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. Avoiding trivial solutions Whether to use data or theory to constrain the simulation Non-falsifiability Design methodology 3 Problems:

10. 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 Design methodology Problem 1: Avoiding trivial solutions

11. How to constrain the possible hypotheses space to exclude trivial solutions? Assume attachment styles are evolved, adaptive behaviours Design methodology Problem 1: Avoiding trivial solutions

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 won’t 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. Data and theories to be incorporated in the simulation Data from the SS and other attachment studies Bowlby’s theory Distributed control architectures Teleoreactive architectures H-cogaff architecture Theories of Executive Function - SAS Machine learning algorithms (RL and ILP) Design methodology Problem 2: How to combine data, theory and AI techniques in the simulation - (Mook (1983) In defense of external invalidity)

15. 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 Design methodology Problem 3: Non-falsifiability

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 Bowlby’s 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’, IWM’s 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. Bowlby’s 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