Cognitive Psychology
Attention
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 (original experiment)
 http://www.youtube.com/watch?v=vJG698U2Mvo
 Change Blindness (office)
https://www.youtube.com/watch?v=diGV83xZwhQ
Change Blindness
 Counter experiment: http://www.youtube.com/watch?v=mAnKvofPs0
 Campus Door Demo:
 http://viscog.beckman.uiuc.edu/flashmovie/12.php
 Construction door
http://viscog.beckman.uiuc.edu/flashmovie/10.php
Gradual Change:
http://viscog.beckman.uiuc.edu/flashmovie/1.php
Aspects of Attention
1. Detection.
2. Filtering and selection.
3. Search.
4. Automatic processing.
5. Concentration.
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
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.
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
 Early Researchers Noticed: 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
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
%
of
Hits
Proportion H
Very sensitive observer
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Moderately sensitive observer
Zero sensitivity
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Proportion FA
1
Zero
Medium
High
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.
Examples of Visual Search
Is there a threat?
Where’s Waldo?
Search
 How do you use attention to locate
items in a complicated array? Two
kinds of search: Feature Search and
Conjunction 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?
 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
Simple feature search
Look for an “O”
T
T
T
T
T
T O
T
T
T
T
T T
T
T T
T T
T
T
T
T TT T T T
T
T
T
T T T T
Simple feature search
Look for something red
O
T
T
T
O
T O T
T
T
O
T
O
T
O
TT O
O T T
O T
T O
O O
T O
O
T T
Conjunctive feature search
Look for something red AND “O”
O T T
T O
T
T T O
T
T
T
O
T O T O
O
T O
T
T
T
O
T
O
T O T
O O
O
T
OT O T
Response Time
Conjunctive Search
Simple feature search
Number of Stimuli in Display
Properties of searches:
 Feature searches:
 Conjunction
 Don’t require
searches:
attention (popout).
 No help from
location cueing
(don’t need it).
 Require attention.
 Affected by the
number of
distracters.
 Helped by cueing
the location.
Pop-Outs in Advertisement
Scan Paths
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
pre-attentively (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.
Attention as Glue
Keep your eye on the fixation point below. A screen with
colored letters will be briefly flashed. Try to remember as many
letters with their colors as you can.
+
L
M
T
Q
S
P
Q
B
O
H
U X
V
Z
K
Attention as a Glue
 What color was the X?
 Do you distinctly recall a particular letter
being a different color? How did that
happen? How did a color in one location get
associated with an object in another
location?
 This is “attention as a glue”
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.
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.
Filtering
 So, thresholds shift…but once set, then
what? What happens when something gets
over the threshold wherever it is? When
does meaning become involved?
 How do we choose what to attend to? Is the
choice made early or late?
Themes
 Early or Late? In other words, does
something get chosen before or after
(respectively) the “stimulus gets stamped with
meaning”
 What is attention?
 Some sort of
bottleneck
or
filter?
 A capacity or resource (or several kinds)?
 Can we learn something by looking for it in brains?
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 low resting level, making it harder to respond
to.
 As you think about something, you raise its activity 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 for a change that is
easy (gender) or a change that is hard
(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.
Emotion Driving Attention
• Detecting a Snake in the Grass (Ohman, Flykt,
Esteves, 2001)
• Stimuli; snakes, spiders, mushrooms, flowers
• Presented in 2x2 or 3x3 displays
• Task; “Do all the pictures belong to the same
category?”
Emotion Drives Attention
• Reaction time to detect
target in ms.
Fearful Neutral
2x2
950
1010
3x3
950
1010
Emotion Drives Attention
• The Emotional Stroop Effect
– You are slower at naming a color of emotionally
charged words than neutral words
• Taboo words vs neutral words
Emotion Drives Attention
• Classical Stroop
RED
GREEN
BLACK
YELLOW
BLUE
RED
BLACK
RED
GREEN
BLACK
YELLOW
BLUE
RED
BLACK
Attic
Bitch
Shit
Anus
Frame
Dyke
Senate
Note
Bank
Queer
Scrotum
Wife
Emotion Drives Attention
Emotion Drives Attention
• Emotional Stroop effect occurs with
Taboo words
Alcohol words (beer) in alcoholics
Smoking words (cigarettes) in smokers
Spider words (web, crawl) in arachnophobics
Food pictures in females with anorexia
Threat words (disease) with people with anxiety
disorders
The Dot Task: Detection in PTSD
Mood and Attention: Levels of Focus
(Gasper & Clore, 2002)
• Hypothesis: Affective cues may be experienced as
task-relevant information, which then influences
global versus local attention.
• Mood Manipulation: Subjects randomly assigned to
write about a happy or sad event in their lives
• Each participant randomly assigned to a drawing
chain, where the first person in each group saw a
drawing of an African shield with the title of “Portrait
of a Man.” In a later session, a 2nd person saw the 1st
person’s reproduction from memory, and so on.
Local Global
 Lesions in LH produce deficits in local perception
 Lesions in RH produce deficits in Global
Mood and Attention: Levels of Focus
(Gasper & Clore, 2002)
• Happy Mood condition more likely than Sad
Mood to contain schema-relevant details like
title and facial features
• Sad Mood drawings became less face-like
down the chain but not Happy Mood drawings
• Sad Mood drawing looked less like original
Mood and Attention: Levels of Focus
(Gasper & Clore, 2002)
• Experiment 2 employed a task in
which the same objects were
sometimes the global and
sometimes the local stimulus
(Kimchi & Palmer, 1982).
Participants saw an overall
shape (e.g., a triangle) made up
of smaller geometric figures
(e.g., triangles). Their task was
to indicate which of two other
figures (e.g., a square made of
triangles or a triangle made of
squares) was more similar to this
target figure.
Result: Sadder people base their
decisions on the local features, and
report doing so.
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
Embodied cognition of attention: is
Cognition Time-Pressured?
• If we were designed to perform under pressure,
we would be good at it.
• But, the reality is that, under time pressure we
fall apart
• We actively avoid operating under conditions
where we are time pressured
• Most of daily behavior consists of mundane,
routine behavior
Embodied Cognition is Time Pressured
Wilson, 2002
• Summary
– Perceptuomotor processes are time-pressured, but
that does not illuminate cognitive performance
under time pressure
– Difficult to interpret whether “cognition is time
pressured” means we evolved to perform under
pressure or that our cognitive abilities must be
understood in the context of coping with
(unsuccessfully) or avoiding time pressures
 Line Bisection and
flower drawing are
examples of spatialbased neglect.
 The dumb-bells are
an example of object
based neglect
IMPLICIT
ASSOCIATION TEST
CATEGORY SWITCH
Insects
Bad
O

