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(U) 
“...quantification  of  the  informational  content  of  transcripts  on  a   small 
set  of  underlying  semantic  dimensions  which  might  serve  as  basis  vectors 
for  the  viewer's  internal  representation  of  the  target.  If  such  basis 
vectors  can  be  found,  complex  constructs  in  the  viewing  data  might  be 
assembled  by  combining  sets  of  data  so  expressed.”9 
(U)  Using  well-established  techniques  from  the  area  of  environmental  psychology 
(which  have  been  used  to  solve  analogous  problems  in  “normal”  perception),  the  proposed 
experiment  will  attempt  to  isolate  underlying  semantic  structures  in  RV  perceptions.  In 
essence,  percipients  will  be  asked  to  remote  view — in  sessions  of  two  targets  each — all  possible 
pairs  of  targets  and  to  estimate  the  similarity  between  targets  in  each  pair.  The  data  will  be 
analyzed  for  important  factors  such  as  intersubject  reliability,  presentation  order  effect,  and 
target  pair  effects.  Multidimensional  scaling  (MDS)  analysis*  will  then  be  applied  to  identify 
the  underlying  semantic  dimensions.  Identification  of  semantic  structures  would  hold 
important  implications  for  descriptor  list  development  and  possibly  for  identification  of 
fundamental  commonalities  of  perception  across  viewers. 
B.  (U)  AI  Techniques 
(U)  According  to  the  following  excerpt  from  the  letter  proposal  submitted  by 
consultant  J.  Vallee,  the  proposed  AI  approach 
“...will  seek  to  build  an  expert  system  for  target  recognition  by  analyzing 
the  process  that  enables  a   human  expert  monitor  to  provide  an 
interpretation  of  a   remote  viewing  session,  or  a  judge  to  match  a   given 
description  with  an  actual  target. 
It  is  expected  that  a   rule-based  expert  system  can  be  developed  in  a 
series  of  iterations  starting  with  the  simple  “twenty  questions”  framework 
already  used  in  the  project.  Later  this  will  lead  to  a   fully-developed 
interactive  model.  We  envision  this  “smart  monitor”  taking  the  analyst 
from  simple  scene  and  “gestalt"  recognition  to  the  detection  of  breaks, 
contradictions  and,  possibly,  analytical  overlays  as  well.”10 
(U)  Assuming  promising  results  with  the  initial  task  of  target  definition,  it  is  anticipated 
that  the  expert  system  will  ultimately  be  expanded  to  play  a   more  active  role  in  operational 
remote  viewing  sessions  through  on-line  capture  of  respondents’  ideograms  and  interactive 
analysis  of  their  features. 
(U)  It  is  beyond  the  scope  of  this  discussion  to  describe  the  details  of  the  MDS  analysis. 
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C.  (U)  In-House  Effort 
(U)  SRI  in-house  approaches  will  focus  on  improvement  and  refinement  of  existing 
technologies.  One  potential  line  of  inquiry  would  focus  on  identifying  an  appropriate  set  of 
abstract  descriptors  to  add  to  the  current  novice  list.  This  effort  would  be  aimed  at  mitigating 
some  of  the  inter— analyst  reliability  problems  that  have  been  encountered  with  novice  data 
(see  Section  III. A.).  An  attempt  would  also  be  made  to  incorporate  additional  concrete 
descriptors  into  advanced  lists. 
(U)  A   second  approach  would  endeavor  to  complete  work  begun  in  FY  1986 — 
namely,  a   reanalysis  of  novice  data  using  fuzzy  mathematical  weighting  factors  with  the  current 
list  of  twenty  descriptors.  The  hypothesis  is  that  the  greater  latitude  afforded  by  fuzzy  set 
membership  values  (as  opposed  to  the  “all  or  nothing”  capability  of  the  descriptors  in  then- 
current  configuration)  will  significantly  decrease  the  granularity  of  the  current  list — i.e.,  will 
allow  capture  of  more  information.  If  the  post  hoc  reanalysis  yields  promising  results,  the 
fuzzy  set  approach  would  be  benchmarked  on  a   blind  basis  against  the  current  binary 
approach. 
(U)  A   third  effort  would  systematically  evaluate  the  efficacy  of  viewer-derived  versus 
analyst-derived  descriptor  lists.  While  viewers  are  sensitive  to  their  internal  perceptions,  they 
are  not  cognizant  of  the  requirements  of  the  analytical  procedures;  conversely,  the  analyst  is 
privy  to  the  linguistic/analytical  aspects  of  descriptor  lists,  but  may  be  unaware  of  how  to 
optimize  a   viewer’s  perceptions  using  descriptors.  The  hypothesis  is  that  the  combined  insights 
of  analyst  and  percipient  will  synergistically  result  in  the  optimal  formulation  of  descriptor  lists. 
One  way  to  test  this  hypothesis  would  be  to  compare  statistics  across  analyst-derived  versus 
percipient-derived  versus  combined  analyst/percipient-derived  descriptor  lists  on  the  same  set 
of  RV  data. 
(U)  A   fourth  approach  would  endeavor  to  take  a   retrospective  look  at  viewers’ 
ideograms  and  their  possible  range  of  meaning.  If,  for  example,  a   viewer  typically  draws  a 
“tic-tac-toe,”  cross-hatch-style  ideogram,  and  post  hoc  analysis  reveals  that  the  drawing 
correctly  corresponds  to  the  presence  of  a   city  in  targets  80  percent  of  the  time,  this 
information  might  be  used  to  assign  an  a   priori  fuzzy  weighting  factor  of  0.8  when  the 
ideogram  is  encountered  on  a  blind  basis.  An  in-depth  examination  of  a   viewer’s  ideograms, 
therefore,  might  result  in  the  development  of  viewer-specific,  prescriptive  guides  for  the 
assignment  of  fuzzy  weighting  factors  in  assessing  RV  responses.  This  type  of  information 
could  also  conceivably  be  automated  and  updated  through  iterative  expert  system  capabilities. 
