Primera mitad del s XX La Conferencia y los arqueólogos extranjeros.

4.2

The   novelty   model   is   quite   complicated.   It   is   an   abstract   architecture   existing   of   three   different   mediums   and   four   novelty   anchors.   Novelty   anchors   are   abstract   constructions,  like  meta-­‐systems  they  become  specific  when  considering  an  actual   implementation   of   the   novelty   model.   The   essence   of   the   novelty   anchors   is   that   they   allow   agency   to   arise.   The   four   different   anchors   can   together   create   the   bootstrapping   for   the   novelty   model   to   overcome   a   conservation   constraint.   The   case   of   sailing   upwind   should   introduce   the   mechanism   of   novelty   regulation   in   a   fluid-­‐based  medium,  where  the  anchors  are  physical  forces.    

Consider   that   in   a   fluid-­‐based   medium   the   artifacts   were   limited.   So   the   novelty   regulation  in  a  fluid-­‐based  medium  is  limited  as  well.  The  sailing  upwind,  examined   in  the  first  subsection  only  creates  novelty  in  respect  to  the  movement.  Because  of   the   primitive   nature,   the   case   can   demonstrate   the   fundamentals   of   novelty   regulation.  All  other  subsections  focus  on  the  novelty  model  that  I  call  "Cohering".   The  Cohering  model  can  show  how  knowledge  can  emerge.  In  this  case,  there  is  no   limitation  to  the  agency  and  so  with  the   Cohering  model  we  build  an  argument  of   how  the  novelty  model  is  essential  for  an  agent.  

Sailing   upwind   overcomes   the   paradox   of   momentum   conservation.   The   Cohering   model   overcomes   the   bias   of   knowledge.   The   bias   of   knowledge   is   also   a   conservation  constraint.  Knowledge  is  both  a  barrier  and  the  source  for  knowledge   creation,   similar   to   how   momentum   is   both   a   barrier   and   the   source   of   sailing   upwind.   This   is   the   essence   of   the   novelty   regulation:   the   ability   to   overcome   a  

seemingly  unbreakable  barrier.  In  following  chapters,  other  novelty  regulations  are  

examined,   while,   depending   on   the   implementation   of   the   medium   and   anchors,   some  kind  of  conservation  gets  tackled.  The  novelty  regulations  for  these  chapters   all  relate  to  innovation,  they  are  more  complicated  to  understand  and  therefore  the   Cohering  model  seems  most  fit  to  demonstrate  the  general  novelty  model.    

For   the   Cohering   model,   the   mediums   are   memories   and   the   anchors   are   anticipation   processes.   The   second   subsection   creates   an   argument   for   the   three   involved  memories.  The  third  subsection  introduces  the  anticipation  process,  as  the   building  block  for  the  novelty  regulation  process.  The  following  subsections  create   an   extension   of   a   basic   process   to   build   the   novelty   model.   The   internalizing   and   externalizing   processes   create   a   default   flow,   which   was   demonstrated   in   Section   2.2.4   by   the   perception   of   a   face.   The   example   is   also   going   to   be   used   to   demonstrate   a   more   formal   notation.   The   default   flow   uses   default   categories   for   associations   and   tags.   It   demonstrates   the   default   bootstrapping   cascade   and   involves  the  three  mediums.  This  default  flow  has  to  be  extended  twice  to  get  to  an   actual  novelty  model.    

The   first   extension   changes   the   default   flow   to   a   directional   flow   by   adding   challenges.   Challenges   change   the   default   categories   to   context   dependent   categories,   which   allows   control   by   focusing   on   the   context.   Just   like   associations  

and   tags,   the   challenges   have   to   get   anticipated.   The   anticipation   can   lead   to   a   cascade  of  different  associations  and  tags  that  may  lead  to  reinforcing  or  falsifying   the  anticipated  challenge.  The  challenge  creates  the  required  tension  for  novelty  to   emerge.  