O

O
Flowers
Good
Get Kristin's
wonderfuldemo
Roach
O


O

nasty
O
O
Daisy
O
O
joyful
O
O
Tulip
O
O
terrible
O

WORD CATEGORIZATION
Flowers
Bad
O

O

O

O
Insects
Good
O
 
O

wonderful
Roach
nasty
O
Daisy
O
O
joyful
O
O
Tulip
O
O
terrible
O
IMPLICIT ATTITUDES
25
Number of
Items Correctly
Classified
20
15
10
5
0
Insects + Bad
Insects + Good
IMPLICIT BELIEFS
2000
Reaction Time
1500
1000
500
0
Insects + Good
Insects +
Bad
Stereotype Threat
(Beilock & McConnell, 2004)
 People perform in compliance with stereotypes
 In one of the first studies on stereotype threat,
Steele and Aronson (1995) had high-achieving
African American and Caucasian students at
Stanford University complete a portion of the
graduate record exam (GRE).
 Prior to doing so, some students were told that the
test was diagnostic of intellectual ability whereas
others were told that the test was a laboratory
problem-solving task not diagnostic of intellectual
ability.
Stereotype Threat
(Beilock & McConnell, 2004)
 Results demonstrated that after controlling for SAT scores
(to equate past academic performance), there was no
difference in GRE performance between White and Black
students for whom the test was not framed as diagnostic of
intellectual ability.
 Of those students who were told that the test was diagnostic
of intellectual ability, however, African Americans
performed significantly worse than Caucasians.
 Steele and Aronson argued that informing students about
the diagnosticity of the test activated the negative cultural
stereotype that “Blacks are not as intelligent as Whites,”
which contributed to the less-than-optimal performance of
African Americans on a test assumed to gauge intelligence.
Stereotype Threat: Memory or Attention
(Beilock & McConnell, 2004)
 How does stereotype threat work?
 One proposal is that stereotype threat produces reduces
working memory capacity
 But past research has shown that stereotype threat effects are most
pronounced for expert athletes, for whose abilities are highly
proceduralized, relying little on working memory.
 On the other hand, expert athletes “choke” when they start to
pay too much attention to the steps of their automatized
processes; this increased attention can backfire and disrupt
what should have been fluent, proceduralized execution. This
idea is often termed the “explicit monitoring hypothesis.”
Stereotype Threat: Memory or Attention
(Beilock & McConnell, 2004)
 Do stereotype threats reduce working
memory capacity, or do stereotype threats
prompt explicit monitoring of automated
procedures
 Expert male golfers perform a putt, before
or after hearing a stereotype (“men are
poorer putters than women”) or receiving
control information (“putting performance
differs as a function of skill level”).
 Experts who received stereotype did worse
Stereotype Threat: Memory or Attention
(Beilock & McConnell, 2004)
 Now how to determine it is attention? Introduce a
dual-task
 Experiment 2 (Beiock et al, 2003)
 Two groups with stereotyped and non-stereotyped
 One group performs putting alone
 Second group putts while listening for target word
 Results. Performance was the same for putters in
the dual & single task who had no stereotype
threat. For putters with stereotype threat,
performance was better in dual-task condition
(Stereo-type Threat affects attention, not memory)
The End
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
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
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.
Embodied cognition of attention: is
Cognition Time-Pressured?
• Cognition happens in “real-time” or “runtime”
– Cognition “must cope with predators, prey,
stationary objects and terrain as fast as the
situation dishes them out.”
– How do you get robots to think about walking on
uneven terrain, or to swing from branch to branch,
or looking around a crowded room looking for a
soda without bumping into something
– Story of legalizing sightdogs.
Embodied cognition of attention: is
Cognition Time-Pressured?
• Examples for:
– Skilled hand movements, or time-locked
perceptuomotor activity (catching, throwing, tying,
walking)
– Inhibition Of Return
– Exogenously driven attention
• Examples Against:
– PRP
– Task-switching
– Trade offs between speed and accuracy in attention
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.
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