Cognitive Psychology
Attention
Overview
Exam
Detection.
Filtering and selection.
Search.
Automatic processing.
Concentration
Maybe If We’re Lucky:
Spotlight
Inhibition of Return
PRP
Change Blindness
Simon Effect
Attention in sports
What do these have in common?
You are driving to a lunch date, and accidentally take the
route to your job. After you correct your route, as you are
driving by the theatre, a red ball chased by a child suddenly
appears on the street, and you screech your brakes. You get to
the restaurant and try to find your friend, who has flaming red
hair. The restaurant is packed, it’s hard to make-out faces, but
you can see people’s hair so you look for red hair. When you
get to your table your friend asks if you noticed the Star Wars
promotion with two costumed people fighting with light
sabers. As you talk about important but dull business, your
mind keeps drifting to your exciting first date last night. You
force yourself not to think about it, but it keeps coming back.
 Innatentional Blindness (UK cycling commercial)
 http://www.youtube.com/watch?v=Ahg6qcgoay4&eurl=http://www.dothet
est.co.uk/
 Attentional Resources (Tide commercial)
 http://www.youtube.com/watch?v=vgtfC5LBAW4
What do these have in common?
1. Detection.
2. Filtering and selection.
3. Search.
4. Automatic processing.
5. Concentration.
The common element is attention.
Architecture
The box model:
Sensory
Store
Filter
Input
(Environment)
Pattern Selection
Recognition
STM
Response
LTM
Attention
 In this model, attention is:
 The filter and selection boxes
 The arrows.
 The special job carried out by each of these boxes
according to different theories of attention
 (Yes, this is cheating)
 In this model attention:
 Puts together information from various sources.
 Gets information into STM
 Works in imagery
Attention
 Highlights parts of the environment and
blocks other parts.
 Primes a person for speedy reaction.
 Helps you retain information.
Attention

As you can see from the attempts to define it,
attention is usually defined as what it does. As a
result, we’re going to study it as five kinds of
things.
1. Detection.
2. Filtering and selection.
3. Search.
4. Automatic processing.
5. Concentration.
Quizz
We discussed five aspects of attention.
Which of the following was not one:
a. selection
b. automaticity
c. forgetting
d. concentration
Themes
 Early or Late? In other words, when
does “meaning” stamp the stimulus
 What is it?
 Some sort of
bottleneck
or
filter?
 A capacity or resource (or several kinds)?
 Can we learn something by looking for it in
brains?
Detection
 Two kinds of thresholds:
 Absolute Threshold: Minimum amount
of stimulation required for detection.
 Difference Threshold (“Just Noticeable
Difference”): Amount of change
necessary for two stimuli to be perceived
as different.
Detection
 Absolute Thresholds:
 Vision: One candle, on a mountain, perfectly dark, 30




miles.
Hearing: A watch ticking 20 feet away.
Smell: A single drop of perfume in a three room
apartment.
Touch: The wing of a bee on your cheek.
Taste: One teaspoon of sugar in two gallons of water.
Determining Thresholds
 How to determine thresholds:
 Method of limits:
 Ascending: Start with a value below the threshold,
increase, ask for detection, increase… At the point a
person says “detect,” average that stimulus value
with the value from the previous trial. Repeat to
estimate threshold.
 Descending: Same, but start above threshold and
work down.
 Combining results from both directions will
give you an estimate of the threshold.
Determining Thresholds
 How to determine thresholds:
 Method of constant stimuli:
 Present a series of randomly selected
stimulus values, ask for yes/no response for
each. The value that’s detected 50% of the
time is the threshold.
 These methods can be adapted to
determine difference thresholds.
Determining Thresholds
 We think thresholds work like a step function, but
they don’t. They are sigmoid or ogive curves
This graph represents a step function. Below the
threshold there is 0% detection. Above the threshold,
there is 100% detection. This is the way we normally
believe our perception to work.
This graph represents an ‘ogivecurve’ and how detection really
changes – it is a gradual slope. The
threshold is defined as a 50%
detection rate.
Quizz
Methods introduced by Fechner included
a. method of constant stimuli
b. method of limits, ascending
c. method of limits, descending
d. method of constant sorrow
Determining Thresholds
 Difference Threshold:
 Weber’s Law:
K = ΔI / I
 K is the Konstant
 Δ is the difference
 I is the stimulus intensity
 The formula states that the threshold for noticing a
difference (whether it’s the length of a line or weight
of a dumbell) is a constant ration between the ‘old’ /
background stimulus and the ‘new’ / target stimulus.
