rm M50 neuron wlur responds mm strongly rt their arrival mmdam Jeffress delay line model ofauditory sound localization based upon temporal differences at the two ears A gt x I 1 x z x Ill x r l 7 1 l l I gt 7 Dlrect paths gt I 2 If l 39 gt I 4 I g 7 Indirect paths k Q K 7 f to ear I l Sound I source l x VJ The complex sound environment due to reflections echoes from surfaces in the environment Echoes make sound localization more difficult Major Sensory Systems Sense Source of information Seeing Light Hearing Sound Balance Gravity and acceleration Touch Temperature and pressure Pose Joint position and muscle stress Smell amp Taste Chemical structure Visual system Auditory system Vestibular system Tactile system Kinesthetic proprioceptive system Olfactory system Gustatory system These are some of the sensory systems in humans Seeing and hearing encode information over relatively large distances and hence are particularly important for survival They will be the focus of this course Important Perceptual Tasks Identification of objects and materials Navigation through the environment Prediction of motion trajectories Estimation of physical dimensions Object manipulation Speech communication Visual communication Some of the important and complex tasks performed by our perceptual systems Visual pathway of the macaque monkey whose visual system is very similar to the human visual system Note that the visual areas take up a very large percentage of the monkey s cortex In humans it is a smaller percentage but still very substantial The optic nerves contain 23 of all the sensory neurons in humans Major neural connections between the Visual areas in the previous map of the macaque monkey s Visual pathway This diagram hints at the complexity of the perceptual systems Kw 4 Y 1 I 423291jsz e u 4 Twisted cord illusion The twisted cord is not a spiral but a Even see series of concen c c 0 es mingly simple tasks such as judging curvature or slant involve co eX rocess1n Another twisted cord illusion The letters are not tilted 55968 9 8 oobo 022 we 5502500 320 0 302 Eoobo o anwn 132 we sounbmsoEoQ QR x V 3n N X36 a a a w V H AAA 4 xk rzga 1 1 IILX 1 TI 7 i wWJ x P xi Demonstration of active perceptual grouping mechanisms by JL Marroquin One sees a uctuating collection of circles of various diameters Vision involves active complex subconscious processes Famous painting by Bev Dolittle The Forest Has Eyes demonstrating how good humans are a detecting meaningful structure in images Complex natural scenes that have never been seen before are quickly and accurately interpreted by the visual system Two Difficult Problems for the Visual System Context problem Objects often appear in a complex and varying context of other objects making recognition of objects difficult Depth problem The images in the eyes are twodimensional projections of the threedimensional world thus the third dimension depth is lost and must be constructed These problems are responsible for much of the complexity in the design of the human Visual system Demonstration of the context problem Appropriate grouping and segregation of features must occur before recognition of familiar objects is possible This pattern contains a familiar image The bottom figure the familiar image is revealed This pattern appears to be a collection of random geometric shapes In an appropriate context the shape fragments coalesce into recognizable objects Natural example of importance of solving the context problem in order to recognize familiar objects A A v V v A m V V v Q A w v V A A i V v W A m v U U Shape from shading patterns Viewed this Way one sees three columns of bumps and two columns of dips Rotate the image and one sees tWo columns of bumps and three columns of dips Agga vvwvv an a Agai The table tops are parallelograms of exactly the same dimensions Perspective shading shadows and occlusion all contribute to perception of the 3D world Understanding Perceptual Systems Perceptual tasks and goals Environment and stimulus information Neural information and information processing Performance Evolution Recurrent Themes Perception is a very complex process Perception generally involves the integration of many sources of information most of which are not very reliable There are many approaches to the study of perceptual systems and each has made important contributions to our understanding Approaches to Perceptual Systems Behavioral psychological PhysiologicalAnatomical biological ComputationalMathematical theoretical EcologicalEvolutionary Aunc e Siapes