 The   last   process   is   for   the   novelty   to   evolve.   It   changes   the   directional   flows   to   a   constructive   flow.   With   the   Cohering   model,   the   evolving   process   works   on   experience   in   the   workspace.   The   evolving   process   is   anticipating   what   aspects   in   the   workspace   are   relevant   and   creates   conceptual   networks   of   the   novelty.   The   directing   and   evolving   process   are   part   of   the   regulation   and   they   can   focus   on   internalizing  or  externalizing.  By  focusing  on  internalizing,  the  novelty  regulation  is   modeling   experiences   into   knowledge.   By   focusing   on   externalizing   the   novelty   regulation  is  mastering  the  novelty.  It  creates,  metaphorically,  the  same  oscillating   movement   as   seen   with   the   sailing   against   the   wind.   The   modeling   and   mastering   are  examined  in  the  next  section  on  learning  novelty.    

Novelty  regulation:  the  case  of  sailing  upwind  

4.2.1

The  source  for  sailing  is  the  wind  working  on  the  sail.  There  are  actually  two  forces   on  the  sail:  drag  and  lift.  The  drag  force  is  the  commonly  known  force.  It  pushes  the   boat  along  in  the  direction  of  the  wind.  Drag  is  not  useful  for  sailing  against  the  wind.   The   lift   force   is   more   difficult   to   understand.   The   basic   principle   of   lift   force   is   to   create   a   curved   surface   so   that   a   flow   (the   wind   in   this   case)   would   pass   with   different  speeds,  creating  a  difference  in  pressure.  The  effect  of  the  difference  is  a   force   in   a   straight   angle   to   the   sail.   The   lift   force   is   not   only   important   for   sailing   against  the  wind.  It  is  the  same  force  that  keeps  airplanes  in  the  sky  and  makes  our   modern  wind  turbines  work.    

The   lift,   acting   on   the   sail,   is   not   enough   to   move   against   the   wind.   At   best,   there   would  be  almost  no  drag  force  and  only  the  lift  force,  resulting  in  an  angle  close  to   90°  opposite  to  the  direction  of  the  wind.  A  price  needs  to  be  paid  to  move  in  the   direction  of  the  wind.  This  requires  a  mechanism  existing  of  some  extra  forces.  The   mechanism  involves  the  viscosity  of  the  water  and  the  specific  position  of  the  keel.   Viscosity  is  a  measure  of  the  resistance  against  a  deforming  of  the  fluid.  Boats  are   designed  to  use  viscosity  to  their  advantage.  The  keel  of  the  boat  is  a  counter  weight   at  the  bottom  of  the  boat  that  often  looks  like  a  long  blade.  It  makes  sure  that  the   boat   can   easily   cut   through   water   along   the   length   of   the   boat,   while   at   the   same   time  restricting  sideway  movements.  By  positioning  the  boat  in  a  45°  angle  with  the   wind,  a  reaction  force  occurs  on  the  keel  that  is  optimal  to  move  against  the  wind.     The  reaction  force  and  the  lift  force  create  a  tension.  The  combined  force  is  smaller   (the  price  paid)  and  goes  into  the  direction  of  the  wind  (forces  A,  B,  C  in  Figure  4.10).   One  fascinating  conclusion  is  that  without  tension,  no  novelty  can  exist.  It  gives  part   of  the  friction  experienced  in  innovation  a  very  different  meaning.  Consider  that  the   medium   defines   what   novelty   can   arise.   In   this   case,   the   novelty   related   to   the   movement.  According  to  the  law  of  conservation  of  momentum,  it  is  paradoxical  to  

move  against  the  wind.  Thanks  to  the  described  mechanism,  it  does  become  possible,   which  requires  a  saw-­‐like  path  of  movement  (from  A  over  B  to  C  in  Figure  4.10).  The   same  reasoning  applies  to  other  novelty  regulations  showing  how  bootstrapping  can   overcome  some  conservation.  The  following  subsections  describe  the  novelty  model   around  knowledge  creation  called  Cohering.  In  this  case,  knowledge  creates  bias  and   fundamental   new   knowledge   can   only   emerge   by   the   saw-­‐like   path   between   mastering,  meaning  actions  to  refine  the  regulation  and  modeling,  meaning  actions   to  refine  the  internal  representation.  

