00100	.SEC THE CENTRAL PROCESSES OF THE MODEL
00200	
00300	
00400	(THIS CHAPTER REQUIRES MANY FLOW DIAGRAMS- SEE BACK OF MS)
00500	
00600		Only  the  major  processes  will  be  described  in   detail
00700	sufficient   to   illustrate   the   logic  of  the  algorithm.  Many
00800	"housekeeping"  procedures  are  needed  to  run  the  model  but  no
00900	understanding  of them is necessary to follow the main flow of symbol
01000	processing.
01100		To  give  some  examples of housekeeping, the first procedure
01200	executed is one of intiialization  which  checks  to  make  sure  the
01300	data-base  has  been  read in and sets a number of variables to their
01400	starting values.  Some of these variables serve as flags  or  indices
01500	pointing  to  the  topic  under  discussion or to the last self-topic
01600	discussed.  Other variables are set by the interviewer who can choose
01700	to run a weak or strong version of the model.  If the weak version is
01800	elected, affect- variables of ANGER and FEAR can be set to  `low`  or
01900	`mild'  values  while  MISTRUST  can be set to `mild' or `high'.  The
02000	interviewer also has the option of following the internal workings of
02100	the  model  which  can  be displayed in "windows" on a console. After
02200	this initialization the algorithm prints out `Ready' to  indicate  to
02300	the interviewer he may now enter his input expression.
02400	
02500		After the  input  expression  is  assigned  a  sentence  type
02600	(statement,  question  or  imperative),  it  then serves as the input
02700	argument to the major procedures which deal with (in  order)  special
02800	reactions,  delusional references, self references, flare references,
02900	interviewer-interviewee  relations,  miscellaneous  expressions   and
03000	self-scanning.
03100	
03200	.F
03300	SPECIAL REACTIONS
03400	
03500		This procedure  produces  appropriate  reactions  to  special
03600	types  of  input  expressions.    If the input consists simply of the
03700	letter `S' {the means by which an interviewer indicates silence  over
03800	a  teletype),  then  the algorithm chooses a linguistic response from
03900	the Silence list. The linguistic output responses are  not  generated
04000	word-by-word.  They  consist  of  pre-formatted  English  expressions
04100	stored in the data-base on ordered lists.
04200	
04300		The procedure which selects the next reply from the  relevant
04400	response list also removes that response from the list so it will not
04500	be output twice.   Thus, in this  case,  where  repeated  silence  is
04600	being  detected  and  if there are no more responses on the `Exhaust'
04700	list {i.e. the 'Exhaust' list is itself exhausted} , the model  would
04800	end the dialogue.
04900	
05000		An  `Exhaust'  list  represents  a  boundary condition in the
05100	model. That is, since the model has a limited  number  of  linguistic
05200	responses  for  each  topic  it can discuss, when these responses are
05300	exhausted the model must have some way of dealing with a large number
05400	of   conceptually   equivalent   repetitions   on  the  part  of  the
05500	interviewer.  When a response list is exhausted, the model  expresses
05600	a wish to change the topic and, as mentioned, when the `Exhaust' list
05700	itself is exhausted, the model ends the dialogue. Since this  process
05800	is  true of all instances in which the response list is exhausted, it
05900	will not be mentioned again. I trust the reader will remember this is
06000	what happens each time this boundary condition is reached.
06100	  
06200		The second case handled by this procedure consists  of  input
06300	expressions  in  which  the interviewer states or insinuates that the
06400	model is mentally ill.  This condition is detected by  finding  "you"
06500	and  a  nearby  (within three words) member of the `Abnormal' list in
06600	the input.  The inputs:
06700	
06800	.V
06900	     {1}Dr.- "You need treatment."
07000	
07100	     {2}Dr.-"You are delusional."
07200	
07300	     {3} Dr.-"Do you think you might be paranoid?"
07400	.END
07500	
07600	would satisfy this condition.
07700		If  the input is a question, as in {3}, ANGER is increased by
07800	an smaller amount of its current value than if  it  is  a  statement,
07900	Thus  a question is interpreted as an implicit insult compared to the
08000	explicit insult of a direct statement.
