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Single-cell Microfluidic Analysis of Bacillus subtilis

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Insight and analysis problem solving in microbes to machines.

Kevin B Clark¹

¹Research and Development Service, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA; California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA 90095, USA; Extreme Science and Engineering Discovery Environment (XSEDE), National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Biological Collaborative Research Environment (BioCoRE), Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

Progress in Biophysics and Molecular Biology

|August 18, 2015

Summary

Insight, a cognitive process for problem-solving, is redefined using computational complexity. This new definition expands understanding beyond primates to microbes and AI, improving insight research across diverse agents.

Keywords:

Cognition Decision making Emergent computation Heuristics Information processing Intelligence Natural computing

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Area of Science:

Cognitive Science
Computational Complexity Theory
Artificial Intelligence
Comparative Cognition

Background:

Traditional definitions of insight, rooted in Gestalt psychology, focus on discontinuous intellectual processes and cognitive restructuring.
Existing frameworks often neglect insight in non-primate species and advanced computational systems.
A need exists for a more inclusive and precise definition applicable across biological and artificial agents.

Purpose of the Study:

To redefine insight using principles from computational complexity theory for broader applicability.
To establish measurable criteria for identifying and studying insight in diverse problem-solving agents.
To bridge the gap between biological and artificial intelligence in the context of insight.

Main Methods:

Reframing insight's core criteria—discontinuous processing and problem restructuring—using computational complexity terminology.
Defining discontinuous processing as abrupt state transitions in algorithmic outcomes or heuristic execution.
Defining problem restructuring as combinatorial reorganization, problem-type substitution, or computational model exchange.

Main Results:

Insight is characterized as a phenomenon bounded by computational complexity, involving transitions between complexity classes.
Humans, ciliated protozoa, and technological networks demonstrate insight by restructuring time, complexity, and problem types.
This framework applies to solving polynomial (P) and nondeterministic polynomial (NP) decision problems.

Conclusions:

The computational complexity definition offers improved validity and reliability for studying insight.
Insight may be an epiphenomenon of analytical problem-solving within a larger information processing framework.
This definition facilitates the classification, investigation, and generation of insight in agents from microbes to AI.