Figure  4.10  How  sailing  against  the  wind  can  be  achieved    

  After  examining  the  Cohering  model  in  detail,  Subsection  4.4.1  returns  to  the  sailing   against   the   wind   case.   Because   fluid-­‐based   mediums   do   not   store   memory,   it   becomes   a   fascinating   case   to   generalize   novelty   regulation   as   a   mechanism   to   overcome   conservations   and   deal   with   complex   adaptive   environments.   It   creates   the   proper   context   for   the   following   chapters   where   different   domains   have   approached  complex  adaptive  environments  and  develop  at  least  part  of  the  novelty   model  for  the  conservation  they  get  challenged  by.  

Three  types  of  memory  

4.2.2

The   mind   needs   a   memory   to   work.   An   extended   mind   makes   it   complicated,   as   memory   does   not   restrict   itself   to   the   brain   but   extends   to   workspaces   like   note   books,   blackboards,   kitchen   tablets,   desks,   online   spaces,   etc.   In   other   words,   the   environment  is  a  memory  too.  A  workspace  is  needed  to  create  information,  which   is  a  third  required  memory.  As  argued  before,  for  a  cognitive  system  only  space  is   needed  to  make  an  external  environment  become  a  workspace.  For  example,  Clark   illustrates  how  space  distribution  in  the  kitchen  contributes  to  problem  solving,  like   separating   washed   from   unwashed   vegetables.   (Clark   2008,   p   46)     makes   an   important  contribution  to  our  investigation:  

It   is   intuitive   that   once   descriptive   complexity   is   reduced,   processes   of   selective   attention,   and   of   action   control,   can   operate   on   elements   of   a   scene   that   were   previously   too   “unmarked”  to  define  such  operations  over.  Experience  with  tags  and  labels  may  be  a  cheap   way  of  achieving  a  similar  result.  

Tags  are  the  element  regulating  the  external  anticipation  process  in  the  default  flow.   The  last  remark  by  Clark  "tags  are  cheap"  needs  some  nuance.  It  seems  to  indicate   that  spatial  distribution  would  be  more  expensive  than  labeling.  Spatial  distribution  

is   less   robust,   but   a   label   requires   a   culture   to   make   sense   of   the   label,   the   production   of   paper   and   markers   for   the   labeling   itself.   Once   the   environmental   enrichment   has   built   tagging,   it   can   indeed   become   cheaper,   but   not   by   default.   Workspaces   are   mostly   overloaded   with   tags,   at   least   for   the   trained   eye.   An   untrained  eye  can  show  remarkable  blindness  to  such  tags,  as  it  is  usually  not  the   intention  to  attract  the  untrained  eye's  attention.  

The  environment  is  its  own  best  representation.  For  example,  it  is  hard  to  memorize   spatial  aspects  exactly,  like  colors,  because  it  is  so  easy  to  perceive  the  color.  Having   a   general   description   is   often   enough.   Even   for   more   abstract   aspects,   the   environment   can   be   a   proper   space   to   store   knowledge.   This   can   include   creating   maps  or  writing  books.  One  misconception  is  that  retrieving  information  from  the   environment  would  be  slow.  (Clark  2008,  p  380)  mentions  Gray  and  Fu  (2004)  to   prove  this  is  wrong:  

Instead,  they  [Gray  and  Fu]  argue  that  their  results  show  that  “the  time  retrieving  something   from  memory  is  weighed  the  same  as  time  spend  in  perceptual-­‐motor  activity”  and  that  it  is   therefore  a  mistake  to  “presume  the  privileged  status  of  any  location  or  type  of  operation”   (Gray  and  Fu  2004,  p  378,  p  380).      

In   summary,   three   types   of   mediums   are   essential   for   the   novelty   model.   In   the   Cohering  model  it  becomes  three  type  of  memories:  

• System Core = Long-Term Memory: the memory contains knowledge. Knowledge is experience

processed to abstractions and ready to use concepts.

