Computational models of CONSCIOUSNESS

1. ReteCog Summer School 2012 Maó, June 4-8, 2012 Computational models of CONSCIOUSNESS • Ricardo Sanz UPM Autonomous Systems Laboratory

2. Consciousness: How it works Modeling the mechanisms of mind

3. What are models for? The Human Brain Project should lay the technical foundations for a new model of ICT-based brain research, driving integration between data and knowledge from different disciplines, and catalysing a community effort to achieve a new understanding of the brain, new treatments for brain disease and new brain-like computing technologies.

4. What are models for? • The development of cognitive computational models has three major motivations: • Explanatory: they “explain” how cognition works. • Performance improvement: they can be used as blueprints for better machines. • Show business: they can be used to mesmerize people. Robonaut vs Power Ranger

5. Talk Content • Analyze the nature of computational models of consciousness • Summarily describe some major examples of models • Assess the state of computational modeling of consciousness • Evaluate future perspectives

6. About Consciousness, Models and Computation • Three terms for a total confusion

7. Consciousness

8. What is Consciousness? • “Consciousness is the appearance of a world” • “Consciousness is the presence of a phenomenal world” • These definitions are based on the distinction between phenomenal and physical reality and they suggest that phenomenal states and consciousness can be treated as interchangeable terms. [Metzinger 2009] [Gamez 2008]

9. Activated semantic memories (Hardcastle 1995) Global workspace (Baars 1988) Neural activity in area V5/MT (Tootell et al 1995) Neural assemblies bound by NMDA (Flohr 1995) Temporally-extended neural activity (Libet 1994) 40-hertz oscillations in the cerebral cortex (Crick and Koch 1990) Intralaminar nucleus in the thalamus (Bogen 1995) Re-entrant loops in thalamocortical systems (Edelman 1989) Nucleus reticularis (Taylor and Alavi 1995) Anterior cingulate system (Cotterill 1994) 40-hertz rhythmic activity in thalamocortical systems (Llinas et al 1994) Extended reticular-thalamic activation system (Newman and Baars 1993) Backprojections to lower cortical areas (Cauller and Kulics 1991) Extrastriate visual cortex projecting to prefrontal areas (Crick and Koch 1995) Certain neurons in the superior temporal sulcus (Logothetis and Schall 1989) Neuronal gestalts in an epicenter (Greenfield 1995) Outputs of a comparator system in the hippocampus (Gray 1995) Quantum coherence in microtubules (Hameroff 1994) High-quality representations (Farah 1994) • ... Too many theories? [Chalmers, Online]

10. Major Varieties of consciousness • Access consciousness (A-consciousness) is representative, quantitative and functional. It is the module of consciousness attached to the sensors reflecting environmental or self-percepts. The input channel of the mind. • Phenomenal consciousness (P-consciousness) allows one to feel emotional experiences, sensations, etc., and thus to get qualitative inputs (qualia) which give “colour” to perceptions. • Self-consciousness (S-consciousness) is the reflective capability that we enjoy when we think about ourselves. S-consciousness involves the ability of self-recognition and the awareness of one’s identity. • Monitoring consciousness (M-consciousness) refers to the state or process of awareness that leads to one’s sensations and percepts, as opposed to the contents of those sensations and percepts themselves ( I really don’t understand Block’s last category of M-C). [Block-1995]

11. Alexander’s “Axioms” • Presence: I feel that I am centred in an out-there world. • Imagination: I can remember not only past experience, but also I can imagine fictitious experience. • Attention: I am only conscious of that to which i attend. • Volition: I can select what i want and can act to obtain it. • Emotion: I can evaluate the results of planning different actions according to previous experience. [Aleksander 2009]

12. Metzinger’s “Constraints” • Global availability • Activation within a window of presence • Integration into a coherent global state • Convolved holism • Dynamicity • Perspectivalness • Transparency • Offline activation • Representation of intensities • Ultrasmoothness of simple content • Adaptivity [Metzinger-2003]

13. Seth’s four “Properties” • The co-existence of segregation and integration in conscious scenes. • The emergence of a subjective first-person perspective. • The presence of affective conscious contents, either transiently (emotion) or as a background (mood). • Experiences of intention and agency that are characteristic of voluntary action. [Seth 2009]

14. Taylor “Characteristics” • 1. Temporal duration. • 2. Attentional focus. • 3. Binding. • 4. Bodily inputs. • 5. Salience. • 6. Past experience. • 7. Inner perspective.

