Perceptual Control Theory (PCT)
as a Framework for Computer
Modelling Across the Social
Sciences
Dr Warren Mansell
Senior Lecturer
School of Psychological Sciences
University of Manchester
Credits to Bill Powers, Tim Carey, Rick Marken, Kent
McClelland, Yu Li, Savas Akgonul, Sara Tai, Martin Brown,
Dominic Rogers, Eric Gruber, Christine Ihenacho, Jason Wright,
Hannah Gaffney, Rachel Edwards
Plan

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My Background
What is Perceptual Control Theory (PCT)?
Working examples
◦ Economics
◦ Sociology
◦ Linguistics
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Modelling Goal Conflict in Psychopathology
MYLO – Manage Your Life Online
Further discussion
My Background
Research into Cognitive Behavioural Therapy
since 1994
 Dissatisfied with cognitive & behavioural
theories
 Came across PCT in 1998
 Use for psychotherapy (Method of Levels)
 Basic Research – with in house computer
software developer – Yu Li
 Aim of talk – wide applicability &
demonstrate integrative capacity of PCT

Demonstration
Can you tell what someone is doing by
watching what they are doing?
 ‘Rubber Band’ Demo

History
Self-Regulation Theory
e.g. Carver & Scheier (1981)
Cognitive Psychology
Perceptual Control Theory
William T. Powers (1960)
Information theory
Science Fiction
e.g. ‘cyberspace’
Cybernetics
Wiener (1948)
Ashby (1952)
Control Engineering
Harold Black (1927)
Homeostasis
Claude Bernard (1865)
Walter Cannon (1932) Ancient technology
Ktesibios (c200 BC)
Heron (10-70AD)
Early Psychology
William James (1890)
John Dewey (1890s)
John Dewey (1896) – the reflex arc

What we have is a circuit, not an arc or
broken segment of a circle. This circuit is
more truly termed organic than reflex,
because the motor response determines
the stimulus, just as truly as sensory
stimulus determines the movement.
(Dewey, 1896; p. 363).
History of Control Engineering
Perceptual Control Theory (PCT)
Developed during the 1950s by a
physicist/engineer – William T. Powers
 First published Powers, Clark & McFarland
(1960)
 Formalised Powers (1973)
 Powers’ latest Book (2008) reviewed in Nature
(Mansell, 2008)
 Diverse range of applications published across
academic domains (see www.pctweb.org)

Core Principles of PCT
Living organisms control their input, not their
output
 ‘Behaviour is (merely) the control of
perception’
 Analogous (and possibly homologous) to
homeostatic mechanisms in physiological
systems
 Engineering principles can be used to explain
these mechanisms of control

Example within speech (Cziko)

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
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

Make a /t/ sound
Notice where your tongue is placed
Repeat with tongue pressed against bottom of
mouth
Can you still say /t/?
ability to speak comprehensibly with tongue in
abnormal position shows that speech sounds are
not pre-programmed motor outputs
same phenomenon demonstrated when talking
with cigarette, eating utensil or other object in
the mouth (e.g.., food)
Negative
Feedback
Loop
Powers
(2008)
Basic Tracking Experiment
Psychological Review (Powers, 1978)
◦ High negative correlation (-.99) between invisible disturbance
(IV) and behaviour (DV)
◦ Low correlations (0.0) between disturbance and input and
between input and behaviour – not a linear causal model
◦ PCT computer model provides 0.99 correlation with actual
behaviour; replicated many times (Marken; Bourbon; see
pctweb.org)
Controlled Input (I)
Human
Behavior (DV)
Disturbance (IV)
r ~.00
IV
r ~.00
I
DV
r ~.99
Principles of PCT

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Control is achieved via negative feedback
Control is achieved via a specific hierarchical
organisation of loops
Individuals can only control their own
perception; controlling others leads to conflict
Conflict between high level control systems
accounts for ‘dysfunction’
Reorganisation re-establishes control via a
specific learning mechanism
Economics: Modelling Market Agents

McClelland (2010). www.pctweb.org
Rational choice model of economic agents
insufficient
 Modelled a range of risky & conservative
strategies – ‘robot investors’
 Relative advantages depended on economy
modelled

McClelland’s proposed model:
Investors try to control two
perceptions.
Investment growth

They want to see steady
growth in the value of their
investments.
Liquidity protection

They want to see steady
growth (or no decline) in
their liquid assets—cash.
Both perceptions are
rates of change.
These two perceptions are
sometimes in conflict.

