Attention - 1 Attention I . I n tr o d u c tio n I I . E a...

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Unformatted text preview: 1 Attention I . I n tr o d u c tio n I I . E a r ly v s . L a te S e le c tio n III. Broadbent’s Model I V . T r e is m a n ’ s M o d e l V. Norman’s Model V I . R e s o lu tio n V I I . R e s o u r c e A llo c a tio n V I I I . C e ll P h o n e s I X . C o n c lu s io n s 2 I. Introduction A . T h e b o ttle n e c k in in f o r m a tio n p r o c e s s in g attended Numerous Sensory Inputs 3 I. Introduction (cont) B . D e m o n s tr a tio n S o m e w h e r e A m o n g h id d e n t h e in m o s t th e s p e c ta c u la r R o c k y M o u n ta in s c o g n itiv e n e a r a b ilitie s C e n tr a l C ity i s C o lo r a d o t h e a n a b ility o ld to m in e r s e le c t h id o n e a m e s s a g e b o x f r o m o f a n o th e r . g o ld . W e A lth o u g h d o s e v e r a l t h is h u n d r e d b y p e o p le f o c u s in g h a v e o u r lo o k e d a tte n tio n f o r o n it, c e r ta in th e y c u e s h a v e s u c h n o t a s f o u n d t y p e it c o lo r . 4 II. Early vs. Late Selection A . E a r ly s e le c tio n : Input #1 Perceptual Pattern C ontrol Processes Input # 2 Filter Recognition e.g., memory response organization e tc. II. Early vs. Late Selection (cont) 5 B . L a te S e le c tio n Input #1 Pattern Input # 2 Recognition Output Filter Control Processes e .g., memory response organization e tc. 6 III. Broadbent’s Filter Model A . P r o p e r tie s : 1 ) E a r ly s e le c tio n 2 ) Selection (fi ltering) is based on physical p r o p e r tie s o f th e s tim u lu s ( e .g ., p itc h , lo u d n e s s , e tc ...) . 3 ) A tte n tio n is d ir e c te d to in f o r m a tio n th a t p a s s e s th e fi lte r o r to p h y s ic a lly s a lie n t in f o r m a tio n th a t le a d s to a s h if t in a tte n tio n . 4 ) O n ly o n e in p u t c h a n n e l c a n b e p r o c e s s e d a t a tim e . 5 ) I t ta k e s tim e to s h if t a tte n tio n . III. Broadbent’s Model in pu t #1 5 3 1 i np ut # 2 6 4 2 B . P h y s ic a l A n a lo g y o ut p u t i n c o nd i t i o n 1 : 1 , 2 , 3 , 4 , 5 , 6 o u tp u t i n c o n d i t i o n 2 : 1 , 3 ,5 , 2 ,4 , 5 8 III. Broadbent (cont) C . S u p p o r tin g E v id e n c e : B r o a d b e n t ( 1 9 5 4 ) : s p lit s p a n e x p e r im e n t S tim u lu s p r e s e n ta tio n ear #1 e ar# 2 7 8 4 2 3 6 R esponses c ondition 1: recall by ear: “743-826” c ondition 2: recall in order: “78, 42, 36” f/f f/f m /f m /f 9 III. Broadbent (cont) C . S p lit S p a n E x p e r im e n t ( c o n t) : R e s u lts : C o n d itio n 1 : 6 5 % c o r r e c t C o n d itio n 2 : 2 0 % c o r r e c t 10 III. Broadbent (cont) D . P r o b le m s w ith th e m o d e l: 1. “cocktail party” phenomenon 2. Moray’s experiments a ) s h a d o w in g b ) in f o r m a tio n r e ta in e d f r o m th e u n s h a d o w e d e a r in c lu d e d : i m p o r ta n t w o r d s ( fi r e ) s u b je c t ’ s n a m e c o n te x t r e le v a n t in f o r m a tio n IV. Treisman’s Attenuation Model 11 A . P r o p e r tie s : 1 ) E a r ly s e le c tio n 2) Selection (attenuation) is based on p h y s ic a l p r o p e r tie s o f th e s tim u lu s ( e .g ., p itc h , lo u d n e s s , e tc ...) . 3 ) A tte n tio n is d ir e c te d to w a r d in f o r m a tio n that reaches a threshold of recognition. 