theories of high level cognition in natural and artificial ... · cognition: circa 1970-1990...
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Theories of high level cognition in natural and artificial systems: can we agree on any underlying principles?
John Fox
University of Oxford (Engineering Science)UCL (Oncology, Royal Free Hospital) www.cossac.org
Cognitive systems
“… are natural or artificial information processing systems, including those responsible for perception, learning, reasoning, decision-making, communication and action” (www.foresight.gov)
High level cognition: medical expertise
Imaging Staging Radiotherapy Prognosis
Biopsy Chemotherapy Hormone therapy Risk assessment
Diagnosis Surgery Adjuvant therapy Trial eligibility
Summary of talk
• What is cognition? depends on your objectives– “Scientific”, empirical– Axiomatic, “rational”– Engineering, practical
• Research programme on medical expertise – A general model for HLC?
• High level cognition and folk psychology– A meeting place for different tribes in
cognitive science?
Rational principles, Axiomatic theories
Empirical studies of natural cognition
Practical engineering, application design
Understanding high level cognition: circa 1970-1990
Memory, semantic networks, spreading
activation … heuristics & biases
Logic/deductionBayesian inference
Decision theory
Expert systems,Knowledge
representation tools,
Ontologies
Understanding high level human cognition: cognitive psychology
Rational principles, Axiomatic theories
Empirical studies of natural cognition
Practical engineering, application design
“Making decisions under the influence of memory” Psych Review, 1980
Run me
Issues
• Good account of the subject behaviour– Focused on group data – Individual differences left unexplained
• Task still very simple compared with real medical expertise– We want an account of the whole
process – Autonomous operation
Understanding high level cognition: engineering
Rational principles, Axiomatic theories
Empirical studies of natural cognition
Practical engineering, application design
A model of medical expertise
Clinical
objectives
Options
Selectoption
-
Patient data
Care plans
Actions
Safe and Sound: Artificial Intelligence in Hazardous ApplicationsFox, and Das, AAAI and MIT Press 2000
Decisions Plans
Modelling clinical processes
Medical case,
clinical requirements
Task and process
modelling
Specification and Verification Of model
Behaviour
testing
Clinical
deployment
Medical case,
clinical requirements
Task and process
Modelling
Specification and Verification Of model
Performance
testing
Clinical
deployment
The patient journeyGP referrals, MedInfo 2001
Detection of breast abnormalities,
Med Image Anal, 1999
Family history and risk assessment
BMJ 1999, 2000
Triple assessmentB J Cancer 2006
Personalised care planning and counselling
Cancer Education
Clinical decision support
ApplicationsSupport for cancer multidisciplinary decision-making(Patkar et al BASO 2008, San Antonio 2009, Barcelona 2010)
Spoken language dialogue interfaces for cognitive agents (Beveridge and Fox, J Biomed Informatics, 2006)
Triple assessment in breast cancer (Hurt, Patkar et al Brit J Cancer 2006)
Dose adjustment in prescribing for children with ALL (Bury and Hurt Brit J Haematology, 2005)
Counselling women at risk of breast/ovarian cancer
(Glasspool et al Methods of Information in Medicine 2004)
GP referrals for common cancers (Bury et al MEDINFO 2003)
Genotype of HIV+ patients interpretation and selection of anti-retrovirals (Tural et al AIDS 2002)
Genetic risk assessment (Coulson, Glasspool and Emery BMJ 1999, 2000)
Support for mammographic screening (Taylor et al, Medical Imaging 1999)
Prescribing in general practice (Walton et al, BMJ 1997)
Understanding high level cognition: “agents” 1990-2010
Empirical studies of natural cognition
Rational principles, Axiomatic theories
Practical engineeringDesign
Formalising the domino model
Goals
Options
Commitments-
Beliefs
Plans
Actions
Das, Fox et al J Exp. Theor. AI 1997, Fox, and Das, MIT Press 2000Fox et al IEEE Intelligent Systems, 2006
Run me
Runme 2
From autonomous agents to frontal lobe function
Shallice T “Fractionation of the Supervisory System” in Stuss and Knight (eds) Principles of Frontal Lobe Function, OUP 2002
Shallice T “Contrasting domains in the control of action: …” in Minataka & Johnson (eds) Attention and Performance, 2006
1. Articulation of a goal (abstracting from the current situation)
2. Production of one or more solutions to attain the goal3. Selection between them and the consequent decision to
act4. Articulation of the procedure into a sequence of
implementable steps5. The realisation of the steps as actions6. Checking actions are realising the goal(s)
A general schema for cognitive science?
• It is proving useful in a variety of ways– As a general agent model (wider than BDI)– As a productive engineering framework– As a rationale for understanding human “executive
function”
• Can it support interdisciplinary cognitive systems research? – It is not grounded in a rigorous experimental
programme – It is not clear what status the objects in the model
have, or how computational processes relate to neuro-cognitive mechanisms
– It adopts a kind of folk psychology perspective, with all the philosophical and other issues this implies
High level cognition and folk psychology
Functionalism (1)
• “A theory of mind which implicitly defines terms such as "believe", "want" and "desire” relating sensory experiences to mental states; mental states to other mental states; and mental states to behaviour”
• “A theory of human mind-brain which postulates a data structure or knowledge representation which mediates between our observations of behavior-in-circumstances and our predictions and explanations of that behaviour”
Functionalism (2)
• Functionalism is a theoretical level between the physical implementation and behavioural output (Marr, 1982)
• Led to the adoption of BDI (beliefs, desires, intentions) model of agency in AI.
High level cognition, expertise
ConcreteImplementations
Critique of folk psychology
• The words that represent “mental states” are at best imprecise and quite possibly meaningless– They are culturally defined– Not “real” states of brain-mind
• As a “theory” folk psychology is scientifically barren: it makes no testable predictions
General cognitive principles?