To see your investments grow, you need to get
into the market and buy.

To increase your supply of cash, you need to
sell some of your investments.

You can’t have it both ways at once, but both
goals are desirable.
PCT Model
Above the lighter dotted line:
Our two control systems
Between the two dotted lines:
Perceptions controlled by lower-level
systems (not explicitly modeled)
Below the heavier dotted line:
Variables and relationships in the
external environment.
We treat the market price of the
investment as a disturbance.
Here’s the price of the fictitious stock. It follows the ups and
downs of the Dow-Jones average from 2000 to 2010.
There is a 6% drop overall.
140.00
130.00
120.00
110.00
Dollars
100.00
90.00
80.00
70.00
60.00
50.00
40.00
1995
1996
1997
1998
1999
2000
2001
Date
2002
2003
2004
2005
2006
Here are the starting values for the
robot investors.

Each investor starts with $200,000 in assets:
Stock worth $100,000 (1000 shares at $100 a share) and $100,000 in
cash.

Minimum transaction is 50 shares bought or sold.

Each run of the simulation is 520 iterations (weeks).
…see what they’re worth at the end of ten years.
One example - Derek
Derek’s profile:
Control System
Reference Value
System Gain
Investment Growth
15%
1000
Liquidity Protection
15%
1000
How did Derek do?
997 shares: $93,621.54
Cash: $110,953.38
Total assets: $204,574.92
2.3% GAIN
1,400
Market
Price
(times
$10)
1,300
1,200
1,100
Shares
Held
1,000
900
800
700
Cash on
Hand (in
$100's)
600
500
400
Dec 1995 Dec 1996 Dec 1997 Dec 1998 Dec 1999 Dec 2000 Dec 2001 Dec 2002 Dec 2003 Dec 2004 Dec 2005
Date
How Derek did it
He bought when the price was going down and sold
when the price was going up.
Contributions of this study to theories
of economic behavior

The agents demonstrate that an actor can appear
“rational” without having any ability for rational
deliberation.

The findings call rational choice theory into question,
since none of the assumptions for rational choice
theory are satisfied by the robot investors.

PCT offers an intriguing alternative to the received
wisdom about economic behavior.
Emergent Group Processes
Computer simulations of multiple agents
 Each formed from control systems with a
reference and gain for a variable – e.g.
proximity to others
 McPhail, Powers & Tucker (1992) - demo

Simulation ‘Run’ A
14 SA’s were given 2 identical goals:1) avoid collisions with anything in their path
2) pursue the target actor (PT)
Co-ordinates of origin for SA’s
OUTCOME:
Ring Formation
Co-ordinates of origin of target
Simulation ‘Run’ B
Randomly distributed obstacles introduced
Co-ordinates of origin for SA’s
Stationary Actors
OUTCOME:
Ring formation
Effectively guided round
obstacles
Atypicality of Runs A & B
The formations evident in Runs A & B were atypical for 2 reasons:
1) The rings formed were very symmetrical, and this is unlikely to
happen in the non-simulated world.
Almost perfect circle
2) Most gatherings are not made up of solitary individuals, rather they
are made up of ‘companion clusters’ such as families or friendship groups.
Therefore, the next run was programmed differently.
All SA’s (except the target actor) were programmed to follow another actor, to make
asymmetric pairs, with the latter member of each pair pursuing PT (the target).
[ SA1
SA2
PT]
Simulation ‘Run’ C & D
Run C (with asymmetrical pairs) ended in a
double arc form.
Again this is rarely observed in public places.
Run D included 15 obstacles (stationary actors)
and this caused some of the pairs to get
separated.
This resulted in a less symmetrical, more
authentic ring that better approximates social
forms observed in the real world.
Modelling Social Interactions
(Mansell et al., in prep)
McPhail et al. used quantitative data to
generate qualitative outcomes
 Can a PCT model be used to generate
quantitative models of actual behaviour?
 Personal Distance Paradigm