08100	
08200	     The linguistic response now chosen is selected from the `Alien'
08300	list, e.g.
08400	
08500	.V
08600	     {4} Pt.- "I think I know what you doctors are up to."
08700	.END CONTINUE
08800	
08900		If conditons for the procedure handling special reactions are
09000	not found to obtain,    the  algorithm  next  attempts  to  recognize
09100	references to delusions.
09200	
09300	.F
09400	DELUSIONAL REFERENCES
09500	
09600		The strong version of the model contains in its  data-base  a
09700	delusional  network  of  beliefs about the Mafia.  The next procedure
09800	called scans the input expression looking for  a  reference  to  this
09900	delusional  network.      As  will  be  seen,  reactions to the first
10000	reference differs  from  reactions  to  subsequent  references.   The
10100	conceptual  contentives  of  the delusional net are classified in the
10200	data-base into `strong' and `ambiguous' terms.  Thus  "murder"  is  a
10300	strong  term  whereas "bug"(as mentioned in chapter 4), is ambiguous.
10400	If delusional terms are detected in the input, a  local  variable  is
10500	set  to  the  list of terms found and the terms are then deleted from
10600	the delusional word list for reasons which will become clear later.
10700	
10800		Two situations in the interview must be distinguished, one in
10900	which a delusional topic occurs for the first time and the second  in
11000	which  some  aspect  of the delusional net is under discussion or has
11100	been under discussion and is now being taken up  again.    Since  the
11200	topic  of  the  Mafia  is  a fearful one, any reference to it for the
11300	first time raises FEAR by an increment much greater than if the topic
11400	has  already been discussed. The concept of `mafia' is represented in
11500	the data-base by a node in a weighted and directed conceptual graph.
11600		((DRAWING  OF  GRAPH  HERE)  Horses  →  Horseracing → Bookies
11700	→Gangsters →  Rackets  →  Mafia  ↑  ↑  Gambling  Police  ↑  ↑↑  Money
11800	Italians)).   The  nodes  in  the graph represent "flare" concepts to
11900	which the model is particularly  sensitive.   Associated  with  these
12000	nodes  are  small  stories  which the model can narrate about each of
12100	them as a theme. Nodes closer to the Mafia node are  weighted  higher
12200	to  represent  the notion that they are of greater concern since they
12300	bear more directly on  the  delusional  network.  If  a  Mafia  topic
12400	appears  for the first time , pointers in the directed graph of flare
12500	concepts must be modified accordingly since the Mafia  node  has  the
12600	highest  weight  in  the  graph.   A  topic  such as "bookies", while
12700	leading eventually to Mafia beliefs, is of much less importance  than
12800	Mafia-topics.   But  if  "bookies"  comes  up  in  the interview, the
12900	algorithm must know  whether  or  not  the  Mafia  has  already  been
13000	discussed.   Also, if an introductory-topic {see pOO} or subtopic was
13100	under discussion  when  reference  to  a  Mafia-topic  is  made,  the
13200	algorithm must unset the introductory-topic indicator.
13300	
13400		Since the model strives to tell its story about the Mafia,  a
13500	flag  is  set  to  indicate  that,  if  the  topic  is changed by the
13600	interviewer, the model will return to this point in its  story  under
13700	appropriate   circumstances,   e.g.   when  the  interviewer  asks  a
13800	non-specific question or requests any information the patient  wishes
13900	to volunteer.
14000	
14100		If the interviewer's input expression contains a reference to
14200	the delusional net, a delusional statement is output.  But which one?
14300	If this is the first time  the  topic  has  come  up,  the  algorithm
14400	outputs  the  first statement of its delusional story.   From then on
14500	the output delusion selected depends on what has been said,  what  is
14600	still  unsaid  and  what  the interviewer has said about the previous
14700	delusional statement.   Thus the most recent delusional statement  is
14800	saved,along with expected anaphoric references, anticipating that the
14900	interviewer may ask a question or make a statement about it.
