Simulating Attachment

1
Simulating Attachment

• Why simulate attachment?
• Origins of Attachment theory
• The target behaviours to be simulated
• Design methodology and demo
• Architectural design issues to be investigated

2
Why Simulate Attachment?

• It provides a target for a design process - by
  building cognitive architectures to perform
  certain specific tasks we better understand
  architectures generally
• Reproducing in simulation specific patterns of
  attachment behaviour acts as a test-bed for
  developing architectural theories of human
  information processing

3
Why Simulate Attachment?

• the developmental trajectory has normative stages
  which show representational change
• initially only need to simulate limited cognitive
  resources
• linked with evolutionary, physiological,
  anthropological, AI, cybernetic and cross-species
  data and theory
• can abstract attachment behaviour

4
Origins of Attachment Theory

• John Bowlby, The Attachment Trilogy
• Psychoanalysis, Ethology, Evolutionary Theory and
  Cybernetics
• Early concentration on long separations, loss,
  mistreatment and psychopathology
• Changed hospital visiting practice
• Later focus on Individual Differences

5
The target behaviour

• The Strange Situation Experiment arose from
  comparing Ugandan and US infant attachment
  behaviours
• Involves 3 separation/re-union stages
• Each new stage increases the amount of anxiety
  they produce
• Four patterns of response
• A key pattern is link between home behaviour of
  mother and infant and infant behaviour on
  re-union in the SS

6
Infant reunion responses in the SS
The target behaviour

• Secure (B type) behaviour
• positive, greeting, being comforted
• Avoidant (A type) behaviour
• not seeking contact, avoiding gaze
• Ambivalent (C type) behaviour
• not comforted, overly passive, show anger
• Disorganised (D type) Behaviour
• totally disorganised and confused

7
Maternal home behaviour prior to the SS
The target behaviour

• sensitivity-insensitivity
• acceptance-rejection
• co-operation-interference
• accessibility-ignoring
• emotional expressiveness
• rigidity(compulsiveness)-flexibility

8
Attachment SS Subgroups vs prior maternal home
behaviour
The target behaviour

9
Design methodology
3 Problems

• Avoiding trivial solutions
• Whether to use data or theory to constrain the
  simulation
• Non-falsifiability

10
Design methodology
Problem 1 Avoiding trivial solutions

• The simulation is NOT trying reproduce
  superficial details of facial expression or body
  movement - like a Kismet robot might
• It is trying to simulate the causal mechanisms
  behind the behaviour, at the level of goals and
  action plans within a complete agent in a
  multi-agent simulation
• BUT an abstraction of the target behaviour in a
  broad and shallow complete agent is TOO easy to
  reproduce

11
Design methodology
Problem 1 Avoiding trivial solutions

• How to constrain the possible hypotheses space
  to exclude trivial solutions?
• Assume attachment styles are evolved, adaptive
  behaviours

12
Design methodology
Problem 1 Avoiding trivial solutions

• Concentrating on Secure (B type), Avoidant (A
  type) and Ambivalent (C type) behaviours as
  potentially adaptive responses
• Disorganised (D type) Behaviour is unlikely to be
  adaptive, but inclusion of this phenomena remains
  a possible future constraint

13
Design Methodology

• Taking an evolutionary/adaptive approach the
  differences in infant security in the Baltimore
  and Uganda studies suggests the following
  questions
• Are Internal Working Models that are used in
  moments of attachment anxiety in part formed in
  episodes centred on non-anxious socialisation and
  exploration?
• What information might infants gain from frequent
  episodes of exploration and social interaction
  that they use in infrequent episodes of
  attachment anxiety?
• If my carer wont socially interact on my terms
  at all then I am less secure and I must use my
  own actions to gain security
• If my carer sometimes socially interacts on my
  terms then I am less secure and need to
  concentrate my efforts in eliciting a response

14
Design methodology
Problem 2 How to combine data, theory and AI
techniques in the simulation - (Mook (1983) In
defense of external invalidity)

• Data and theories to be incorporated in the
  simulation
• Data from the SS and other attachment studies
• Bowlbys theory
• Distributed control architectures
• Teleoreactive architectures
• H-cogaff architecture
• Theories of Executive Function - SAS
• Machine learning algorithms (RL and ILP)

15
Design methodology
Problem 3 Non-falsifiability

• Duhem, Auxiliary Assumptions
• Popper, Falsifiability and Broad and Shallow
  architectures
• Lakatos,
• Three kinds of falsification
• Core assumptions and ad hoc assumptions
• Progressive and Degenerative Problem Shifts

16
Design methodology
An unfinished simulation
17
Architectural design issues

• how goals are chosen and represented?
• when goals are chosen how are consequent
  behaviours chosen?
• whether SS behaviour is deliberative or reactive?
• how skill acquisition, chunking, parsing,
  perceptual affordances relate to attachment?
• when and how new subsystems come on-line in
  attachment stage changes?

18
Architectural design issues
Bowlbys theory

• Behavioural systems from ethology attachment,
  exploration, fear and sociability
• Stages defined by available control mechanisms
  reflex (0-3), fixed action patterns (2-12), goal
  correction (9-36), goal corrected partnership
  (24- ), (age in months)
• Coordination and control mechanisms chaining,
  planning, totes, IWMs and language

19
The H-cogaff architecture
Architectural design issues
20
Architectural design issues
Shallice and Burgess - SAS and contention
scheduling
The cogaff schema
SAS
contention scheduling
21
Bowlbys Behaviours represented in an
infant-cogaff architecture
Internal Working Model?
22
Architectural design issues
Development of partnership in planning as
linguistic competence develops
Development of deliberative affordances or
exploration and socialisation driven by
deliberation?
Deliberation in re-union episodes
23
Architectural design issues

• A distributed control system that adapts with
  Re-inforcement Learning at each node, has a
  non-central, non-symbolic representation, given
  by the genes, and undergoes no qualitative change
  in representation
• A teleoreactive system that adapts using
  Inductive Logic Programming, has a simple central
  symbolic representation given by the genes that
  undergoes qualitative change in representation