• Workspace = Working Memory: the temporal memory that creates tension and focus, allowing

novelty to emerge. The workspace is the centerpiece in the novelty model.

• Environment = external memory: tags, signs and meaning in the environment. Some are created by

the agent, other are serendipitous.

Anticipation  as  a  building  block  

4.2.3

The   anticipation   process   is   considered   the   cognitive   building   block   of   the   novelty   regulation   process.   Anticipation   works   on   an   information   flow   with   feedback   and   feedforward.  To  provide  additional  detail  of  what  is  happening  during  an  event  (t),  I   consider  a  time  moment  just  in  front  of  the  event  (t-­‐)  and  a  moment  just  after  the   event   (t+).     At   t-­‐   the   feedforward   suggests   what   may   happen,   at   t+   the   feedback   contains  what  has  happened.  The  moment  that  things  are  happening  is  when  output   is  created.  The  t-­‐  and  t+  are  relevant  to  understand  the  anticipation  process.  Figure   4.11   illustrates   two   ways   to   present   an   anticipation   process.   The   left   side   of   the   figure   illustrates   the   information   flow   from   input   to   output.   On   t-­‐   input   to   the   feedforward  process  predicts  an  outcome.  After  the  output,  t+  feedback  updates  the   long-­‐term   memory   used   by   the   anticipation   process.   The   right   side   of   Figure   4.9   shows   an   alternative   presentation   by   combining   input   and   output   to   the   same   workspace.   The   benefit   of   this   figure   is   that   multiple   anticipation   processes   can   work  in  parallel  on  the  working  memory,  which  is  required  for  the  novelty  model.     Figure   4.11   Two   presentations   of   anticipation:   left   side   focuses   on   the   flow,   the   right   side   on   the  

An   event   progresses   in   time,   which   can   be   approximated   by   a   discrete   number   of   steps  (t1,  t2,  t3,  …).  Each  step  has  a  feedforward  t-­‐  and  feedback  t+.  In  real  situations,  

the  t1+  feedback  and  t2-­‐  feedforward  would  overlap.  So  t2-­‐  may  still  be  working  on  

the  incorrect  memory.  To  simplify  the  process  for  the  theory,  consider  small  enough   steps,   so   no   overlap   would   exist   :   t1-­‐   <   t1+   <   t2-­‐   <   t2+.   The   pattern   used   in   the  

anticipation  process  will  be  presented  as  a  set  of  variables  X  =  {x1,  x2,  x3,…,  xn}.    The  

variables   can   be   presented   as   numbers.   It   is,   however,   essential   that   we   consider   sets,  as  networks  of  associations,  tags,  challenges  and  experience  are  in  some  way   connected  and  the  connections  allow  for  the  novelty  regulation  to  manifest.      

The   anticipation   function   '   ϒ   '   predicts   what   X   would   be.   We   can   use   t-­‐   and   t+   to   describe   when   an   anticipation   is   incorrect:   ϒ(Xt-­‐)   ≠   Xt+.    Delta-­‐Learning   in   neural  

networks   (Jacobs   1988)   uses   the   difference   between   target   output   and   the   actual   output  to  calculate  a  delta:  Δw  =  (target  –  actual)  *  neural_activation.  For  the  novelty   regulation,  a  similar  delta  is  expressed  as  the  symmetric  difference:  

X  ΔY  =  (X∪  Y)  \  (X  ∩  Y)      

The   symmetric   difference   contains   the   novelty.   It   is   the   set   of   what   was   expected   and  did  not  manifest  (wrong  knowledge)  and  what  manifests  that  was  not  expected   (unknown  observation).  In  case  what  was  expected  and  what  manifest  are  the  same   we  get  an  empty  set.  This  delta  can  be  used  with  the  anticipation  function:  

ΔN  (X)  =  Xt+  Δ  ϒ(Xt-­‐)  =  (Xt+∪  ϒ(Xt-­‐))  \  (Xt+  ∩  ϒ(Xt-­‐))  