15000	
15100		One special case must be noted.  If the values of ANGER, FEAR
15200	and/or MISTRUST are extremely high, above a particular threshold, the
15300	program  will  refuse  to discuss Mafia-topics at all since it is too
15400	`upset' to talk about this most sensitive area.
15500	
15600		To make some of these operations more  intelligible,  let  us
15700	consider  interview examples.  Suppose at some point in the interview
15800	the doctor asks a standard first-interview question as follows:
15900	
16000	.V
16100	     {5}Dr. - "Do you ever have the feeling you are being watched?
16200	.END
16300	
16400	If this is the first reference  to  the  delusional  net,  FEAR  will
16500	increase greatly and the linguistic response will be:
16600	
16700	.V
16800		{6} Pt. - "You know, they know me."
16900	.END CONTINUE
17000	
17100	In making this response, the model must expect from the interviewer a
17200	number  of  typical  questions  of  the  WH-type as well as rejoinder
17300	statements.  The use of "they" by the interviewer in his response  to
17400	the  model's  output  is  assumed to be an anaphoric reference to the
17500	"they" the model  is  talking  about.   Although  it  is  likely  the
17600	interviewer  will  react  to the model's output of {6}, the algorithm
17700	must be prepared for the possibility that the interviewer will change
17800	the  topic.   Hence  if  the  interviewer  at  this  point  asks some
17900	non-sequitur question such as:
18000	
18100	.V
18200	     {7} Dr.- "How long have you been in the hospital?"
18300	.END CONTINUE
18400	
18500	the  program recognizes that no reference to the delusional topic has
18600	been made and answers the question just as it would if it were  asked
18700	in  any other context.  This ability to deal with input in a flexible
18800	context-independent  manner  is  important  because   of   the   many
18900	contingencies which can occur in psychiatric dialogues.
19000	
19100		If   the  topic  is  changed  abruptly  in  this  way  by  an
19200	interviewer, the algorithm `remembers' that it has output  its  first
19300	delusional  statement  of  {6}.    When the interviewer makes another
19400	neutral delusional reference, the next `line' of the delusional story
19500	will be output, e.g.
19600	
19700	.V
19800	     {8} Pt.- "The Mafia really know about me."
19900	.END CONTINUE
20000	
20100	The  ability  to  answer  typical WH-and HOW questions depends on how
20200	much conceptual information is contained  in  the  delusional  belief
20300	being addressed.  For example, suppose the model replied as in {6}
20400	
20500	.V
20600	     {6) Pt. - "They know about me."
20700	.END CONTINUE
20800	
20900	and the interviewer then asked:
21000	
21100	.V
21200	     {9}Dr.- "Where do they know about you?"
21300	.END CONTINUE
21400	
21500	If the expectancy-anaphoras contain no "where", then a question about
21600	location   cannot  be  answered.   In  this  default  situation,  the
21700	algorithm recognizes the anaphoric "they","know" and "you". Hence  it
21800	knows  at least that the topic has not been changed so it outputs the
21900	next statement in the delusional story;
22000	
22100		(9) Pt. - "They know who I  am."  
22200	and again anticipates questions and  rejoinders  pertaining  to  this
22300	statement.
22400	
22500		In constructing the data-base of beliefs, we tried to pack as
22600	much  information  in each belief as any `reasonable'(like ourselves)
22700	interviewer might request.  However, one cannot anticipate everything
22800	and   when  some  unanticipated  information  is  requested,  another
22900	relevant reply must be  output.This  heuristic  may  seem  less  than
23000	perfect  but  there  is little else to do when the model simply lacks
23100	the pertinent information. Humans do this also.
23200	
23300		When  the interviewer shows interest in the delusional story,
23400	the model continues to output assertions appropriate to the dialogue.
23500	However,  when the interviewer expresses doubt or disbelief about the
23600	delusions, ANGER  and  FEAR  increase  and  the  interviewer  becomes
23700	questioned as in
23800	
23900	.V
24000	     (10) Pt.- "You don't believe me, do you?"
24100	.END CONTINUE
24200	
24300	Such an output expression attempts to prompt the dialogue towards the
24400	relation  between  the  interviewer  and  the  model  which  will  be
24500	described later ( see p 00).