Vestwbu ar nerve Coch ear nerve Tympanum Round window Middle eav Eustachuan lube cavuy General schematic of the ear Cmbul Amhkvr Thalmnm immim mlinulm Midbrzin Dunnli Mmlulla 39 39x 9m Audimry Hunt bupormr uhmn nudul Cochlen Major auditory pathways up to primary auditory cortex Outer ear Eardrum Ossicles Inner ear Pinna Auditory canal Pinna and Directionat eardrum microphone x Middle Impedance matching ear and overload protection Inner Frequency analysis ear hair cells Major functions of the structures of the ear Anterior vemca Semicilcular Posterior Canals venica Vesubular nerve I e be Mod 0 us Cochlear news Horizonta Scale mama Spwral ganghun HeHmmzma Scala vestibuli Sca a mama Hawceus Reussner s membran organmcnm Scam ympam Spiral ganglion Structure of the cochlea 5mm genqhnn my hair 2 ausuar I msmhmne Snala mum Twcxanal membrane Bony wan 0V occmea sznnal membrane Omar m can Cross section of the cochlea In humans there are approximately 15500 hair cells in each ear 12000 outer and 3500 inner 4 25 Hz 50 Hz ID H1 2 g i E f E K 3 9 3 200 Hz 5 14AL trt 7a 21 27 73 u 25 35 7v 2 n u a a 3mm quotWm I m Hz a EDD H1 j ISDU Hz a 0 m 20 Drslance lrom saves mm Envelope of the Bekesy traveling wave The peak shifts toward the stapes as frequency increases mm poims ox mdurial and basila membranes are offset lt High Low frequency trequency A B C D Cocmea Basriar membrane Slimuius magmnme IdBSF Li Tuning curves measured by nding the pure tone amplitude that produces a criterion response in an 8th nerve fiber Tuning curves forfour different neurons are shown 4 25 Hz 50 Hz ID H1 2 g i E f E K 3 9 3 200 Hz 5 14AL trt 7a 21 27 73 u 25 35 7v 2 n u a a 3mm quotWm I m Hz a EDD H1 j ISDU Hz a 0 m 20 Drslance lrom saves mm Envelope of the Bekesy traveling wave The peak shifts toward the stapes as frequency increases E39 I m 50 m u 40 AP threshold a Is kHz 3734 dB SPL 30 053433 dB SPL I post morrem l I I lll4ll l l 2 lo 20 Frequency kHz Tuning curves at low noise levels high noise levels and postmortem The increased sensitivity at lower sound levels in the living organism is due to the cochlear amplifier quot Ouler hair cells passive Outer hail cells ac ve Contraction and expansion of outer hair cells helps to amplify the responses of the basilar membrane in a narrow region This may be a large component of the cochlear amplifierquot ll snmums clxck quotV l I 36 kHz J 26 kHz J 20 kHz I 1 15 kHz W 1 o 20 Time ms Click evoked emissions Sound level as I I l I o 1000 30m 4000 2am Frequency Hz Spontaneous otoacoustic emissions measured from two different ears Eye movements Eyal Seidemann eyalmai1cpsutexasedu Depts of Psychology and Neurobiology and Center for Perceptual systems University of Texas Austin Why study eye movements Where we look determines what we see Tightly linked to perception and cognition Relatively simple and well characterized Amenable to precise quantitative study One of the best understood systems in terms of anatomy physiology and computations Sensorymotor integration Con icting demands for Vision High resolution Maximal coverage Speed Limited resources Elegant solution Variable resolution Mobile platform Number of rods or cones pev mm2 180000 160000 140000 120000 100000 80000 m p o o a 1 P o o a 20000 Variable resolution Photoreceptor density Blind spot Col ecwr cens Reuna and ganglmn news Photovecemms Variable resolution Cortical magni cation factor Foveated imaging Geisler and Perly Foveated imaging Geisler and Peny Mobile platform Extraocular muscles superior oblique superior rectus lateral recius medial rectus superior oblique superior rectus lateral rectus inferior rectus inferior oblique Eye Movements Saccadic Movements Scanning movements where the gaze is abruptly shifted from one point to the next conjugate ballistic no visual feedback Vergence Movements Cooperative movements that keep both eyes fixed on the target converge or diverge Pursuit movements Smooth tracking movements that keep an object s image xed in place on the retina Vestibulo Ocular Re ex W OR Stabilize image during head and body movements Micro movements Tremor drift microsaccades Properties of saccades Target7 quotquotquotquot Eye position 800 s Eye velocity 1 200 ms HHw J 0 1 1 IN 1le Amphludu Iigun 4A 11w mm m 1 39mL 1 nmph HI 4 mum m hull dunmnn Hinlull V lhc an mum m hummxncuu1ml m 1101 u in Amplmmr Properties of saccades Average 34 saccadessec gt100000day Average 5 deg Max 40 deg from center Stereotyped xM Peak velocity Up to 900 degsec Variable reaction time 7 Saccade and Pursuit How can we measure eye