The Method
• 45 participants
• 5 participants each trial
• Each participant pairs up for a conversation with each
of the other 4 participants
• Video camera used to record personal distance for
each pairing
The conversation task
analysis – pairings of
participants as A or B
Participant
1
2
3
4
5
1
2
A
3
4
5
B
B
A
A
B
B
A
B
A
Analysis
Computer model of two feedback loops
controlling the same input (personal
distance)
 Estimate Reference Value (R) from the
mean of the distance in A pairings
 Use trial & error changes to select Rs and
Gains (G) that generate the measured
distance as their input
 Input values of R and G into model to
simulate novel pairings

Reference
Value
The PCT computer
program
(Mansell et al, 2011)
Referenc
Output
e
Gain
Value
-Has all the functions, signals
and amplification factor of
gain
(Input and output gain)
-Adjust the reference value
-Adjust the gain
-2 control systems
-Until desired input achieved
= measured personal
distance
Referenc
Input
e
Quantity
Value
Hypothesis

The estimated personal distance
generated by computer models trained
on A pairings will generate an estimate of
personal distance for B pairings that is
closely correlated with actual personal
distance
r2=0.32
The computer model
(r = .563, n= 44, p <0.01)
This is close to the correlation between actual personal
distance between A and B pairings: r = 0.60
Modelling Psychopathology
PCT states that chronic psychological
distress is caused by unresolved conflict
between control systems (Powers, 1973)
 Control systems are organised in a hierarchy
to manage complex goals
 Psychological change is carried out through a
trial-and-error learning process called
reorganisation
 Awareness and therefore learning needs to
be directed to the deeper, superordinate
systems
 Explanation of these components…

SYSTEM CONCEPT
Be a responsible person
PRINCIPLE
Follow through on commitments
PROGRAM
Drive over and return the notes
RELATIONSHIP
“Drivingness”
SEQUENCE
Make a turn
TRANSITION
Turning of steering wheel
CONFIGURATION
Fingers around rim of wheel
SENSATION
Gripping
INTENSITY
Muscle tensions
INPUT
Effect on environment
MULTI-LEVEL
CONTROL
SYSTEM
Adapted from
Carver & Scheier
(1981, 1982)
based on Powers
(1973)
Control
Hierarchies:
- Organisations
of negative
feedback loops
that specify goals
for lower level
systems
- required for
complex control
Hierarchies in PCT Robots

Kennaway (1999)
◦
◦
◦
◦
Archy
two level hierarchy
angles; orientation
simulated 3D landscape
Lippett (2005) – Real world version
of Archy
 Young (2011) – controls perception
of closeness; two levels
 Neal et al. (1997) – robotic grasping
of fish fingers on production line

Reorganisation as Learning
Error is assessed within intrinsic systems, e.g. related to
survival
 Discrepancy sensed between current error and ‘intrinsic
error’
 This discrepancy drives random variation in the change in
properties of the control systems until error is reduced
 New change in organisation persists
 No specific behaviour is learned; not reinforcement
 Modelled on computer simulations (Marken & Powers,
1989; Powers, 2008)
 Chronic conflict requires reorganisation of the systems
that set the standards for the conflicting systems – i.e.
prioritisation in upper level systems (see also social
construal theory)
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PCT Model of
Conflict (Carey,2006)
Modelled in computer
simulation (Akgonul,
Mansell, Li, Powers, &
Carey)
Compare error
reduction with
reorganisation in mid
vs. high level systems
PCT and Spoken Language
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Contemporary automated speech and machine generated speech
poor
Direct contrast to the robust and flexible nature of human speech.
PRESENCE “PREdictive SENsorimtor Control and Emulation”
(Moore, 2007)
◦ based on PCT principles
◦ novel approach for linguistics.
◦ Move away from independent study e.g. speech recognition, spoken
language.