15. Access Consciousness Self Qualia

16. Design Principles • The analysis of the problem of construction of a general robust controller has lead us to the formulation of several design principles • These principles can be used in the construction of reusable assets for a product line approach to robust autonomous systems • Can also be used as theoretical, systemic models of natural cognition and consciousness

17. Design “Principles” • Model-based cognition: A cognitive system exploits models of other systems in their interaction with them. • Model isomorphism. An embodied, situated, cognitive system is as good performer as its models are. • Anticipatory behavior. Maximal timely performance is achieved using predictive models. • Unified cognitive action generation. Generate action based on an integrated, scalable, unified model of task, environment and self in search for global performance maximisation.

18. Design “Principles” • Model-driven perception. Perception is realised as the continuous update of the integrated models used by the agent in a model-based cognitive control architecture by means of real-time sensorial information. • System awareness. An aware system is continuously perceiving and generating meaning -future value- from the continuously updated models. • System self-awareness/consciousness. A conscious system is continuously generating meanings from continuously updated self-models in a model-based cognitive control architecture.

19. Models

20. Models and Cognitive Science

21. Models and Engineering

22. From accidental to intentional

23. Where? What? How?

24. Building Models

25. Languages for Modeling • Natural languages • Graphical languages • Mathematics • Programming languages

26. Computational models: the idea

27. A Desirable Modeling Workflow

28. CogSys Engineering Models

29. From bio to tech

30. Computational Models

31. Computational model • A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation. The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by adjusting the parameters of the system in the computer, and studying the differences in the outcome of the experiments. Operation theories of the model can be derived/deduced from these computational experiments. • Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models. http://en.wikipedia.org/wiki/Computational_model

32. # K channelalpha_n = vectorize(lambda v: 0.01*(-v + 10)/(exp((-v + 10)/10) - 1) if v != 10 else 0.1)beta_n  = lambda v: 0.125*exp(-v/80)n_inf   = lambda v: alpha_n(v)/(alpha_n(v) + beta_n(v))# Na channel (activating)alpha_m = vectorize(lambda v: 0.1*(-v + 25)/(exp((-v + 25)/10) - 1) if v != 25 else 1)beta_m  = lambda v: 4*exp(-v/18)m_inf   = lambda v: alpha_m(v)/(alpha_m(v) + beta_m(v))# Na channel (inactivating)alpha_h = lambda v: 0.07*exp(-v/20)beta_h  = lambda v: 1/(exp((-v + 30)/10) + 1)h_inf   = lambda v: alpha_h(v)/(alpha_h(v) + beta_h(v))# K channelalpha_n = vectorize(lambda v: 0.01*(-v + 10)/(exp((-v + 10)/10) - 1) if v != 10 else 0.1)beta_n  = lambda v: 0.125*exp(-v/80)n_inf   = lambda v: alpha_n(v)/(alpha_n(v) + beta_n(v))# Na channel (activating)alpha_m = vectorize(lambda v: 0.1*(-v + 25)/(exp((-v + 25)/10) - 1) if v != 25 else 1)beta_m  = lambda v: 4*exp(-v/18)m_inf   = lambda v: alpha_m(v)/(alpha_m(v) + beta_m(v))# Na channel (inactivating)alpha_h = lambda v: 0.07*exp(-v/20)beta_h  = lambda v: 1/(exp((-v + 30)/10) + 1)h_inf   = lambda v: alpha_h(v)/(alpha_h(v) + beta_h(v))

