1. Canadian
Partner
of
the
UX
Alliance
Exploi<ng
Cogni<ve
Bias:
Crea.ng
UX
for
the
Irra.onal
Human
Mind
Jay
Vidyarthi
Mobile
IVR
Website
iTV
So6ware
1

2. The
Plan.
Understanding
“cogni<ve
bias”
…an
easy
way
to
apply
psychology
into
your
day
to
day
work!
2
2

3. Exploi<ng
Cogni<ve
Bias:
Crea.ng
UX
for
the
Irra.onal
Human
Mind
1::
USER-­‐FACING
ELEMENTS
DEMAND
A
PSYCHOLOGICAL
APPROACH
2::
HUMANS
ARE
NOT
STRICTLY
LOGICAL
COMPUTERS
3::
COGNITIVE
BIASES
HELP
PREDICT
HUMAN
IRRATIONALITY
4::
APPLYING
SPECIFIC
COGNITIVE
BIASES
TO
USER
EXPERIENCE
PRACTICE
5::
IF
YOU
ONLY
REMEMBER
ONE
THING,
REMEMBER
THIS
3

4. User-­‐facing
elements
demand
a...
Psychological
Approach
4

5. User-­‐facing
elements
demand
a
PSYCHOLOGICAL
APPROACH
Our
users
are
human
beings:
Subjec.ve.
Cogni.ve.
Emo.onal.
Expressive.
Biased.
Inconsistent.
Unpredictable.
Diverse.
Irra<onal!
5
5

6. User-­‐facing
elements
demand
a
PSYCHOLOGICAL
APPROACH
Psychology
helps
us
understand
how
people
think…
…many
of
its
findings
are
directly
applicable
to
user
experience.
6
6

7. User-­‐facing
elements
demand
a
PSYCHOLOGICAL
APPROACH
A
ques.on:
Why
do
we
need
Psychology
to
build
computer
systems?
7
7

8. Humans
are
not
strictly...
Logical
Computers
8

9. Humans
are
not
LOGICAL
COMPUTERS
The
human
mind
is
constantly…
-­‐ Performing
Calcula.ons
-­‐ Forming
AZribu.ons
-­‐ Accessing
Memories
-­‐ Processing
and
Associa.ng
Inputs
-­‐ Making
Decisions
Sound
-­‐ Solving
Problems
-­‐ Weighing
Alterna.ves
Familiar?
-­‐ Drawing
Conclusions
-­‐ Crea.ng
and
Connec.ng
Ideas
-­‐ Etc.
9
9

10. Humans
are
not
LOGICAL
COMPUTERS
Computers
are
typically…
-­‐ Performing
Calcula.ons
-­‐ Forming
AZribu.ons
-­‐ Accessing
Memories
-­‐ Processing
and
Associa.ng
Inputs
-­‐ Making
Decisions
-­‐ Solving
Problems
-­‐ Weighing
Alterna.ves
-­‐ Drawing
Conclusions
-­‐ Crea.ng
and
Connec.ng
Ideas
-­‐ Etc.
10
10

11. Humans
are
not
LOGICAL
COMPUTERS
It’s
not
a
coincidence.
We’ve
created
technology
to
help
us
accomplish
our
cogni.ve
goals.
11
11

12. Humans
are
not
LOGICAL
COMPUTERS
Why
do
we
need
the
help?
Not
only
can
semiconductors
calculate
things
much
faster,
but
they
also
provide
us
with
a
strict
logical
approach.
It
takes
us
a
lot
of
effort
to
be
so
logical.
(that’s
why
Math
class
was
so
hard!)
12
12

13. Humans
are
not
LOGICAL
COMPUTERS
Computers
are
fully
ra<onal.
They
take
our
inputs
and
compute
logical,
ra.onal,
predictable
output.
(processing
billions
of
instruc.ons
per
second)
13
13

14. Humans
are
not
LOGICAL
COMPUTERS
The
human
mind
is
irra<onal.
It
uses
not
only
logic,
but
also
a
wide
range
of
other
factors.
14
14

