Expoiting Cognitive Biais - Creating UX for the Irrational Human Mind

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

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

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

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

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

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

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

Humans are not strictly... Logical Computers 8

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Expoiting Cognitive Biais - Creating UX for the Irrational Human Mind

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