4 ) S e v e r a l in p u ts c a n b e p r o c e s s e d a t a tim e . IV. Treisman’s Attenuation Model B . D ia g r a m o f th e m o d e l: Mental Dictionary High Thresholds "table" "fire" "green" "chocolate" "help" your name Low Subjective Loudness Attenuator Physical Selection (pitch, loudness, etc.) Shadowed Message Other Input 12 13 Thresholds of Recognition H ig h F r e q u e n c y W o r d s L o w F req u en cy W o rd s (lo w th re s h o ld s ) (h ig h th re s h o ld s ) e_ fo _ t s _ ro _ g h _ r_ e d _ r_ s s s _e_e h _ r_ e 14 IV. Treisman’s Model (cont) S u p p o r tin g E v id e n c e ( T r e is m a n , 1 9 6 0 ) E a r # 1 T h e b o d y w a s b u rie d o n M o ll L e g g Is la n d b e s id e th e a h e a d lis te n a rm s o m e b o d y N o rth C a ro lin a . E a r # 2 W e p o in t v e ile d th e ir m a n y w ife h e tu s s le s la s t o th e r g ra v e a n d a c ro s s p u t a t its h e a d . fe m a le /m a le 15 V. Norman’s Model (1968) A . P r o p e r tie s : 1 ) L a te s e le c tio n : i.e ., a ll s tim u li a r e p r o c e s s e d to s tim u lu s r e c o g n itio n . 2) Selection (pertinence) is based on the im p o r ta n c e o f th e r e c o g n iz e d ite m . 3 ) M e m o r y p r o c e s s e s ( e .g ., r e h e a r s a l) a r e d e v o te d to s e le c te d in p u ts . 16 V. Norman’s Model B . D ia g r a m o f th e m o d e l: 17 c. Real life nature of the issue: CF-5 Freedom Fighter cockpit 18 VI. Resolution A . T r e is m a n & G e f f e n ( 1 9 6 7 ) T a s k 1 : s h a d o w m e s s a g e in o n e e a r . T a s k 2 : s u b je c ts a s k e d to ta p p e n c il w h e n they heard the target word “green.” R e s u lts : ta p p in g to “ g r e e n ” in s h a d o w e d e a r : 8 7 % ta p p in g to “ g r e e n ” in th e o th e r e a r : 8 % 19 VIII. Resource Allocation A . D u a l ta s k p e r f o r m a n c e : s im p le m u ltip lic a tio n ta p p in g VII. Resource Allocation Models: 20 B . D ia g r a m o f a C a p a c ity th e o r y , based on Kahneman, 1973. c. Automatic vs. controlled processes. (Shiffrin & Schneider, 1977) Automatic Processes Do not require attentional resources Occur without intention Not available for conscious inspection Well practiced responses Fast Controlled Processes Require resources Require conscious intention Conscious activities Not well practiced Slow Examples: freeway driving recognition of frequent words driving in an unfamiliar city recognition of rare words 21 22 Example of automatic processing S tr o o p ( 1 9 3 5 ) e f f e c t 23 Stroop Continued Blue Yellow Green Red Green Blue Red Green Yellow Blue Green Red Automatic vs. Controlled processes (cont) Klapp, Boches, Trabert, and Logan (1991) D u a l ta s k p e r f o r m a n c e : 1 ) a lp h a b e t a r ith m e tic Initial Letter B N Addends 2, 3, 4 2, 3, 4 Responses D, E, F P, Q, R 2 ) m o n th s a y in g ( J a n u a r y , F e b r u a r y , M a r c h , A p r il ...) 24 25 Klapp, Boches, Trabert, and Logan (1991) r e s u lts Reaction Time (ms) 2400 Pretest 2200 Posttest 2000 1800 1600 1400 1200 1000 2 3 4 Addend D. Resource Allocation and Cell Phones 26 Cell phone use may be a controlled process, requiring attention, leading to a reduction in resources to devote to driving. M a n y s ta te s a r e p a s s in g la w s lim itin g c e ll p h o n e u s e w h ile d r iv in g . 