24600		If no  delusional  reference  at  all  is  detected  by  this
24700	procedure  ,  the algorithm attempts the next function which searches
24800	for certain types of references to the self.
24900	
25000	
25100	.F
25200	SELF REFERENCES
25300	
25400		Since the main concern of a psychiatric interview consists of
25500	the beliefs, feelings, states and actions of the patient,  the  model
25600	must  be able to answer a large number of questions about its `Self'.
25700	
25800	
25900		If  the  input  is  recognized  as a question and no topic is
26000	currently under discussion and the question  refers  to  the  'Self',
26100	then  it  is  assumed  temporarily  that it will refer only to a main
26200	self-topic. These  main  or  "introductory"  self-topics  (age,  sex,
26300	marriage,  health,  family,  occupation,  hospital stay,etc.) in turn
26400	have  sub-topics  to  varying  depths.   For  example,  suppose   the
26500	interviewer asks:
26600	
26700	.V
26800	     (12) Dr.- "How do you like the hospital."
26900	.END CONTINUE
27000	
27100	Since "hospital" is a main `introductory' topic with several levels
27200	of sub-topics, the algorithm answers the question with
27300	
27400	.V
27500	     (11) Pt. - "I shouldn't have come here."
27600	.END CONTINUE
27700	
27800	and  then  anticipates  a  variety  of likely questions such as "what
27900	brought you to the  hospital?",  "how  long  have  you  been  in  the
28000	hospital?",  "how  do  you  get along with the other patients?", etc.
28100	Each of these questions  bring  up  further  topics,  some  of  which
28200	represent  a continuation of the main topic "hospital", but others of
28300	which represent a shift to  another  main  introductory  topic,  e.g.
28400	"other  patients".   Since  many  of  the  inputs  of the interviewer
28500	consist of ellipses or fragments, the algorithm assumes them to refer
28600	to the topic or subtopic under discussion.  If some  topic  is  being
28700	discussed,  the algorithm checks first for a new main topic, then for
28800	a follow-up to the last subtopic, then (unless the subtopic is itself
28900	a  main  topic,  as  for example "other patients" in the above) for a
29000	follow-up to the last main topic.  Thus continuity and  coherence  in
29100	the dialogue is maintained.
29200	
29300		If some meaning cannot be extracted from the question but  it
29400	is  recognized  that a question is being asked, a procedure is called
29500	which attempts to handle certain common miscellaneous questions which
29600	are   difficult  to  categorize.      These  include  the  space-time
29700	orientation questions ("what day is this?") and everyday  information
29800	("who  is  president?)  asked  by  psychiatrists  in  a mental-status
29900	examination to test a  patient's  awareness  and  orientation.   Some
30000	qantitative "how" questions ("how many", "how often", "how long") are
30100	here recognized. Since any adjective or adverb can follow a "how",
30200	one  of  the  limitations  of  the model consists of its inability to
30300	handle all of them satisfactorily because the relevant information is
30400	lacking  in  the data-base.  If absolutely no clues are recognized in
30500	the question, the algorithm is forced to output a  noncomittal  reply
30600	such as:
30700	
30800	     (12) Pt. - "Well, I don't know."
30900	
31000		This function also checks for statements about the self which
31100	are taken to be insulting or complimentary. Naturally the presence of
31200	a negator in the input reverses the meaning.  Thus
31300	
31400	     (13) Dr.- "You don't seem very alert."
31500	
31600	is classified as an insult whereas
31700	
31800	     (14) Dr. - "You are right."
31900	
32000	is considered complimentary and benevolent.
32100	
32200		Among the introductory self-topics are those which constitute
32300	sensitive  areas,  e.g. sex, religion and family.  If the interviewer
32400	refers to one of these areas, the value of  ANGER  increases  sharply
32500	and  a  response  is  selected  from  one of the lists categorized as
32600	'hostile', 'defensive', 'personal' or  'guarded',  depending  on  the
32700	level  of  MISTRUST  at  the moment.  For example, if the interviewer
32800	asks a question about the model's sex life, it first replies with:
32900	
33000	   (13) Pt. - "My sex life is my own business."