movements Optical methods Video based Analog Purkinj e Magnetic search ooil Purkinje eye tracker re ections of infrared light cornea rst Purkinje image back of the lens fourth Purkinje image Eye tracking devices Eye Tracking devices How are saccades generated Relation between motoneuron discharge rate R and eye movements A e i R Kl E K2 dEdt E is eye position in the pulling direction of the associated muscle dEdt is eye velocity in the same direction Recording Abducens motor neuron N Vl Saccade generation Time gt R K1 E K2 dEdt Eye position R motoneuron discharge rate Z Eyevelocity E eye position in the pulling direction 1 eye VCIOCity in the Spikess Pulse same direction Fm i HHHiH39l H39H H39l Spike 4 Multiple saccades A J Lateral Abducens motor neuro N V 93 Latera Heft Medwa right F B m5 Abducens motor neuron a a E m m 3 E w E Q gt o E E c D Midbrain and Brainstem Pusxenor commlssure Thalamus Superwor ecucums RosuaJ Mesencephauc MLF n retwcu ar formauan nleriarcamcwus Iascmums CereneHum Pmmemau ponune rencu ar formauon Saccade 9 wed Madam a mms teclu5 9 t 1 WatDer Ocu ovnuwr quotwe nerve Ocmumowr nucleus 1 mom mm imam mngmmal Vasmcums B Lung man Numeus a me narsal raphe bursirmumn Ommpause Pavameduan momma 1 or w neuron rsucuiir iovmamquot Eye Emquot nosmon mm l l r 0 ms m M W MOW neuron hh mmV neuron nuc cus WHHH Burst neuron Ommpause W neuron Saocade control Supplementary Posterior eye fields parietal cortex Frontal eye fields Caudate nucleus Superior colliculus Substantia nigra pars reticulata Mesencephalic and pontine reticular formations Basal Ganglia Aokll HI WWWWH HWHWWWW Tonic Inhrbm39on Disinhlbirron Tonic nl1bitron W4 Saccadic Eye Movement Superior Coliiculus Saccade Generating Circuit Frontal I I Parietal Dorsolateral Cortex Cortex PreFrontal Vlsual Cortex Basal Ganglia Superior Colllculus Retma Eye RthCUIar Movement Formation MumZ 2000 Control of Saccade and Pursuit Tha amus Furebwa39m Eye thbram Pans CerebeHum Dors39v merposuns demate MeduHa r Krauzlis 99 Superior Colliculus SC Rostral Caudal Sparks 2000 Munoz 2000 Burst Generator Ircmt Population coding in the SC Lee and Sparks 89 Saocade control Supplementary Posterior eye fields parietal cortex Frontal eye fields Caudate nucleus Superior colliculus Substantia nigra pars reticulata Mesencephalic and pontine reticular formations Frontal Eye Field FEF longitudinal ssure O 25 a o principal a suicus central sulcus dorsomedial posterior anterior lateral veniroiate ral fissure M17 0 ow1hreshuld Frontal Eye Fieids xx size of elicited saccade Bruce 85 Frontal Eye Field FEF Bruce 85 O 0 Visual Stimulus Learned Saccade No Saccade No Target AMJM misumlm ac Visual Target Cell 13415 Visual Activity Cell 13600 FEF responses Cell 13530 Visual 8 Movement Bruce 85 Cell 13499 Movemenl amp Inhibitory Visual i Approaches to Perception Behavioral psychological PhysiologicalAnatomical biological ComputationalMathematical theoretical EcologicalEvolutionary 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 Sine wave gratings with different contrasts The contrast is the luminance amplitude of the sine wave divided by the mean luminance 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 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 5mm rmmx m o mum RESPONSE mu mmm z E 39 30 20 u 39u as v 5 75 o e c s 05 E m U yo m E 7 L g 0 o I z I 5 as V 05 j P x z z I 025 E E I n S 05 39 o w z 3 4 5 rmasnmu DIFFERENCE EEYWEEN A AND a Illustration of the twointerval tvvo altelnative forced choice task and the concept of the psychometn39c function Proportion Correct 0 O O I 00 O O m 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 Cumulative normal function Fxa q1 qlJ2 e a Logistic function 1 Fx0t q1q 1 6 Weibull function Fx0 ql q 1 e x0 Three different equations that have been used to describe psychometric functions Each has three parameters which are typically estimated with maximum likelihood methods 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 0 CD I 0 O I Proportion A quotLongerquot o 4 I 0 N 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 An objective identi cation task with no feedback The illusion in this example is called the MullerLyer illusion 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 PhysiologicalAnatomical Approaches Microscopy and Imaging Single neuron recording Functional imaging Event related potentials ERPs Lesion Monet Sensmy Luca Component neuron nemon neuron Interneuron mlemeumn Projechon Nemoendocrme ceH mpu ntegranve Conducme Ompln Secreuon There many different types of neurons but they share