15. Humans
are
not
LOGICAL
COMPUTERS
The
human
mind
is
irra<onal.
Logical
Conclusions
+
Emo.onal
State
+
Social
Circumstance
+
Perceptual
Biases
+
etc.
=
Decision
/
AZribu.on
/
Ac.on
15
15

16. Humans
are
not
LOGICAL
COMPUTERS
Technological
tools
enable
humans
to
perform
strict
logical
computa<on
quickly…
…but
our
irra<onal
minds
are
in
control
of
these
ra.onal
tools!
16
16

17. Humans
are
not
LOGICAL
COMPUTERS
A
good
technological
interface
connects
irra<onal
minds
to
logical
computers…
Logical
Controls Symbol
Manipulator
Irrational
User Interface Tailored
to Irrational Mind
17
17

18. Cogni<ve
Biases...
...help
predict
human
irra.onality.
18

19. COGNITIVE
BIASES
help
predict
human
irra.onality
So,
what
is
a
cogni.ve
bias?
Wikipedia’s
Defini<on:
“A cognitive bias is the human tendency to make
systematic errors in certain circumstances based
on cognitive factors rather than evidence.”
19
19

20. COGNITIVE
BIASES
help
predict
human
irra.onality
They
aren’t
necessarily
errors
or
mistakes…
"Rational decision-making methods... logic, mathematics, probability
theory... are computationally weak: incapable of solving the natural
adaptive problems our ancestors had to solve reliably in order to
reproduce... This poor performance on most natural problems is the
primary reson why problem-solving specializations were favored [sic]
by natural selection over general-purpose problem-solvers. Despite
widespread claims to the contrary, the human mind is not worse
than rational... but may often be better than rational."
- Cosmides & Tooby, 1994
20
20

21. COGNITIVE
BIASES
help
predict
human
irra.onality
My
all-­‐encompassing
defini.on:
A
cogni<ve
bias
represents
a
predictable
distor<on
in
our
percep<on
of
reality
based
on
using
cogni<ve
factors
and
heuris<cs
as
opposed
to
a
ra<onal
analysis
of
evidence.
(a
valuable
tool
for
the
design
of
technology
for
human
users)
21
21

22. Applying
specific
cogni.ve
biases
to...
User
Experience
Prac<ce
22

23. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Demonstra<ng
the
power
of
applying
cogni<ve
bias
to
UX:
Today’s
Menu:
-­‐ define
a
cogni.ve
bias
(or
two)
-­‐ describe
a
design
implica<on
of
the
bias
-­‐ present
a
real
example
of
this
design
implica.on
at
work
-­‐ lather,
rinse,
repeat!
23
23

24. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Nega<vity
Bias
“Bad
is
Stronger
than
Good”
(Baumister
et.
Al.,
2001)
Nega<ve
informa<on
has
a
stronger
impact
on
people
than
neutral
or
posi<ve
informa<on.
People
typically
pay
more
aeen<on
to
and
give
more
weight
to
their
nega<ve
experiences
over
their
posi<ve
ones.
24
24

25. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Nega<vity
Bias
Design
Implica<on:
USABLE
ERROR
MESSAGES
Best
prac.ces
for
error
messages;
-­‐
clearly
describe
the
problem
-­‐
provide
next
steps
toward
correc.on
Error
is
a
nega.ve
experience
and
will
weigh
heavily
on
UX.
Nega.ve
experiences
should
be
used
sparingly,
and
a
quick
recovery
is
necessary
to
maintain
posi.ve
UX.
25
25

26. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Nega<vity
Bias
Design
Implica<on:
USABLE
ERROR
MESSAGES
EX:
Project
for
a
web
start-­‐up:
We
aZempted
to
use
an
error
to
mo.vate
and
inform
new
users
to
sign
up
for
a
pay
account
before
their
free
trial
use.
Users
reacted
strongly
to
this
nega.ve
message;
many
users
ignored
page
content
and
focused
on
this
element
due
to
its
inherent
nega.vity.
26
26

27. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Nega<vity
Bias
Design
Implica<on:
USABLE
ERROR
MESSAGES
EX:
Project
for
a
web
start-­‐up:
Based
on
user
tests,
we
changed
the
error
message
to
a
posi.vely-­‐framed
informa.ve
alert
with
links
to
next
steps.
This
approach
prevented
users’
nega.vity
bias
from
taking
over,
giving
the
page
a
more
balanced
depth
of
focus.
27
27

28. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Dis<nc<on
Bias
"Dis.nc.on
bias:
Mispredic.on
and
mischoice
due
to
joint
evalua.on."
(Hsee,
C.K.,
&
Zhang,
J.,
2004).
The
simultaneous
evalua<on
of
op<ons
makes
them
seem
less
similar,
when
compared
to
independent
evalua<on
of
the
same
op<ons.
In
other
words,
people
no<ce
more
differences
between
op<ons
presented
together.
28
28

29. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Contrast
Effect
“Phantom
Choices:
The
Effects
of
Unavailable
Alterna.ves
on
Decision
Making,"
(Farquhar
and
Pratkanis,
1987).
The
tendency
to
exaggerate
our
percep<on
or
cogni<on
of
an
element
in
the
opposite
direc<on
of
an
adjacent
element
on
a
specific
dimension.
In
other
words,
a
house
looks
bigger
when
it’s
placed
beside
a
smaller
one.
29
29

30. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Dis<nc<on
Bias
/
Contrast
Effect
Design
Implica<on:
NAVIGATION
IS
JUXTAPOSITION
Naviga.on
inherently
places
op.ons
in
a
context
where
human
users
will
tend
to
exaggerate
the
differences
between
them.
Designing
labels
and
naviga.onal
structure
will
tend
to
elicit
compara.ve
generaliza.ons.
We
must
take
this
into
account
and
design
with
compara.ve
user
strategies
in
mind!
30
30

31. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Dis<nc<on
Bias
/
Contrast
Effect
Design
Implica<on:
NAVIGATION
IS
JUXTAPOSITION
EX:
A
leading
sta<s<cal
organiza<on’s
digital
archive:
Naviga.on
labels
were
tested
me.culously
with
a
three-­‐pronged
methodology.
1. Test
moderator
asked
them
to
predict
what
was
behind
each
label
in
the
naviga.on.
2. Spontaneous
qualita.ve
comments
pertaining
to
naviga.on
and
organiza.on.
3. Scenario
scores
for
each
label
were
calculated
to
determine
labels’
success
rate.
31
31

32. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Dis<nc<on
Bias
/
Contrast
Effect
Design
Implica<on:
NAVIGATION
IS
JUXTAPOSITION
EX:
A
leading
sta<s<cal
organiza<on’s
digital
archive:
Good
labels
were
not
only
clear
themselves,
but
they
were
unambiguous.
Bad
labels
elicited
user
commentary
about
their
similarity
to
other
labels.
32
32

33. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Dis<nc<on
Bias
/
Contrast
Effect
Design
Implica<on:
NAVIGATION
IS
JUXTAPOSITION
EX:
A
leading
sta<s<cal
organiza<on’s
digital
archive:
Parallel
example:
where
would
you
go
to
learn
technology
user
demographics?
-­‐ “Home”
-­‐ “Specific
Topics
in
Technology”
-­‐ “Member
Services”
-­‐ “About
the
Organiza.on”
33
33

34. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Dis<nc<on
Bias
/
Contrast
Effect
Design
Implica<on:
NAVIGATION
IS
JUXTAPOSITION
EX:
A
leading
sta<s<cal
organiza<on’s
digital
archive:
Parallel
example:
where
would
you
go
to
learn
technology
user
demographics?
-­‐ “Home”
-­‐ “Specific
Topics
in
Technology”
-­‐ “Data
and
Sta<s<cs”
Each
op<on
changes
-­‐ “Member
Services”
interpreta<on
of
the
others!
-­‐ “About
the
Organiza.on”
34
34

35. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Commitment
Bias
“Knee-­‐deep
in
the
Big
Muddy:
A
Study
of
Escala.ng
Commitment
to
a
Chosen
Course
of
Ac.on"
(Staw,
B.M.,
1976).
People
tend
to
make
irra<onal
decisions
which
align
with
past
decisions.
Behaviour
appears
to
tend
toward
con<nued
jus<fica<on
of
previous
ac<ons,
and
away
from
admijng
a
previous
ac<on
was
wrong.
35
35

36. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Commitment
Bias
Design
Implica<on:
USERS
ARE
LESS
LIKELY
TO
BACKPEDAL
Commitment
bias
shows
us
that
users
will
most
likely
con.nue
as
if
their
ini.al
ac.on
was
correct
with
respect
to
their
goals.
We
see
that
good
UX
keeps
a
sense
of
forward
mo.on
(even
in
the
process
of
correc.ng
mistakes).
36
36

37. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Commitment
Bias
Design
Implica<on:
USERS
ARE
LESS
LIKELY
TO
BACKPEDAL
EX:
Ethnographic
User
Research
on
LexisNexis’
QuickLaw:
Open
ended
research
revealed
a
large
variety
of
issues
with
lawyers’
interac.on
with
the
system.
The
two
most
prominent
findings
involved
problems
which
related
to
a
lack
of
con.nued
forward
mo.on.
37
37

38. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Commitment
Bias
Design
Implica<on:
USERS
ARE
LESS
LIKELY
TO
BACKPEDAL
EX:
Ethnographic
User
Research
on
LexisNexis’
QuickLaw:
Cri<cal
Finding:
users
frustrated
with
back-­‐and-­‐forth
mo.on
between
ini.al
search
screen,
search
results,
and
individual
ar.cles.
Corrobora<on:
proposed
design
concepts
which
reduced
back-­‐and-­‐forth
were
the
most
favoured
by
users.
38
38

39. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Commitment
Bias
Design
Implica<on:
USERS
ARE
LESS
LIKELY
TO
BACKPEDAL
EX:
Ethnographic
User
Research
on
LexisNexis’
QuickLaw:
39
39

40. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Informa<on
Bias
“Thinking
and
Deciding"
(Baron,
J.,
1988,
1994,
2000).
We
tend
to
place
extra
emphasis
on
informa<on,
even
when
it
is
not
per<nent
to
our
goal.
Human
curiosity
and
confusion
of
goals
compels
us
to
gather
extra
informa<on
even
when
it
is
irrelevant
to
our
decision.
40
40

41. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Informa<on
Bias
Design
Implica<on:
SUPERFLUOUS
INFO
WILL
BE
SOUGHT
Users
will
tend
to
gather
extra
informa.on
before
making
decisions
to
proceed
on
an
interface.
Balancing
the
right
amount
of
content
is
important.
Extra
informa.on
will
reduce
the
efficiency
of
the
interface,
as
users
will
choose
to
pursue
it
even
if
not
needed.
41
41

42. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Informa<on
Bias
Design
Implica<on:
SUPERFLUOUS
INFO
WILL
BE
SOUGHT
EX:
Website
Conversion
Best
Prac<ces
Web
usability
and
conversion
specialists
tell
us
to
remove
distrac.ons
from
key
conversion
pages
(Sage,
b2bento,
Jakob
Nielsen,
SEOp.mize,
Dis.lled).
Your
users
will
look
up
that
addi.onal
informa.on,
slowing
down
their
progress.
Think
before
placing
addi.onal
unnecessary
content.
“It
can’t
hurt”
mentality
=
false.
42
42

43. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Informa<on
Bias
Design
Implica<on:
SUPERFLUOUS
INFO
WILL
BE
SOUGHT
EX:
Website
Conversion
Best
Prac<ces
(Amazon.com)
43
43

44. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Informa<on
Bias
Design
Implica<on:
SUPERFLUOUS
INFO
WILL
BE
SOUGHT
EX:
Website
Conversion
Best
Prac<ces
(Amazon.com)
When
purchasing,
categories
dissappear!
44
44

45. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
These
same
biases
also
have
logis<cal
implica<ons
toward
UX
prac<ce!
45
45

46. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Nega<vity
Bias
Logis<cal
Implica<on:
HOLISTIC
APPROACH
TO
CUSTOMER
EXP.
Nega.ve
experiences
take
precedence,
so
no
maZer
how
good
95%
of
the
customer
experience
is,
they
will
focus
on
the
nega.ve
5%.
This
bias
strengthens
the
argument
that
compe..ve
businesses
must
focus
on
designing
a
holis.c,
mul.-­‐plaworm
customer
experience
(from
kiosk
to
call
centre
to
website).
46
46

47. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Dis<nc<on
Bias
/
Contrast
Effect
Logis<cal
Implica<on:
SIMULTANEOUS
AND
PARALLEL
DESIGNS
Presen.ng
parallel
designs
simultaneously
highlights
differences.
Especially
with
low
fidelity
wireframes...
non-­‐designers
need
help
seeing
the
differences
without
colour
and
completeness.
47
47

48. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Commitment
Bias
Logis<cal
Implica<on:
MILESTONES
INCLUDING
WHOLE
TEAM
Produc.vity
will
increase
and
conflict
will
decrease
if
team
members
believe
they’ve
had
a
part
in
major
milestones,
commiyng
to
the
project’s
direc.on
so
far.
48
48

49. Applying
specific
cogni.ve
biases
to
USER
EXPERIENCE
PRACTICE
Informa<on
Bias
Logis<cal
Implica<on:
KEEP
MILESTONES
SPECIFIC
/
FOCUSED
UX
design
typically
works
in
stages.
Milestone
mee.ngs
/
documents
lead
to
cri.cal
decisions
which
will
decide
the
fate
of
a
design
project
and
overall
user
experience.
Focus
on
specific
elements
to
be
discussed;
extra
informa.on,
assump.ons,
predic.ons
and
future
plans
will
lead
discussion
and
progress
off
track.
49
49

50. If you only remember one thing...
Remember
This
50

51. Humans
are
not
LOGICAL
COMPUTERS
We’ve
seen
a
few
key
cogni<ve
biases,
with
examples
of
how
they
apply
directly
to
UX.
But
this
is
the
Google
Age!
You
don’t
need
to
memorize
them.
51
51

52. Persuasive
Design
Beyond
today’s
examples…
• Confirma<on
Bias
–
tend
to
gather
facts
which
confirm
our
exis.ng
beliefs.
• Op<mism
Bias
–
wishful
thinking,
posi.ve
view
• Alterna<ve
Effects
–
adding
op.ons
has
dras.c
psychological
effects
(dominance,
choice
under
conflict,
etc.)
• Choice-­‐suppor<ve
Bias
–
distort
our
past
choices
to
seem
more
aZrac.ve
• Repe<<on
Bias
–
believe
what
we’ve
heard
repeated
by
the
most
sources
• Anchor
Bias
–
build
a
first
impression
and
then
adjust
based
on
later
info
• Group
Think
–
peer
pressure
and
social
conformity
• Illusion
of
Control
–
tend
to
think
we
have
more
control
than
we
do
• Loss
Aversion
–
tend
to
avoid
loss
stronger
than
we
pursue
gain
• Aeribu<on
Asymmetry
–
aZribute
our
success
to
ability,
our
failure
to
chance
and
situa.on
(vice
versa
for
others’
success/failure)
52
52
52

53. Humans
are
not
LOGICAL
COMPUTERS
All
you
need
to
remember
is:
-­‐ Term:
“cogni<ve
bias”
-­‐
so
you
can
Google
it
yourself.
-­‐ Idea:
cogni.ve
biases
can
help
us
predict
irra.onal
human
behaviour.
-­‐ Thought
process:
applying
cogni.ve
bias
to
UX
strategy
and
design.
-­‐ Thought
process:
use
of
cogni.ve
bias
to
jus.fy
UX
prac.ce.
53
53

54. Humans
are
not
LOGICAL
COMPUTERS
Next
.me
you’re
planning
a
project,
explaining
to
clients,
evangelizing
UX,
designing
an
interface,
analyzing
user
research,
planning
usability
tests,
etc.
…
Remember
that
“cogni<ve
biases”
are
an
easy
way
to
strengthen
your
approach
with
psychology!
54
54

55. Thank
you!
Jay
Vidyarthi
User
Experience
Designer
Research
Coordinator
Ques<ons?
jay@yucentrik.ca
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55