'H a n d s - o f f ' d r i v i n g l a w i s O K 'd f o r a g e s 1 6 , 1 7 27 Y o u n g m o to ris ts c a u g h t u s in g g a d g e ts fa c e fi n e s By Michael Gardner COPLEY NEWS SERVICE S e p te m b e r 1 4 , 2 0 0 7 SACRAMENTO – Gov. Arnold Schwarzenegger has sent a message to young drivers: no text messaging, no cell phone calls, no using laptops. At least not while behind the wheel. Schwarzenegger signed legislation yesterday that will make it illegal for drivers younger than 18 to engage in such activities. “I t will save lives. It's really that simple,” said state S en. Joe Simitian, a Palo Alto Democrat who carried the measure, Senate Bill 33. S a n d ra T o rre s , le ft, th e ju n io r c la s s p re s id e n t a t S e q u o ia H ig h S c h o o l in R e d w o o d C ity , p le a d s w ith G o v . A rn o ld S c h w a rz e n e g g e r fo r th e p e n h e u s e d to s ig n a b ill th a t b a n s te e n a g e d riv e rs fro m u s in g e le c tro n ic d e v ic e s s u c h a s c e ll p h o n e s , p a g e rs a n d la p to p s w h ile d riv in g . 28 Cell Phones (cont) Field Studies (e.g., Redelmeier & Tibshirani (1 9 9 7 ) - c e ll p h o n e u s e a s s o c ia te d w ith a 4 - f o ld in c r e a s e in a c c id e n ts - n o r e d u c tio n in a c c id e n ts f o r th o s e w h o u s e d h an d s -free p h o n es . 29 Cell Phones (cont) S tr a y e r & J o h n s to n ( 2 0 0 1 ) S im u la te d d r iv in g s tu d y D ual T asks: 1 ) S im u la te d d r iv in g : a ) u s e a jo y s tic k a n d a c o m p u te r to k e e p th e c u r s o r a lig n e d w ith a m o v in g ta r g e t. b ) p r e s s a “ s to p ” b u tto n w h e n th e c u r s o r tu r n e d r e d . 2 ) D is tr a c tio n : ( 3 c o n d itio n s ) a ) c o n v e r s e w ith a c o n f e d e r a te o n h a n d h e ld p h o n e b ) c o n v e r s e w ith a h a n d s f r e e p h o n e c ) lis te n to a r a d io b r o a d c a s t o f th e ir c h o ic e 30 Results: 1 . N o d if f e r e n c e in r e s u lts in th e h a n d - h e ld v e r s u s th e h a n d s - f r e e c o n d itio n s . 31 Results: 2 . C e ll P h o n e u s e in c r e a s e d th e lik e lih o o d o f “missing” the “stop” signal. 32 Results: 3 . C e ll P h o n e u s e s lo w e d r e a c tio n tim e to th e “stop” signal. Strayer & Johnston (2003) T e s tin g d r iv in g in a s im u la to r ( F o r d C r o w n V ic to r ia ) : 33 34 Strayer & Johnston (2003) cont. E x p . 1 : te s te d a b ility o f c e ll p h o n e u s e r s to r e s p o n d to a v e h ic le b r a k in g in f r o n t o f th e m . 4 0 p a r tic ip a n ts d r o v e 4 0 m ile s : c o n tr o l: n o t ta lk in g o n c e ll p h o n e s e x p : ta lk in g o n “ h a n d s - f r e e ” p h o n e s I n h e a v y tr a f fi c , 3 c e ll p h o n e u s e r s r e a r - e n d e d th e v e h ic le in f r o n t o f th e m . N o n e o f th e m d id s o in th e c o n tr o l c o n d itio n . IX. Conclusion on Attention & Resource Allocation 1 ) P a r tia l s e le c tio n o c c u r s e a r ly in th e in f o r m a tio n p r o c e s s in g s tr e a m . 2 ) S e le c tio n is n o t th e r e s u lt o f a c tio n o f a s im p le p h y s ic a l fi lte r . 3 ) T h e s e le c tio n p r o c e s s is s e n s itiv e to th e p a s t e x p e r ie n c e s ( th r e s h o ld s ) o f th e p e r s o n a n d th e c o n te x t o f th e r e c o g n itio n ta s k . 4 ) P e r f o r m a n c e o f m u ltip le ta s k s is a c o m p le x p r o c e s s o f a llo c a tin g lim ite d r e s o u r c e s a n d p e r f o r m a n c e o f s o m e o p e r a tio n s th a t a r e a u to m a tic a n d th u s d o n o t r e q u ir e r e s o u r c e s . 35 ...
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This note was uploaded on 09/26/2011 for the course PSYCHOLOGY 110 taught by Professor Kannan during the Spring '11 term at Anna University Chennai - Regional Office, Coimbatore.

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