expoiting cognitive biais - creating ux for the irrational human mind

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Canadian Partner of the UX Alliance

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Mobile IVR Website iTV So6ware

Exploi<ng Cogni<ve Bias: Crea.ng UX for the Irra.onal Human Mind

Jay Vidyarthi

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Understanding “cogni<ve bias”

…an easy way to apply psychology into your day to day work!

The Plan.

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3:: COGNITIVE BIASES HELP PREDICT HUMAN IRRATIONALITY

4:: APPLYING SPECIFIC COGNITIVE BIASES TO USER EXPERIENCE PRACTICE

2:: HUMANS ARE NOT STRICTLY LOGICAL COMPUTERS

1:: USER-‐FACING ELEMENTS DEMAND A PSYCHOLOGICAL APPROACH

Exploi<ng Cogni<ve Bias: Crea.ng UX for the Irra.onal Human Mind

5:: IF YOU ONLY REMEMBER ONE THING, REMEMBER THIS

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User-‐facing elements demand a...

Psychological Approach

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Our users are human beings:

User-‐facing elements demand a PSYCHOLOGICAL APPROACH

Subjec.ve. Cogni.ve. Emo.onal. Expressive. Biased. Inconsistent. Unpredictable. Diverse. Irra<onal!

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Psychology helps us understand how people think…

…many of its findings are directly applicable to user experience.


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A ques.on:

Why do we need Psychology to build computer systems?


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Humans are not strictly...

Logical Computers

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The human mind is constantly…

-‐  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.

Humans are not LOGICAL COMPUTERS

Sound Familiar?

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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.


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It’s not a coincidence.

We’ve created technology to help us accomplish our cogni.ve goals.


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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!)


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Computers are fully ra<onal.

They take our inputs and compute logical, ra.onal, predictable output.

(processing billions of instruc.ons per second)


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The human mind is irra<onal.

It uses not only logic, but also a wide

range of other factors.


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The human mind is irra<onal.

Logical Conclusions + Emo.onal State + Social

Circumstance + Perceptual Biases + etc.

= Decision / AZribu.on / Ac.on


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Technological tools enable humans to perform strict

logical computa<on quickly…

…but our irra<onal minds are in control of these ra.onal tools!


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A good technological interface connects irra<onal minds to logical

computers…


Logical Symbol

Manipulator

Irrational User Interface Tailored

to Irrational Mind

Controls

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

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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.”

COGNITIVE BIASES help predict human irra.onality

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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


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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)


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Applying specific cogni.ve biases to...

User Experience Prac<ce

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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!

Applying specific cogni.ve biases to USER EXPERIENCE PRACTICE

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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.


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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.


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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.


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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.


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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.


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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.


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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!


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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.


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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.


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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”


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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” -‐  “Member Services” -‐  “About the Organiza.on”


Each op<on changes interpreta<on of the others!

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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.


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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).


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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.


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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.


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Commitment Bias

Design Implica<on: USERS ARE LESS LIKELY TO BACKPEDAL EX: Ethnographic User Research on LexisNexis’ QuickLaw:


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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.


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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.


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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.


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Informa<on Bias Design Implica<on: SUPERFLUOUS INFO WILL BE SOUGHT EX: Website Conversion Best Prac<ces (Amazon.com)


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Informa<on Bias Design Implica<on: SUPERFLUOUS INFO WILL BE SOUGHT EX: Website Conversion Best Prac<ces (Amazon.com)


When purchasing,

categories dissappear!

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These same biases also have logis<cal implica<ons toward

UX prac<ce!


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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).


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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.


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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.


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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.


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If you only remember one thing...

Remember This

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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.


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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)

Persuasive Design

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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.


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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!


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Thank you!

Jay Vidyarthi User Experience Designer Research Coordinator

Ques<ons?

[email protected]

expoiting cognitive biais - creating ux for the irrational human mind

Design

facing elements

applying specic

predict human

logical computers

human mind

psychological

ng ideas

drawing conclusions