important features An electrode next to a pyramidal cell a projection neuron in the Visual cortex Micro electrode 0pm mm o a M visual mm electrode mel gcniculalc body Opnc radxanons Simple cell39s receptive field Lateral geniculate nucleus cell Simple cell B SIMPLE DeAngelis et al 1995 Main thing to note is the receptive fields of neurons in the retina lgn and primary Visual cortex cover only a small part of the Visual field m Microelecde N um i Microelectde M e in measure membrane poiemial Mambrane potential mVJ Some basics of single neuron behavior and single neuron recording 1 Sensory Signals Stimulus inpm Inlegralion Conducuon Dulpul mansmiuev releasel Action Dmsnual Action Graded Achequot veceplov pulennal polenllal Wranlial Receplar JEL f polannal Sirexch Muscle SDmdlE Sensory neuron 2 Motor signals lnpul lmeglalion Conduction Oulpm Nansmmer release Graded Action Anion Action synapnc potential poignnal pmannal palemml Race manual Mowr neuron 3 Muscle Signals lnpm lnlegvaxmn Conducllon Oupu Inehaman s eded Acuon svnepuc polennal pammial Electrical activity of a muscle re ex from the stretch receptor to muscle contraction Functional Imaging Functional Magnetic Resonance Imaging fMRl Positron Emission Tomography PET Optical lmaging OI Functional Imaging What is measured VlRl Blood oxygen level PET Radioactive marker concentration OI Re ectance or Scattering of nearinfrared light A Looking at words Listening to words C Speaking words PET images demonstrating how localization of activity can change depending on the task Demonstration of how MRI can be used to quantify brain activity in speci c brain locations in humans ComputationalMathematical Approaches Ideal observer analysis Descriptive models Information processing models Physiological models Ideal Bayesian Observer Perception as Rational Inference Possible stimulus categories 0102 c Prior probability 1701 Posterior probability pltcz IS Rational decision rule make response 139 ifpltcl IS gtpltcj IS for all 139 j Ideal Bayesian Observer Perception as Rational Inference Possible stimulus categories 0102 c n Prior probability 1701 Stimulus likelihood pltScl Rational decision rule pick category 139 ifpltSclpcl gtpltScpc for all 139 j Ideal Bayesian Observer State of environment 0 Stimulus S Response r make the response r that maximizes WIS Zvlrawlplslwlplwn l utility function stimulus likelihood prior probability eg growth factor Ideal Bayesian observers provide a useful conceptual and modeling framework for understanding the design of perceptual systems Which obieci description generated the image aaia obiect descriptions w image data s Bayesian solution iikeiinoodmalwx narrows seleclion prior pw lunher narrows selection consislent with projection curreni Damion In Neuromoiogy Logic of Bayesian approach Sinha amp Adelson 1993 Likelihood Utiiiiy mnchon E B n IE 6 sumulus q I I 7 0 Aspect ram Aspecl ram errov Pnor pinbabimy Posterior prababitit Expected umin E E E m m m a gt a gta I 0 I Aspect ram Aspect vatic Aspecl raua Bayesian statistical decision theory as a framework for studying perception From Geisler amp Kertsen 2002 Knill and Kersten demonstration of the effects of geometrical cues on perceived lightness Illusions may represent rational inferences by the perceptual systems Subj ectiveillusory contours of the sort designed by Kanizsa R C James picture that demonstrates the importance of learning in perception Knowledge of prior probabilities can be learned as well as inherited EcologicalEvolutionary Approaches Natural scene statistics Natural tasks Fitness and natural selection Genetics Near Miss to Weber s Law AIKI Weber s Law AIK1IU Fechner s generalization of Weber s Law AIK1 10 Near miss to Weber s Law Intensity discrimination in the auditory system is described approximately by Weber s law The generalized Weber s law holds for noise bursts The near miss to Weber s law holds for pure tones Strength of neural response 10 20 so 40 so 60 70 so so 100 Sound intensity dBSPL lt Saturation Low Strength of neural response 10 20 30 40 50 60 7O 80 90100 Sound intensity stpL Responses of 8th nerve fibers as a function of sound intensity The different kinds of eighth nerve neurons may help explain how Weber s law can hold over such a large range Threshold 120 of feeling dB SPL Audibility curve threshold of hearing I l l I 20 100 500 1000 5000 10000 Frequency Hz Equal loudness contours and audibility curve for humans These show which sound levels have the same loudness but they do not provide a measure of the absolute loudness