33100	
33200	If the interviewer persists or even later tries to ask about sex, the
33300	model will respond with a hostile reply, such as:
33400	
33500	     (14) Pt. - "Do you know what you are doing?"
33600	
33700		The  particular  sensitive areas in the model are part of the
33800	initial  conditions  specific  for  this  hypothetical  patient.   Of
33900	course,  these  topics  are  commonly  found to be sensitive areas in
34000	human patients.
34100	
34200		The  model  operates  sequentially  trying  one major process
34300	after another.  If  it  has  come  this  far,  after  trying  special
34400	reactions,   delusional   references   and  self  references  without
34500	recognizing anything in the input pertinent to these  procedures,  it
34600	proceeds to the next, which involves flare references.
34700	
34800	.F
34900	FLARE REFERENCES
35000	
35100	The data-base contains a directed graph of concepts involved  in  the
35200	model's  'stories'.   The  model  has  small  stories  to  tell about
35300	horseracing, gambling, bookies, etc.  The  major  concepts  of  these
35400	stories are termed "flare" concepts since they activate stories which
35500	are differentially weighted in the graph.  
35600	
35700	
35800		In the strong version of the model, the  concept  'Mafia'  is
35900	given  the  highest  weight  while  in  the  weak version the concept
36000	'Rackets' is most heavily weighted.   In both versions  'horses'  has
36100	the  lowest  weight. The weights are assigned to the concepts and not
36200	individual words or word-groups denoting the concepts.
36300	
36400		The  graph  is  directed  in  the  sense  that  reference  to
36500	horseracing elicits the first line of a story about horseracing. When
36600	a story is ended, a prompt is given to the interviewer to discuss the
36700	next story in the graph which involves `bookies'.   The model strives
36800	to tell its  stories  under  appropriate  conditions  and  leads  the
36900	interviewer  along  paths of increasing delusional relevance.    Much
37000	depends on whether the interviewer follows these leads "benevolently"
37100	and reacts to the prompts.
37200	
37300		The first step in this procedure is to scan the input  for  a
37400	flare concept having the highest weight.   Thus if a flare concept is
37500	already under discussion, a weaker new flare will be disregarded.  If
37600	the flare concept is one in a story which has already been told, then
37700	a prompt is offered regarding the next story-node in the graph.
37800	
37900		If a question is asked about the events of a story, the model
38000	tries to answer it.  Also the  model  is  sensitive  to  whether  the
38100	interviewer  is  showing interest in the story or whether he tries to
38200	change  the  subject  or  expresses  a  negative  attitude,  such  as
38300	disbelief.
38400	
38500		If the interviewer indicates a positive attitude towards  the
38600	story,  then  benevolence  is  recognized and the variables of ANGER,
38700	FEAR and MISTRUST  decrease  slightly  after  each  I-O  pair.  ANGER
38800	decreases  more rapidly than FEAR while MISTRUST, being a more stable
38900	variable once it has risen, decreases least.
39000	
39100		If  no  flare concepts are recognized in the input, the model
39200	next tries to detect if a reference is being  made  to  the  relation
39300	between  the  interviewer and the model.  In an interview interaction
39400	there exists two situations, one  being  talked  about  and  one  the
39500	participants  are  in  at  the moment. Sometimes the latter situation
39600	becomes the former, that is, the one talked about.
39700	
39800	.F
39900	INTERVIEWER-INTERVIEWEE RELATIONS
40000		As described in Chapter 4, the algorithm  must  be  ready  to
40100	handle input referring to the relation between interviewer and model.
40200	The simplest cases are exemplified by expressions such as:
40300		(15) Dr.- "I understand you."
40400		(16) Dr.- "You do not trust me."
40500	Those phrases in an expression which can appear between "I" and "you"
40600	or between "you" and "me" we classified as representing a positive or
40700	negative  attitude  on  the  part of the interviewer. Thus expression
40800	(15) is taken  to  be  positive  whereas  (16)  is  negative  because
40900	although it contains a positive verb, the verb is negated.