Evidence to support PCT in spoken language
◦ Adjust volume according to surroundings.
◦ Problems when voice cannot be heard as expected.
Demonstrating PRESENCE

Moore (2007) built a Lego NXT
computer model (Alpha Rex) to
maximize synchrony of its own
behaviour with an external source.
◦ To copy the rhythm of speech

3 control loops:
1. Generate own rhythmic behaviour
2. Sense its own sounds
3. Sense external sounds
Tapping Rhythm
Alpha Rex reduces the error
Alpha Rex predicts
next word
Implications

A simple model can produce adaptive behaviour that
would be very difficult to program explicitly.
◦ 3 control loops only

The model shows that behaviour can be learned and
provide anticipatory ability
◦ the timing of next word

The model shows that PCT can be used to make
predictions about behaviour.
◦ Allows for use of context in spoken language
◦ Simulation & Learning
(Moore, 2007)
Text-Based Communication for Therapy
Manage Your Life On-line

MYLO (Manage Your Life On-line)
◦ aims to simulate Method of Levels as described by Carey (2006; 2008).

Questioning technique to sustain awareness at higher levels in a
control system hierarchy

Developed at University of Manchester for improving access to
self-help

Evaluated in comparison to a control condition (ELIZA;
Weisenbaum, 1969)

Promising results on self-reported distress…
Manage Your Life On-line
You said: I'm worried about graduating next year. I want to get a job but my
parents are putting pressure on me to do a masters.
MYLO: What do you think about the pressure that's being applied?
You said: It's stressing me out. I don't think that a masters is for me, but they
keep going on and on.
MYLO: How did you work that out?
You said: I have been struggling a little since I started the third year and don't
know if I am even going to be able to pass this! I'm worried about failing.
MYLO: You mentioned a feeling - ‘worried' - how is that right now?
Question
You mentioned a feeling - ‘worried' - how is that right now?
Answer:
It's pretty bad. I have an essay to write and I don't really understand it. I'm so
stupid.
submit
How MYLO works
I have been feeling [[worried] about doing] a presentation at uni for my
dissertation. [I know that] I’m going to forget what to say and look stupid.
•
•
•
•
Anxious
Worried
Scared
etc…
•
•
•
•
I know that
I think that
I’m sure he
etc…
•
•
•
•
next time
he might think
Worried about doing
Etc…
Perception
Sensing
Future Events
+55 more
• Can you tell me more about what
“worried” is like for you?
• When you say “worried” how does that
actually feel for you?
• What do you think about feeling '*'?
• What makes you think that?
• How does thinking this affect you?
• What makes you believe this is the
case?
• How is it to picture the future like that
just now?
• How does picturing the future like that
make you feel now?
• How does that image sit with you?
Summary of Current Research

Compare effects of MYLO with control
condition (ELIZA) on distress
◦ Develop as a mental health intervention

Computer models of personal space control
◦ Hierarchical goals in future

Computer models of psychopathology and
psychotherapy
◦ Build algorithm that automatically selects the
most adaptive focus of reorganisation
◦ Model two agents to illustrate how control is
critical in psychotherapy

See www.pctweb.org
Some Points for Discussion…
How does PCT compare to existing
models?
 Where to get software – most online at
www.billpct.org/ ; also liaising with
Powers, Marken, McClelland, Moore,
etc…
 Need an empirical overview paper
 Does this truly integrate disciplines?