Determining Thresholds
 But there is a problem: Thresholds Shift
These are ogive curves for
stimuli of the same intensity but
with different signal to noise
ratios or payoff matrix
 How to get around this problem: A model that
accounts for signal to noise ratios and payoff
matrixes  Signal Detection Theory
Weber’s Law states that
a. The just noticeable difference is a constant
ratio of the original stimulus.
b. attention is a process of selecting and
ignoring.
c. what goes up must come down.
d. the noise distribution is false
Signal Detection
 Can estimate detection (sensitivity) independent of
bias.
 Two kinds of trials:
 Noise alone: Background noise only.
 Signal+noise: Background noise with signal.
 Two responses from observer:
 Detect.
 Don’t detect.
Signal Detection:
Four Situations
State of the world
Signal
Noise
Yes (Present)
Hit
False Alarm
No (Absent)
Miss
Correct Rejection
Response
Hits
(response “yes” on signal trial)
Probability density
Criterion
N
Say “no”
S+N
Say “yes”
Internal response
Correct rejects
(response “no” on no-signal trial)
Probability density
Criterion
N
Say “no”
S+N
Say “yes”
Internal response
Misses
(response “no” on signal trial)
Probability density
Criterion
N
Say “no”
S+N
Say “yes”
Internal response
False Alarms
(response “yes” on no-signal trial)
Probability density
Criterion
N
Say “no”
S+N
Say “yes”
Internal response
Signal Detection:
Sensitivity and Bias
 We can estimate two parameters from
performance in this task:
 Sensitivity:
Ability to detect.
 Good sensitivity = High hit rate + low false alarm rate.
 Poor sensitivity = About the same hit and false alarm rates.
 Response Bias: Willingness to say you detect.
 Can be liberal (too willing) or conservative (not willing
enough).
 For example, if the true signal to noise ratio is 50% and you
have a 75% detection rate, then your response bias is to be too
liberal.
Signal Detection:
Sensitivity and Bias
 Computing
sensitivity or d’ (“d-prime”)
 Is a measure of performance (like percent correct, or
response time)
 Typical values are from 0 to 4 (greater than 4 is hard to
measure because performance is so close to perfect)
 A d-prime value of 1.0 is often defined as threshold.
d-Prime
 d-prime is the distance between the N and S+N
Probability density
distributions
 d-prime is measure in standard deviations (Z-Scores)
 In SDT, one usually assumes the two underlying
distributions are normal with equal variance (i.e.,
both curves have the same standard deviation)
d’
N
S+N
Internal response
Signal Detection:
Sensitivity and Bias
 Computing bias:
 The criterion is the point above which a person
says “detect.” It can be unbiased (the point
where the distributions cross; 1.0), liberally
biased (< 1.0), or conservatively biased (> 1.0).
Signal Detection:
Sensitivity and Bias
 Since sensitivity and bias are independent, you can
measure the effect of different biases on
responding to a particular value for detectability.
 Influences on bias:
 Instructions (only say “yes” if you’re absolutely
sure).
 Payoffs (big reward for hits, no penalty for false
alarms).
 Probability of signal (higher probability leads to
more liberal bias).
Signal Detection:
Sensitivity and Bias
 Receiver operating characteristic (ROC)
curves:
 For a given detectability value, you can
manipulate the hit and false alarm rates. An
ROC curve shows the effect of changing bias
for that level of detectability.
Sample ROC Curves
Very sensitive observer
1
0.9
%
of
Hits
P r op ortion H
0.8
0.7
0.6
Moderately sensitive observer
0.5
0.4
Zero
Medium
High
0.3
0.2
Zero sensitivity
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Proportion FA
0.7
0.8
0.9
1
Optimal Performance
 Depending on the probability of a signal trial and
the payoff matrix, the optimal placement of the
criterion will vary.
p(N)
opt =
value (CR) - cost (FA)
X
p(S)
value (H) - cost (M)
 You can compare performance to the ideal
observer to assess the operator.
Quizz

A.
B.
C.
D.
The Titanic hitting an iceberg would be a
pretty good example of a
hit
miss
false alarm
correct rejection
Quizz
Bob, an electrician, is trying to see how faint he can
make a light. He starts by turning the light ON to
its maximum, then turning it down until he
cannot see it.
a) Difference detection; methods of limits; ascending
b) Difference detection; method of constant stimuli;
random
c) Absolute thresholds; method of limits; descending
d) Absolute thresholds; constant stimuli; random
Quizz
In Signal Detection Theory, which of the following
is not true:
A. attention requires more hits than false alarms
B. there is a normal distribution for signal and one
for noise with the distance apart measured in Zscores
C. d-prime measures the difference between signal
and noise
D. bias and sensitivity are independent
Filtering
 How do we choose what to attend to? Is the
choice made early or late?
 We’ll look at several versions of filter
models and some of the evidence.
Filtering
Attended
Sensory
Store
Unattended
 Early: Broadbent.
Selection happens at the
filter and sensory store
before pattern
recognition. The
selection is made at the
EARLY STAGE of
crude physical analysis.
