Biofeedback methods for enhancing knowledge transfer

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Bio-feedback methods for enhancing knowledge
transfer
Florin Munteanu, Center for Complexity Studies,
Romania
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Complicated vs. complex

• A complicated system can be modeled and
  controlled its setup is predetermined its
  behavior is predictable it obeys clear causality
  and operation rules.

A complex system is not represented by a single,
stationary model it is plastic it evolves
within its environment it can self-organize its
internal structure it switches from one
operation mode to another depending on stresses
in the environment (phase transitions) it
shows a loose cause-effect connection.
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Consequences

• Approaching and harnessing Complexity requires a
  fundamentally different approach
• Identifying patterns of behavior fingerprints
  of each state
• Identifying behavior discontinuities, clues for
  re-organization
• New approaches to data processing, which
  explicitly account for non-linearity, memory,
  scale-dependence, etc
• New experimental protocols

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Proposal

• The Knowledge Based Society defines HUMANS as the
  source of creation. Knowledge becomes the main
  asset. Management and creation of knowledge
  become an important drive for development.

Tools and methods to facilitate knowledge
transfer focus on tacit knowledge
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Hypothesis 1

• Tacit knowledge is only partially externalizable
  the person is often unaware of it, or cannot
  explain it This knowledge is attained through
  personal experience, therefore is connected to
  the persons history.

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Hypothesis 2

• There is a strong Mental/Affective coupling in
  the brain
• Through hormonal release, mental and affective
  processes influence physiology throughout the
  body!

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Hypothesis 3

• Personal discovery occurs through comparison with
  others quantifying differences in perception,
  abilities, and attitude between people can vastly
  improve mutual understanding, knowledge transfer,
  career management, etc

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Approach biomonitoring and biofeedback
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Parameter space
fingerprint of behavior
Behavior Parameters
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Parameter space
Results of 4 different physical exercises
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Data Synthesis
Subject 1
Integration (e.g., PCA neural nets)
Testing
SignalDatabase
Subject 2
Vectorization A representative set of signal
estimators
Subject n
Modeling Signal processing
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Database