41000		If a positive attitude is expressd by the  interviewer,  FEAR
41100	and  ANGER  decrease.  FEAR  and  ANGER  increase  depending  on  the
41200	conceptualizations of the input. These attitudes of  the  interviewer,
41300	as interpreted by the model, are reflected in the values of the affect
41400	variables.
41500		Associated in the  data  base  with  each  type  of  attitude
41600	expression expected are lists of appropriate output expressions. Thus
41700	in reply to:
41800		(16) Dr.-"I understand you."
41900	the model would reply:
42000		(17) Pt.- I'm glad you do."
42100	or
42200		(18) Pt.- "I appreciate your trying to understand."
42300	or  some  equivalent  expression  depending  on  values of the affect
42400	variables.  When  ANGER  and  FEAR  are   high,   positive   attitude
42500	expressions  are  interpreted  as insincerity and hence evoke hostile
42600	replies.
42700		The remainder of input expressions not thus far discussed are
42800	handled by a procedure for other types of miscellaneous expressions.
42900	MISCELLANEOUS EXPRESSIONS
43000	
43100		This procedure deals with all those  interviewer  expressions
43200	from  which no clear conceptualization can be formed.  The only thing
43300	which can be determined is perhaps the sentence-type  of  the  input.
43400	Presented  with  one  of these expressions, if FEAR is extremely high
43500	the model signs off without  a  farewell  expression  and  cannot  be
43600	contacted through further natural language input.  If FEAR is high
43700	but not extreme, and the input is recognized as a question, the model
43800	chooses a reply from a list which  brings  up  the  attitude  of  the
43900	interviewer as in:
44000		(19) Pt.- "Why do you want to know?"
44100	or
44200		(20) Pt.- "You pry too much".
44300	If  the  input is recognized as a statement, a reply is chosen from a
44400	list which indicates some degree of anxiety:
44500		(21) Pt.- "Who are you really?"
44600		(22) Pt.- "You are making me nervous."
44700	If ANGER is high and the input is a question, a reply is chosen from
44800	a list designed to express hostility as in:
44900		(23) Pt.- "Do you know what you are doing?"
45000		(24) Pt.- "Perhaps you are just posing as a doctor."
45100		Sometimes in these default conditions the  flag  set  in  the
45200	procedure  for  delusional references allows the model to continue by
45300	giving the next line in its delusional story.  If the story is  under
45400	discussion,  continuity  is  maintained.  But if it is not, the model
45500	appears to ignore the input and jumps back to  one  of  its  previous
45600	preoccupations.   In  this instance the observed property of rigidity
45700	is a function of linguistic non-comprehension and not of the paranoid
45800	processes  per  se.   A  further  increase  in the model's ability to
45900	comprehend conversational language would remedy this deficiency.
46000		If  a story flag has not been set by a previous discussion in
46100	the interview and ANGER and FEAR are not high, the algorithm tries to
46200	see  if the input is some type of general prompt from the interviewer
46300	such as:
46400		(25) Dr.- "Go on."
46500	or
46600		(26) Dr.- "Tell me more."
46700	If so, the model continues with its current story or attempts to
46800	initiate another story. 
46900		If none of these conditions hold, the procedure ANSWER
47000	is called. This procedure handles a group of common special-case
47100	miscellaneous questions such as:
47200		(27) Dr.- "How do you do?"
47300	and miscellaneous statements such as:
47400		(28) Dr.- "Hi."
47500		(29) Dr.- "Good evening."
47600	
47700	.F
47800	SELF SCANNING
47900		The  final  major  procedure  in the algorithm scans what the
48000	model has chosen to output. That is, it  treats  its  own  output  as
48100	input.  If  this expression contains a flare or delusional reference,
48200	the appropriate flags are set and FEAR is raise slightly, but not  as
48300	much  as if this expession came from the interviewer. In this way the
48400	model "frightens itself" by what it says about a frightening topic.
48500	
48600			SUMMARY
48700	(STOP HERE OR SUMMARIZE SOMETHING??)