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(S)  Finally,  work  will  be  initiated  to  develop  mission— specif ic 
descriptor  lists  for  technical  site  applications.  It  is  anticipated  that 
efforts  along  these  lines  will  enable  us  to  better  address  intelligence 
community  requirements. 
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V   CONCLUSIONS  (U) 
(U)  The  FY  1986  evaluation  effort  has  resulted  in  (1)  refinement  and  extension  of 
current  techniques,  and  (2)  identification  of  candidate  new  technologies  for  preliminary 
research. 
(U)  The  mathematical  formalism  for  the  current  evaluation  procedure — the  figure  of 
merit  analysis — is  well  understood  and  stable.  In  addition  to  the  system’s  ability  to  provide  a 
reasonable  assessment  of  remote  viewing  data,  it  has  also  provided  a   mechanism  for  systematic 
examination  of  inter-analyst  reliability  factors. 
(U)  The  descriptor  lists  that  currently  form  the  basis  for  the  figure  of  merit  analysis 
have  been  evaluated  on  a   post  hoc  basis.  Preliminary  observations  indicate  that  lists  designed 
for  novice  responses  require  greater  abstract  descriptor  capability,  whereas  lists  designed  for 
advanced  responses  (i.e.,  higher-quality  data)  require  greater  concrete  descriptor  capability. 
It  is  anticipated  that  fuzzy  set  technology  will  assist  in  formalizing  the  interdependence 
between  abstract  and  concrete  descriptors,  by  providing  a   mathematical  framework  through 
which  basis  vector  descriptors  can  be  combined  to  form  concrete  descriptors. 
(U)  Research  into  new  technologies  for  RV  evaluation  will  begin  in  FY  1987.  One  of 
these  approaches,  the  proposed  “similarity”  experiment,  shows  promise  for  identifying  basis 
vector  descriptors.  A   second  approach,  using  rule-based  expert  systems,  will  explore  a 
different  dimension  by  endeavoring  to  capture  RV  analysts’  expertise  in  codifying  targets. 
Should  this  initial  effort  in  artificial  intelligence  prove  successful,  it  will  be  expanded  to 
address  the  more  difficult  problem  of  response  interpretation. 
(S)  It  is  hoped  that  this  multifaceted  approach  to  the  refinement  of 
RV  evaluation  procedures  will  result  in  increased  capabilities  for 
addressing  the  more  complex  problems  of  mission-oriented,  operational  RV. 
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REFERENCES  (U) 
1.  Puthoff,  H.  E.  and  Targ,  R.,  “A  Perceptual  Channel  for  Information  Transfer  Over 
Kilometer  Distances:  Historical  Perspective  and  Recent  Research,”  Proceedings  of  the 
IEEE,  Vol.  64,  No.  3  (March  1976)  UNCLASSIFIED. 
2.  Targ,  R.,  Puthoff,  H.  E.,  and  May,  E.  C.,  1977  Proceedings  of  the  International 
Conference  of  Cybernetics  and  Society,  pp.  519-529  UNCLASSIFIED. 
3.  May,  E.  C.,  “A  Remote  Viewing  Evaluation  Protocol  (U),”  Final  Report  (revised),  SRI 
Project  4028,  SRI  International,  Menlo  Park,  California  (July  1983)  SECRET. 
4.  May,  E.  C.,  Humphrey,  B.  S.,  and  Mathews,  C.,  “A  Figure  of  Merit  Analysis  for 
Free-Response  Material,”  Proceedings  of  the  28th  Annual  Convention  of  the 
Parapsychological  Association,  pp.  343-354,  Tufts  University,  Medford,  Massachusetts 
(August  1985)  UNCLASSIFIED. 
5.  Fisher,  R.  A.,  “Statistical  Methods  for  Research  Workers,”  Oliver  &   Boyd  (7th  ed.), 
London,  England  (1938)  UNCLASSIFIED. 
6.  Edgington,  E.  S.,  “A  Normal  Curve  Method  for  Combining  Probability  Values  from 
Independent  Experiments,”  Journal  of  Psychology,  Vol.  82,  pp.  85-89  (1972) 
UNCLASSIFIED. 
7.  Cooper,  B.  E.,  “Statistics  for  Experimentalists,”  pp.  219-223,  Pergamon  Press,  Oxford, 
England  (1969)  UNCLASSIFIED. 
8.  Zadeh,  L.  A.,  “Fuzzy  Sets  versus  Probability,"  Proceedings  of  the  IEEE,  Vol.  68,  No. 
3,  p.  421  (March  1980)  UNCLASSIFIED. 
9.  Spottiswoode,  S.  J.  P.,  “Proposals  for  Experimental  Studies  on  Semantic  Structure  and 
Target  Probability  in  Remote  Viewing,”  Private  Communication  (July  1986) 
UNCLASSIFIED. 
10.  Vallee,  J.,  “Applications  of  Artificial  Intelligence  Techniques  to  Remote  Viewing: 
Building  an  Expert  System  for  Target  Description,”  Private  Communication  (August 
1986)  UNCLASSIFIED. 
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