Filter
Pattern
Recognition
Selection
Shortterm
memory
Filtering
 Early: Evidence:
“7-4-1”
 Dichotic listening. Two messages, one to each
ear, played simultaneously.
 Shadowing: Repeat out loud everything in one ear.
What do people (or what don’t people) notice in the
unattended ear?
 Miss change of speaker.
 Miss change of language.
 Miss change of direction.
“3-2-5”
Filtering
 Early: Evidence:
“7-4-1”
 Filter flapping: Two sets of numbers come in,
one set in each ear.
 Report by ear: Easy.
 Report in order: Hard.
 The argument is that the filter lets in all of one
channel, then the other, no problem. To switch
back and forth takes a lot of effort.
“3-2-5”
Filtering
 Problem for early models:
 People detect their name on the unattended
channel (cocktail party phenomenon).
 Treisman (1960): If a shadowed story switches
ears, people follow it, and then correct. They
have to be attending to meaning to follow the
story.
Filtering
 Problem for early models:
 Example 1:
 …I SAW THE GIRL/song was WISHING…
 …me that bird/JUMPING in the street…
 Example 2:
 …AT A MAHOGANY/three POSSIBILITIES…
 …look at these/TABLE with her head…
Filtering
Attended
Sensory
Store
Filter
Pattern
Recognition
Selection
Shortterm
memory
Unattended
 Attenuation model:
 Everything in memory is active at some resting level. Some stuff
that’s important has a high resting level, making it easier to
respond to (e.g., your name).
 Other stuff has a lower resting level, making it harder to respond
to.
 As you think about something, you raise its resting level.
Filtering
 Attenuation model:
 The unshadowed ear is attenuated (the volume
is low). This little bit of attention can reach
something with a high resting level (your name,
a story you’re shadowing), but not some
random bit of information.
 So, no filter, just attenuation.
Filtering
 Capacity model:
 You have a certain amount of attention, you can spread
it around as needed. If you spend a lot on one task, then
you have less for others.
 Primary task: Do well on this no matter what (main focus of
resources).
 Secondary task: Also do this.
 By manipulating the difficulty of the primary task and
measuring the secondary task, we can see how attention
allocation affects performance.
Filtering
 Capacity model:
 For example, Johnston and Heinz (1978)
had two tasks:
 Primary: Shadow one ear. This can be based
on gender or category.
 Secondary: Detect a light.
Filtering
 Capacity model: Johnston and Heinz (1978)
Primary
Secondary
Shadow one list 1.4% error
(control)
Easy (gender)
5.3% error
310 ms
Hard (category) 20.5% error
482 ms
370 ms
Filtering
 Capacity model:
 What this implies is that the filter can be early (gender)
or late (category), the amount of your resources that
you allocate to it determines where the filter is.
Quizz
You’re walking to class and thinking about a quiz
that’s coming up. Someone calls your name, but
you don’t hear them.
a) Your ROC curve is high.
b) This counts as a hit
c) You are filtering for perceptual features
d) You are filtering for categorical or semantic
information
e) You didn’t study and you can’t hear people while
throwing-up
Search
 How do you use attention to locate items in a
complicated array? Two kinds of search:
 Feature search: A single feature allows you to find the
item you are searching for.
 Find the blue S.
Search
X
X
T
T
T
T
X
T
X
S
X
X
T
X
X
T
Search
X
T
X
T
T
T
X
T
X
T
X X
T
X
T
T
T
X
S
T
X X
T
X
X X
T
X
T
X
T
X
T
T
T
X
T
X
T
X
line (blob) orientation
Julész & Bergen 83;
Sagi & Julész 85a,
Wolfe et al. 92;
Weigle et al. 2000
curvature
Treisman &
Gormican 88
length, width
closure
Sagi & Julész 85b; Julész & Bergen
Treisman &
83
Gormican 88
colour (hue)
density, contrast
Nagy & Sanchez
Healey & Enns 98; 90; Healey 96;
Healey & Enns 99 Bauer et al. 98;
Healey & Enns 99
size
Treisman &
Gelade 80; Healey
& Enns 98; Healey
& Enns 99x
Search
 How do you use attention to locate items in a
complicated array? Two kinds of search:
 Conjunction search: You have to combine features to
find the item you are searching for. This should take
attention and be more difficult (Treisman, 1988).
 Find the green T.
Search
X
X
T
T
T
T
X
T
X
T
X
X
T
X
X
T
Search
X
T
X
X T
X
T
T X
T
X X
X T T XT
X T X TT
TT
XX
TX T X
X XX
T
X
T
T X T X
X
T
T
T
T
X
T
X
T
T
X
T
X
T
X
T
Properties of searches:
 Feature searches:
 Don’t require attention
(pop-out).
 No help from location
cueing (don’t need it).
