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Expoiting Cognitive Biais - Creating UX for the Irrational Human Mind

Expoiting Cognitive Biais - Creating UX for the Irrational Human Mind






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    Expoiting Cognitive Biais - Creating UX for the Irrational Human Mind Expoiting Cognitive Biais - Creating UX for the Irrational Human Mind Presentation Transcript

    • 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
    • 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