Rational principles, Axiomatic theories
Empirical studies of natural cognition
Practical engineeringDesign
Can we understand the core functions of
high level cognition in a generic or canonical
form?
Bayesian inferenceStatistics, decision theory
Countless applicationsCognitive science (Judgement,
perception, NL …)
DeductionPhilosophy, computer scienceCognitive science (psychology
of reasoning)
Neural networksAI, neuroscience,
robotics, applications
Spreading activationCognitive science
Goals
Possible worlds
Decisions
Situations
Plans
Actions
Principle 1: Belief
Any autonomous agent (natural or artificial) needs to maintain a consistent set of beliefs and expectations with respect to its current environment
Goals
Possible worlds
Decisions
Situations
Plans
Actions
If a belief or plan entails a threat or an opportunity then an autonomous agent must be able to coordinate its behaviour to mitigate the threat or exploit the opportunity
Motivation/DrivesClassical psychology
Utilities, preferencesEconomics,
Management …
Non-classical logicAI
computer sciencephilosophy
Other usesEducation
Organisational psychologySoftware engineering
Folk psychology
Principle 2: Goals
If an agent has a goal for which it can propose a possible way of achieving the goal then this should be viewed as a candidate solution for assessment against alternatives
Goals
Possible worlds
Decisions
Situations
Plans
Actions
General PSPsychology,
AI, robotics
Kinds of PSExplanation
Constraint solvingPlanning, Design …
Principle 3: Problem solving
Goals
Possible worlds
Decisions
Situations
Plans
Actions
Every independently justified line of reasoning for or against a candidate problem solution should be considered when assessing decision options
Hypothetical reasoningBayesian inference
Epistemic logic
Practical reasoningDecision theory
Deontic logic
Debate/disputeAI,
multi-agent systemsLaw …
RhetoricPragma-dialecticsCritical questions
Principle 4: Argumentation
Goals
Possible worlds
Decisions
Situations
Plans
Actions
The more independent lines of reasoning there are that are consistent with a decision option (belief or plan) the greater an agent’s preference for the
option
Principle 5: AggregationJudgement
Decision psychologySocial choice theory
Multi-criteria decision-making
Decision theoryEvidential reasoning
Expected utilityStatistical decision theory
Decision logics
Goals
Possible worlds
Decisions
Situations
Plans
Actions
If an agent determines that its current preferred option will not change with further information then it can safely commit to that option
AcceptanceLogic
Philosophy
and truthand beliefand action
and altruism …
Principle 6: Commitment
Goals
Possible worlds
Decisions
Situations
Plans
Actions
If an agent's commitments include actions or plans that are necessary to achieve one or more of its goals, then these should be enacted in a way that is optimised with respect to the agent’s priorities.
Computer scienceProgram execution
AI, roboticsPlan enactment
Cognitive neuroscienceContention scheduling
EngineeringBusiness process modelling
Workflow
Principle 7: Plan enactment
Principle 8: Action
If an agent has an intension/plan to carry out an action and there is no reason to postpone its execution then implement the appropriate act
Ballistic operationEngineering
Engineering psychology
Conditional operationSoftware
Servomechanismshardware
Actions
Goals
Possible worlds
Situations
Plans Decisions
An agent should continually check that its actions have all and only the anticipated and intended effects Goals
Possible worlds
Decisions
Situations
Plans
Actions
Principle 9: Monitoring
Signal processingVision science/psychophysics
Hearing and speechNeuroscience
Instrumentation …
PerceptionCognitive psychology
AI
ReflectionAI, philosophySelf monitoring
Consciousness studies
Cognitive principles; standard components? IEEE Intelligent Systems, 2006
Canonical principles?
Fox et al, IEEE Intelligent Systems, 2006
Functionalism revisited
• Cognitive systems researchers have very different perspectives, but in practice they are converging on some common questions
• Cognitive scientists would benefit from a common framework in which to discuss common interests and compare different designs (natural and artificial)
• Is a functionalist perspective a practical foundation for productive conversations??
Other stuff
Provide bridging tools
Abstract functions,“components”
ConcreteImplementations
Promote communication
http://neurocogblog.blogspot.com/
The “bifocal view”• How do states of consciousness fit into
neuroscience (since they seem to be created by the brain) and into psychology (since they seem to be related to behaviour)?
• Today’s dominant view is functionalism: the (input-output) relationships that hold between a behaving organism and the environment in which it behaves
• To achieve an understanding of how activity in the brain can relate [input to output] one has to consider … the central nervous system … as falling simultaneously under the laws of physics and chemistry and under those of cybernetics (or ‘information processing’)
Jeffrey Gray
Consciousness: Creeping up on the hard problem
Thinking About Thinking• Introducing the Real Time Club Brain, Mind & Computing Forum
Lunch
Wednesday 12 May 12:00 to 14:00
Venue: National Liberal Club, Whitehall Place London SW1A 2HE
http://www.realtimeclub.org/index.php?option=com_rtcmeetings&detail=46&Itemid=8&LinkedIn=dC
The relationship between neurophysiology (ie structure and biochemistry of the brain) and cognition (ie how we think and experience the world) is central to every aspect of our lives, yet one of the least understood areas of science. This will be a very important area of study over the coming decades, and is already producing research and debate on issues such as biological bases for criminality and antisocial behaviour (including use of evidence of tendency to such behaviour in court) and use of drugs that can enhance cognitive performance (e.g. nicotine and ritalin). Modern digital computing techniques will be crucial to research on the linkages of neurophysiology and cognition, as has been the case with recent tremendous advances in genetic research.