 Conjunction searches:
 Require attention.
 Affected by the number of
distracters.
 Helped by cueing the
location.
Feature Conjunction: Attention as
Glue
~ The significance between conjunctive and disjunctive searches is that
it means that individual features like color and size are loaded preattentively (attention is not required), but a conjunctive search requires
attention to bind the two features to the object to a location in space. You
need attention to know an object is both red and large and where it is.
~ The integration may happen in the visual cortex as a result of
synchrony, with attention affecting the tuning properties of sensory
neurons, and preparing other cognitive processes like working memory.
Treisman’s Feature Integration Theory
– A two-stage theory of visual attention.
Stage 1. fast parallel for single features
Stage 2. Slow serial for conjunctions of
single features.
Several primary visual features are
processed and represented with
separate feature maps that are later
integrated in a saliency map that can
be accessed in order to direct attention
to the most conspicuous areas.
A parallel search, a red circle amidst
green circles, takes no time no matter
how many green circles (it’s cheap). A
serial search, with conjunction
features, like red circles amidst black
circle and red triangles, requires you to
check each distractor serially.
Quizz
You are looking for a friend at a party. This person
has brown hair and is very tall.
a) You are performing a serial search, that will be
affected by the number of people there
b) You are performing two serial searches, and the
person will “pop-out”
c) You are performing a conjunction search which
will be affected by the number of people there
d) You are performing a conjunction search and the
person will “pop-out” because there is nobody
else there with those two qualities.
Automatic Processing
 After practice, some tasks no longer require attention.
Three criteria for automatic tasks:







Occur without intention.
When the load is low
Required reaction times are short
The tasks are “over-learnt” or well-practiced
No conscious awareness/Can’t be introspected.
Don’t interfere with other activities.
Fast processes -- the brain does them ‘automatically’, they are a
basic feature
 You can tell how the process of automatization is going by
doing dual task studies (primary and secondary).
Automatic Processing
 Read the
Words.
 Say the colors
 Which is
harder?
Automatic Processing
 You did the Stroop task.
 The interpretation is that you automatically read
the word. If that’s the task, the color doesn’t
interfere because you don’t automatically register
that. If you’re supposed to name the color,
automatic reading messes you up.
Quizz
You drove home and did not stopping at the store.
a) This was due to a search failure because the sign
for the store did not pop-out.
b) You have a lot on your mind, and you are easily
distracted
c) Going to the store is a conscious decision, but
you were filtering based on perceptual features
d) Driving home is an automatic process
e) Driving home is a conditioned response
Concentration
 Our last topic has to do with the task of “paying
attention.”
 Sometimes you have to concentrate on something in
which you have no interest.
 Sometimes you have to not think about something in
which you have an interest.
Concentration
 Wegner, Schneider, Carter, and White (1987).
 Try not to think of a white bear.
 Five minutes, measure the number of times people do
it.
 Or, try to think of it.
 Both are hard, with less activity later on.
Concentration
 Wegner, Schneider,
Carter, and White
(1987).
 After suppression, it’s
easier to keep thinking
about a white bear.
 After expression, it’s
still hard not to think of
a white bear at first, but
people adapt.
Quizz
You have a big date tonight, and you can’t stop thinking about
it, even though you tell yourself you have to focus on
tomorrow’s International Trivia Pursuit competition.
a) Keep telling yourself that while resting in bed, because it
will eventually work. You will habituate.
b) Write the name of your date down on paper, while saying
it aloud, over and over. You will get desensitized.
c) Prepare for the competition, acknowledging to yourself
when you are thinking of the date. You will minimize the
rebound.
d) Practice for the competition, suppressing the thought of
your date. You will maximize concentration.
Posner Cueing Task
Central cue
peripheral cue
cue
ISI
target
Central Cue condition
triggers endogenous attention /
voluntary attention
Top-down
Peripheral Cue condition
triggers exogenous attention/
Reflexive attention
Bottom-up
Inhibition of Return
The “Been There, Done That” Reflex
 We are faster at unpredicted cues after a long
enough pause
 IOR prevents going over the same ground,
promotes searches for novel stimuli
• The findings from patients with brain damage led Posner
to construct a model for attention that involves three
separate mental operations:
• Disengaging of attention from the current location
• Moving attention to a new location
• Engaging attention in a new location to facilitate
processing in that location.
Psychological Refractory Period
(PRP)
Timing the Central Bottleneck
 A: Multiple sensory input
 B: Serial Decision maker
 C: Multiple action output
Stimulus 1
RT
ms
Stimulus 1
Stimulus 2
A
A
SOA
Stimulus 2
Slope = 1
25 150 400
SOA
900
B
Time
C
PRP
B
C
 Line Bisection and
flower drawing are
examples of spatialbased neglect.
 The dumb-bells are
an example of object
based neglect
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