33. Cognitive Architecture CLARION Intensive Boxology [Sun 2005]

34. CAIRO Cognitive Architecture for Interactive Robot

35. Diagram of the microcircuitry • “Knowing the state of every neuron and every synapse in such a model, one may analyze the mechanisms involved in neural computations with a view toward development of novel computational paradigms based on how the brain works.” [Izhikevich 2007]

36. Intrinsic correlations • “Finally, by reproducing the global anatomy of the human thalamocortical system, one may eventually test various hypotheses on how discriminatory perception and consciousness arise.” [Izhikevich 2007]

37. Modeling levels [Marr 1982]

38. An Alternative Hierarchy [Sun 2005]

39. Using models to “explain” • Construct an architecture that models an agent who has the concept of, say, "qualia" • Tune up to synthesize the right behavior • Find the processes in that architecture that are the ones actually being referred to by that agent's use of "qualia" • The explanations of those processes will be explanations of qualia • Repeat/enlarge for other aspects

40. Some Examples • Of Computational Models of Consciousness

41. Many models of C • Freeman, W., and Taylor, J. G., 1997, Neural Networks for Consciousness, Special Issue, Neural Networks, 10(7). • Gray’s Hippocampal Predictor model (see Gray et al.). • Aleksander’s MAGNUS (see Browne et al.). • Shallice’s SAS (see Shallice). • Baar’s Global Workspace (see Newman et al.) • Harth’s Inner Sketchpad Model (see Harth). • Roll’s Higher Order Theory (see Roll). • Edelman’s Reentrant Theory (see Edelman) • ...

42. Classifying Consciousness Models • Process vs Representation: Is consciousness supposed to arise from particular computations that are performed over representations in the brain, or does it arise from some intrinsic property of representations themselves? • Specialized vs Non-specialized: Is consciousness assumed to involve mechanisms dedicated to consciousness (such as temporary memory systems or executive systems-inferior parietal lobes, frontal lobe) or is it assumed to arise from the appropriate kinds of computations or representations wherever in the brain they may occur? [Atkinson 2000]

43. Computational Theories of C What does “computational” mean here? [Atkinson 2000]

44. Computational = Mechanistic • Consciousness has always been at odds with mechanistic models. • Sufficiency of mechanistic explanations: Hypothesis of computational sufficiency:every phenomenological distinction is caused by/supported by/projected from a corresponding computational distinction. [Jackendoff 1987]

45. Sun and Franklin’s Typology • Existing computational explanations of the conscious/unconscious distinction may be categorized based on the following different emphases: • Differences in knowledge organization (e.g. SN+PS), • Differences in knowledge-processing mechanisms (e.g. PS+SN), • Differences in knowledge content (e.g. the episode vs activation), • Differences in knowledge representation (e.g. localist vs distributed), • Differences in processing modes of the same system (e.g. attractor vs threshold). [Sun 2005]

46. Focus on aspects of C • There are may examples of models addressing: • Access consciousness • Phenomenal Consciousness • Self-awareness

47. Sun CLARION ConsciousUnconscious Ability to report

48. Global Workspace Theory • Baars argues that the function of consciousness is to broadcast information to separate functional, specialist modules throughout the brain. • His ‘global workspace’ is a central processor that contains the contents of consciousness. • The Workspace functions as a cognitive “blackboard”. • Indeed Baars is inspired by Blackboard and Pandemonium AI architectures). [Baars 1998]

49. Baars GWT • Multiple parallel specialist processes compete and co-operate for access to a global workspace • If granted access to the global workspace, the information a process has to offer is broadcast back to the entire set of specialists

50. Consciousness as a ‘Theatre’ • Baars’ theory addresses the problem of access consciousness. • Consciousness is enabled by a broadcasting working memory, which provides a means to control what information can become conscious. • To explain his theory, Baars uses the analogy of consciousness as a ‘theatre’. Working memory provides the ‘stage’ of consciousness. • We only become conscious of information held in working memory if it is selected by the central executive to perform in the stage. • Central idea of Baars’ theory is that once a representation becomes conscious it becomes available to other cognitive processes.