the psychology of feedback - inspiration for ass-kicking wearables
TRANSCRIPT
THE PSYCHOLOGY OF FEEDBACK
INSPIRATION FOR ASS-KICKING WEARABLES
Action Design NYC
Brian Cugelman, PhD
@cugelman
New York City30 March 2015
AGENDA
• Collecting data (input)
• Data science (processing)
• Interventions (Output)
• Cooking and digital psychology
• Domain 7. Audience Feedback
3
STARTING QUESTION
What feedback can you capture,
that’s useful to your users, that can
inspire them to take positive action?
How can you add feedback-based
persuasive design principles to
boost your impact?
Mind
Body
Relations
Ambience
Collecting data (input)
•Research-based feedback•Manual data capture •Interaction-based data capture•Automated data porting •Sensor-based data capture
Data science (processing)
•Algorithms•Statistics•Correlations
Interventions(Output)
Hardware•Mobile•Wearable devices•Desktop
Software•Applications
DATA CAPTURE BY SENSORS
• A transducer that converts a signal in one form of energy to a signal in another
• Detects changes in quantities and converts them into an output, generally an electrical or optical signal
SENSORS WITH HUNDREDS OF SUB-CATEGORIES
AND THOUSANDS OF OPTIONS
• Thermal, heat, temperature
• Acoustic, sound, vibration
• Electric current, electric potential, magnetic, radio
• Environment, weather, moisture, humidity
• Flow, fluid velocity
• Ionizing radiation, subatomic particles
• Position, angle, displacement, distance, speed, acceleration
• Optical, light, imaging, photon
• Pressure
• Force, density, level
• Proximity, presence
• Chemical
E-HEALTH SENSOR PLATFORM V2.0 (ARDUINO AND RASPBERRY PI)
1. Pulse,
2. Oxygen in blood (SPO2)
3. Airflow (breathing)
4. Body temperature
5. Electrocardiogram (ECG)
6. Glucometer
7. Galvanic skin response (GSR- sweating)
8. Blood pressure (sphygmomanometer)
9. Patient position (accelerometer)
10. Muscle/eletromyographysensor (EMG)
10 different sensors in a dev kit for about $500
PROCESSING DATA
Detecting
• Mind
• Body
• Ambience
• Relations
Challenge
• Making sense of complex
multi-device signals
• Lots of data: 400 MB/day from
a mobile with millions of
records on full throttle
• Accurately detecting events
that matter to people
• Building human centered
algorithms11
OPTIMIZATION: THE MIX OF INGREDIENTS THAT
ACHIEVES THE MOST IMPACT WITH THE LEAST EFFORT
15
Number of persuasive ingredients
Infl
uen
ce p
ote
nti
al
How do you know when you have too few or too many?
Too
fewToo
manyJust right
Cugelman, B., Thelwall, M., & Dawes, P. (2011). Online Interventions for Social Marketing Health
Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors.
Journal of Medical Internet Research, 13(1), e17.
Feedback loop (self-regulation)
18
Compare goal to performance
(reaction)
Behavioral outcome (variable)
Set a goal(reference value)
Barriers & friction
(disturbance)
Receive feedback on performance
(input)
Perform behavior(output)
Carver, C. and M. Scheier (2005). On the structure of behavioral self-regulation. Handbook of self-regulation. M. Boekaerts, P. Pintrich and M. Zeidner. San
Diego, USA, Guilford Press.
Forget the science of attrition a moment. How
long could you realistically stay on this never ending
self-regulation loop, without falling off the
bandwagon.
PSYCHOLOGICAL ARCHITECTURE OF
HEALTH BEHAVIOR CHANGE TECHNOLOGIES
CUGELMAN, B., THELWALL, M., & DAWES, P. (2011) Online interventions for social marketing health behavior change
campaigns: A meta-analysis of psychological architectures and adherence factors. Journal of Medical Internet Research, 13(1),
e17. http://www.jmir.org/2011/1/e17/19
Eff
ect
Siz
e (
d)
Inte
rve
nti
on
s (
%)
Let’s take a realistic
approach to habit
formation with 66 avg
days and most people
abandoning your
technology after just
a few sessions.
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SEQUENTIAL REQUESTS
22
Can you sign this petition?
Can you donate $100?
Would you donate $100?
Ok, how about $10?
[P-702]
SHOPPING CART ABANDONMENT:
DOOR IN THE FACE
23Wang, D. (2014). 13 Amazing Abandoned Cart Emails (And What You Can Learn From Them).
TARGETED, PERSONALIZED, TAILOREDADAPTED FROM KREUTER ET AL. (2000)
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1.
Inp
ut
2. Output
Personalized
Targeted
Tailored
No
feedback
Lots of
feedback
Generic
Interpersonal
Generic Individualized
[P-705]
[P-706]
[P-707]
PERSONALIZATION AND RECOMMENDATION ENGINES
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Targeting [P-705]Tailoring [P-707]Personalization [P-706]
WHY DOES TAILORING WORK
1. Unnecessary information is eliminated
2. The information is personally relevant to the individual
3. People pay more attention to information that is personally relevant
4. People are more likely to act on information that they have pondered in-depth
28
PERSONAL BARRIERS AND FRICTION
31
Goal
Cugelman, B., M. Thelwall, et al. (2011). "Online Interventions for Social Marketing Health Behavior
Change Campaigns: A Meta-Analysis of Psychological Architectures and Adherence Factors." Journal
of Medical Internet Research 13(1): e17.
Drivers
Friction
[P-710]
FEEDBACK ON PERFORMANCE
Cugelman, B., M. Thelwall, et al. (2011). "Online Interventions for Social Marketing Health
Behavior Change Campaigns: A Meta-Analysis of Psychological Architectures and
Adherence Factors." Journal of Medical Internet Research 13(1): e17.
[P-711]
REINFORCEMENT
A reinforcer is anything that occurs with an act
that increases the odds of it happening again.
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Negative reinforcer Positive reinforcer
[P-712]
Something someone wantsSomething someone wants
to avoid
PUNISHING AND REWARDING
WITH DIALOGUE BOXES
Nasty Mac
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FAILURE
You have NOT reached your goal.LOSER!
You’re not yet finished. Try harder. Lazy bugger.
Never degrade, insult, or offend.
For negative feedback, focuses on positive encouragement or loss aversion.
Punishment can undermine users’ motivation, confidence, and boost the odds
that they avoid your technology like a nasty person.
[P-712 b] Nice Mac
SUCCESS
You’ve reached your goal. CONGRATULATION!!!
You’ve finished. Way to go!
[P-712 a]
DON’T CONFUSE
MOTIVATION WITH REINFORCEMENT
Before acting While acting After
Motivation(Incentives / Loss aversion)
Reinforcement(Rewards / Punishments)
Don’t confuse threats of loss aversion with punishment.
Be careful with punishment, and only use it with care.
ENDING QUESTION
What feedback can you capture,
that’s useful to your users, that can
inspire them to take positive action?
How can you add feedback-based
persuasive design principles to
boost your impact?