When  an  anticipation  is  correct  we  get  ΔN  (X)  =  Ø,  if  not  we  get  ΔN  (X)  =  Y,  where  Y  is  

the   set   of   differences   that   need   to   be   understood.   To   express   this,   consider   two   experiences   at   a   different   time   around   the   same   set   of   events:   .   For   example,   pouring   out   some   liquid   in   a   glass   can   be   an   event.   By   pouring   out   different  liquids  and  by  using  different  glasses  a  more  general  conceptualization  of   the  event  is  reached  by  the  different  experiences.  

A  correction  has  occurred  if  ΔN(exp1)  ≠  Ø  and  ΔN(exp2)  =  Ø.  The  correction  does  not  

directly  mean  novelty,  maybe  exp1  was  a  miscalculation,  maybe  the  anomaly  did  not  

occur   any   more   at   exp2.  To   deal   with   such   perturbations,   it   is   essential   that   many  

experiences   exist   of   a   particular   event.   The   event   would   be   incomprehensible   if:  

ΔN(expi)  ≠  Ø,   .  

Default  flow  

4.2.4

The   default   flow   is   the   bootstrapping   cascade   between   associations   and   tags   as   illustrated   with   the   perception   of   a   face   (see   Section   2.2.4).   The   associations   and   tags  are  specific  to  the  Cohering  model.  In  general,  the  associations  become  internal   matches,   while   the   tags   become   external   matches.   The   basic   anticipation   design   (right   side   of   Figure   4.11)   only   has   a   working   memory(the   workspace).   With   the   default  flow,  the  basic  anticipation  design  is  extended,  relating  to  the  System  Core   (the   Long-­‐Term   Memory)   via   an   internalizing   anticipation   process   and   the   environment  via  an  externalizing  anticipation  process.  Notice  that  in  contrast  to  the   basic   anticipation   design,   this   design   does   not   contain   a   correction,   which   is   reinstated  with  the  constructive  flow.  The  default  flow  does  use  all  three  mediums   of  the  novelty  model  and  allows  regulation,  if  knowledge  exists.    

Figure  4.12  Internalizing  and  externalizing  creating  a  default  flow  

The  memory  element  X  now  gets  a  dimension  related  to  the  four  processes  involved:   internalizing   (in),   externalizing   (ex),   directing   (di)   and   evolving   (ev).   With   the   default  flow,  the  memory  element  becomes:  X  =  {xin1,  xin2,  xin3,…,  xinn,  xex1,  xex2,  xex3,  …,   xexn'},   where   there   are   n   associations   and   n'   tags.   The   information   flow   from   the   design  that  is  defined  as  a  specific  anticipation  becomes  ϒα  _{and  works  on  X}α  _{=  {xα1,}   xα2,   xα3,   …,   xαn}  ∀𝛼   ∈ 𝑁𝑜𝑣𝐷𝑖𝑚.  𝑁𝑜𝑣𝐷𝑖𝑚  is   short   for   the   four   dimensions   that   the   novelty   can   have   (in,   ex,   di,   ev).   The   anticipation   processes   are   parallel   processes   that  have  no  overlap  of  values:  Xα  _{∩  X}β  _{=  Ø  for  α  ≠  β  and  ∀𝛼, 𝛽   ∈ 𝑁𝑜𝑣𝐷𝑖𝑚}  

To   illustrate   the   default   flow,   I   reconsider   the   Gestalt   bootstrapping   example   for   perceiving  a  face  (see  Figure  3.8).  This  example  has  some  simplification  that  I  undo   in  the  later  formalization,  but  it  seems  best  to  introduce  the  notion  in  a  simplified   way.  The  example  demonstrates  how  anticipation  happens  with  intermediate  stages   before  a  stable  pattern  would  emerge:  ϒ(X)  =  X',  meaning  that  the  X  was  get  refined  

