Michael Graziano's consciousness

Summary

Introduction

• Attention is the focusing of our mental resources onto a specific piece of information.  Attention uses valuations assigned to each potential object of thought by the basal ganglia. 
  is viewed as processing real physical phenomena.  Attention in the context of neuroscience is seen as the enhancement of some signals at the expense of others by focusing resources.  He notes this process can be programmed into a computer chip. 
  
• Awareness in contrast is viewed as the brain's high level model of the real physical phenomena. 
  

1. Historically we concluded there was high level subjectivity based on introspection.  But introspection is accessing internal mental models and the models are flawed. 
   
2. There is just a data processing device which leaves no room for subjective impression. 
   
3. While it might seem unlikely a brain without the property of subjective impression would waste energy computing models of subjective awareness and attribute that property to itself Graziano says that is so.  Graziano's research framed by the attention schema of consciousness identifies an alternative explanation for the value of subjective models. 
   
4. He asserts:
1. The brain needs an approximate model of attention to be able to control the item of attention efficiently. 
   
2. To predict the behavior of other creatures, the brain needs to model their brain states including their attention. 
   
5. Attention schema theory pulls together evidence from social neuroscience, attention research, control theory and elsewhere.  Almost all other theories of consciousness are rooted in our intuitions about awareness which depend on the brain's models which are caricatures of reality.  

• This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
  
  Emergence introduces a set of constraints which apply to humans, their brains and all the other organs, and the cells and chemicals from which they are constructed.  An emergent hierarchy of platforms obeying
  This page discusses the physical foundations of complex adaptive systems (CAS).  A small set of rules is obeyed.  New [epi]phenomena then emerge.  Examples are discussed. 
  
  physical rules and constraints support the development of emergent phenomena encapsulating additional rules at each higher emergent level.  At each higher level the apparent operation of lower level supporting phenomena is typically an approximation at best.  But the approximations benefit to the maintenance of the emergent phenomena is real.  The key point is that an emergent event always results in emergent approximate models of reality that help sustain the emergence. 
  
• Emergent
  Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
  It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
  The participation of agents in flows is introduced and some implications of this are outlined. 
  
  agents must start out with a very poor understanding of the physical world in which they exist.  This is true for both:
  
• The
  Richard Dawkin's explores how nature has created implementations of designs, without any need for planning or design, through the accumulation of small advantageous changes. 
  
  initial generations which have rudimentary plans and
  
• Any current generation 'organism' when it is
  This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
  
  first assembled since it has little context about its proximate environment. 
  
• Science follows emergence, and
  This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
  
  evolution in testing out hypothesis of what is happening in the real world.  Science and evolution record the strategies for testing the hypothesis and hence build iteratively better hypothetical models of reality based on prioritizing highly the successful strategies.  But evolution's competitive mechanism is relative.  The one-eyed man is king in the land of the blind.  Evolved models of reality only need to be good enough to ensure reproductive success.  And in our
  Russ Abbott explores the impact on science of epiphenomena and the emergence of agents. 
  
  emergent layered world it is likely that the scientific models are also just useful approximations of the underlying physical phenomena. 
  
• When rapid response is important for survival evolution can support the emergent development of plan-ahead serial buffers which support the representation of
  Representing state in emergent entities is essential but difficult.  Various structures are used to enhance the rate and scope of state transitions.  Examples are discussed. 
  
  structurally enhanced state. 
  
• The architecture of emergent agents is recursive.  Grasiano notes that the high level human is just a data processing device.  The CAS
  Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
  It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
  The participation of agents in flows is introduced and some implications of this are outlined. 
  
  agent architecture allows evolution to gather together useful algorithms that encode how to construct and then execute.   It can provide a vast variety of
  Agents use sensors to detect events in their environment.  This page reviews how these events become signals associated with beneficial responses in a complex adaptive system (CAS).  CAS signals emerge from the Darwinian information model.  Signals can indicate decision summaries and level of uncertainty. 
  
  sensors, signals,
  Flows of different kinds are essential to the operation of complex adaptive systems (CAS). 
  Example flows are outlined.  Constraints on flows support the emergence of the systems.  Examples of constraints are discussed. 
  
  channels and controls, structural members, and
  Plans emerge in complex adaptive systems (CAS) to provide the instructions that agents use to perform actions.  The component architecture and structure of the plans is reviewed. 
  
  planning fragments.  Its programs are developed emergently and are
  This web page reviews opportunities to enhance computing theory and practice by using biological mechanisms and complex adaptive system (CAS) theory. 
  
  highly robust unlike the Von Neumann, John was a brilliant Hungarian mathematician who published the earliest paper specifying architecture for digital computing.  It ensured this computing architecture was not patentable.  The architecture has a central processing unit (CPU), random access storage addressable by the CPU and a sequencer.  The architecture encourages a serial software architecture that matches the logic of the sequencer and processing operations on program and data.  Von Neumann, his history, computing architecture and some alternative architecture are reviewed by Melanie Mitchell. 
  data processing architecture. 
  
• In each agent, at whatever layer of the CAS hierarchy,
  This page discusses the potential of the vast state space which supports the emergence of complex adaptive systems (CAS).  Kauffman describes the mechanism by which the system expands across the space. 
  
  state must be represented.  This must be sophisticated enough to allow both bootstrapping from a
  This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
  
  single cell, and representation of the local environment that the agent is operating in.  That must mean that there are mechanisms retained to both:
  
• Work bottom up based on the controlled state provided at conception. 
  
• Work top down evaluating sensed streams of data from the detectable epiphenomena modelled by the agent sensors and deciding on the priority of building alternative infrastructure to effectively cope with the local environment. 
  
• One method of deploying cooperating sets of agents is to separate into a
  This page discusses the interdependence of perception and representation in a complex adaptive system (CAS).  Hofstadter and Mitchell's research with Copycat is reviewed. 
  
  perception and representation architecture:
• Managers who focus on rapidly allocating tasks and deciding on the priority of the results, and then update the local state of the gross agent they represent. 
  
• Sets of modelers which use bottom up rapid strategies to validate signals relative to the current local state of the agent.  
• Sets of evaluators which respond to the decision signals proposing alternative ways the higher level plan should proceed.  During development the types of evaluators will have been tested and pruned to the requirements of the proximate environment.  Hence an adult
  This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
  
  organism is likely to be able to rapidly evaluate many parallel aspects of its typical proximate environment. 
  
• Builders who decide when the situation and proposals of the evaluators justify raising the sentience of an evaluation. 
  
• To gain a more global awareness of the implications of the assessments of managers and builders, higher level viewpoints are also stored as evolved models that have previously proved beneficial in the situation of this set of assessments must be used.  Some agent(s) must take on that
  Jonathan Powell describes how the government of, the former UK Prime Minister, Tony Blair, actually operated.  Powell was Blair's only chief of staff. 
  
  chief executive role.  We conclude consciousness has this role. 
  
• Consciousness, or at least conscious access is, argues Stanislas Dehaene, when some attended information eventually enters our awareness and becomes reportable to others. 
  as it is defined in '
  Consciousness has confounded philosophers and scientists for centuries.  Now it is finally being characterized scientifically.  That required a transformation of approach. 
  Realizing that consciousness was ill-defined neuroscientist Stanislas Dehaene and others characterized and focused on conscious access. 
  In the book he outlines the limitations of previous psychological dogma.  Instead his use of subjective assessments opened the window to contrast totally unconscious brain activity with those including consciousness. 
  He describes the research methods.  He explains the contribution of new sensors and probes that allowed the psychological findings to be correlated, and causally related to specific neural activity. 
  He describes the theory of the brain he uses, the 'global neuronal workspace' to position all the experimental details into a whole. 
  He reviews how both theory and practice support diagnosis and treatment of real world mental illnesses. 
  The implications of Dehaene's findings for subsequent consciousness research are outlined. 
  Complex adaptive system (CAS) models of the brain's development and operation introduce constraints which are discussed. 
  
  
  Consciousness And The Brain', appears to be a necessary set of high level agents, that provide:
  

1. Feedback on the appropriateness of current environmental model assessments allowing for lower level modelers to perform recording of more situational signals so that the brain can adapt to its proximate environment. 
   
2. Responding to the stream of internal models that reflect awareness prioritization of incoming environmental signals, and where possible resolution of lower level assessment conflicts. 
   
3. Responses to the significance of the high level state, with only internal models to support it.  These changes to the high level state must be broadcast out to the rest of the high level agents and lower level agents that model signals based on the current proximate state. 
   
4. Provide long term integration of the multiple strategies that make up the current very
   This page looks at schematic structures and their uses.  It discusses a number of examples:
   

   • Schematic ideas are recombined in creativity. 
   • Similarly designers take ideas and rules about materials and components and combine them. 
   • Schematic Recipes help to standardize operations. 
   • Modular components are combined into strategies for use in business plans and business models. 

   
   As a working example it presents part of the contents and schematic details from the Adaptive Web Framework (AWF)'s operational plan. 
   
   Finally it includes a section presenting our formal representation of schematic goals. 
   Each goal has a series of associated complex adaptive system (CAS) strategy strings. 
   These goals plus strings are detailed for various chess and business examples. 
   
   high level plan of action.  
5. Adjust the priorities of strategies within the plan, or adopt an alternative plan when the situation in the proximate environment changes significantly.  

• An emergent method of
  The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
  
  modelling other humans and other CAS based organisms is to take the self-models required by the high level agents to define the current state and use it to bootstrap a representation of the 'other' making an initial assumption that they will behave like us.  Given that they are typically branched from the same genetic network that is likely to be quite valid.  To RSS this logic seems more likely than Graziano's assertion that the self-model reflects the need to model other organisms. 
  

Politics, Economics & Evolutionary Psychology

Business Physics Nature and nurture drive the business eco-system Human nature Emerging structure and dynamic forces of adaptation

integrating quality appropriate for each market

Copyright ©2012, 2013, 2014, 2015, 2016, 2017 •  Rob Lingley