to  X',  in  that  case  X  was  a  more  simplified  description  than  X'.  Many  steps  may  be   required  before  a  useful  description  is  reached:  ϒ(X')  =  X'',  …,  ϒ(Xn-­‐1_{)  =  X}n  _{In  our}   face  recognition  example,  only  a  few  steps  existed:    ϒe  _{first  finds  Xround,  became  more}  

concrete  with  Xface  to  finally  fix  on  XAlice.  To  indicate  that  intermediate  steps  exist,  I  

use  the  ϒ(X)  →  Xn  _{as  a  simplified  notation.}    

The  face  recognition  demonstrated  how  two  anticipation  processes  can  reach  what   each  cannot  do  separately,  starting  from  nothing  it  recognizes  the  face:  ϒ(Ø)  →  Xface   while   ϒα   _{(Ø)  Xface∀𝛼   ∈ 𝑁𝑜𝑣𝐷𝑖𝑚.   In   other   words,   coordination   between   the}  

anticipation  processes  is  needed.  The  initial  step  in  the  figure  is  ϒex_{(Ø)  =  Xshape  and}  

ϒin_{(Ø)  =  Ø.  The  externalizing  gets  light  as  input  and  recognizes  a  shade,  resulting  in}   Xshape   =   {  𝑥_!"#$%$"&!" _!,   𝑥_!"#$%$"&!" _!,   …}.   Xshape   only   contains   concrete   tags   and   no  

associations.   Internalizing   only   works   if   there   is   some   concept   to   also   start   associating.  Shapes  are  recognized  in  milliseconds  resulting  into  ϒe_{(Xshape)  =  Xround.}   Round  is  a  concept  that  triggers  associations:  ϒin_{(Xround)  =  {  xinball,  xinapple,  xinface}.  In}   the   meantime,   the   external   is   too   complicated   and   no   information   is   gained:  

ϒex_{(Xround)   =   Xround.   This   local   maximum   is   only   temporal   and   leveraged   by   the}   internal   process.   To   visualize   this,   think   of   the   Gestalt   emerging   where   no   clear   pattern   was   recognized   (Figure   2.15   D   of   a   dog   sniffing   a   tree).   In   the   case   of   the   phase,   the   working   memory   contains   Xround   =   {xinball,   xinapple,   xinface,   …,   𝑥_!"#$%!" _{!"#$!!,  𝑥}

!"#$%!"#$!!

!"  _{,   …},     where   part-­‐1   and   part-­‐2   are   concrete   external   tags}   and  xiface  refers  to  Xface.    

By  retrieving  the  Xface  from  the  Long-­‐Term  Memory,  a  new  set  of  relations  is  added  

to   the   workspace:  Xface   =   {Xnose,   Xupper-­‐lip,   Xleft-­‐eye,   …,   xinfriend-­‐or-­‐foo,   …}.   If   an   object   is   more   complex,   as   with   the   face,   several   steps   are   needed   to   make   the   object   concrete.   In   the   novelty   model,   an   operation   queue   exists.   It   contains   the   first   associations  and  push  new  associations  to  the  back:  Q  =  {Xball,  Xapple,  Xnose,  Xupper-­‐lip  ,   …}.  In  the  next  steps  ϒex_{(Xround)  ≠  Xball  and    ϒ}ex_{(Xround)  ≠  Xapple,  meaning  that  external}   feedback  rejects  the  conceptualization  of  a  ball  or  apple.  While  ϒex_{(Xface)  is  falsifying}  

the   associations,   ϒin   _{would   get   the   next   associations   related   to   round,   maybe}   ϒin_{(Xround)  =  {Xwheel,    Xplate}.  Of  course,  it  becomes  harder  to  find  more  associations  if}  

no  new  verification  comes  from  the  external.  Lucky  ϒex_{(Xround)  =  Xface  is  verified,  so}  

the   next   steps   in   the   bootstrapping   cascade   can   be   explored,   which   leads   to   XAlice.  

Notice   how   both   ϒin_{(Xround)   =   Xface  and  ϒ}ex_{(Xround)   =   Xface     are   needed   to   become}