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Unpredictable homeodynamic and ambient constraints on irrational decision making of aneural and neural foragers

Published online by Cambridge University Press:  19 March 2019

Kevin B. Clark
Kevin B. Clark*
Affiliation:
Research and Development Service, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073; Felidae Conservation Fund, Mill Valley, CA 94941; Campus Champions, Extreme Science and Engineering Discovery Environment (XSEDE), National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801; Expert Network, Penn Center for Innovation, University of Pennsylvania, Philadelphia, PA 19104; Virus Focus Group, NASA Astrobiology Institute, NASA Ames Research Center, Moffett Field, CA 94035. kbclarkphd@yahoo.comwww.linkedin.com/pub/kevin-clark/58/67/19a
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Abstract

Foraging for nutritional sustenance represents common significant learned/heritable survival strategies evolved for phylum-diverse cellular life on Earth. Unicellular aneural to multicellular neural foragers display conserved rational or irrational decision making depending on outcome predictions for noise-susceptible real/illusory homeodynamic and ambient dietary cues. Such context-dependent heuristic-guided foraging enables optimal, suboptimal, or fallacious decisions that drive organismal adaptation, health, longevity, and life history.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e40
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Copyright © Cambridge University Press 2019 

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References

Anreiter, I., Kramer, J. M. & Sokolowski, M. B. (2017) Epigenetic mechanisms modulate differences in Drosophilia foraging behavior. Proceedings of the National Academy of Sciences USA 114(47):12518–23.Google Scholar
Beekman, M. & Latty, T. (2015) Brainless but multi-headed: Decision making by the acellular slime mould Physarum polycephalum. Journal of Molecular Biology 427(23):3734–43.Google Scholar
Bekenstein, J. D. (2004) Black holes and information theory. Contemporary Physics 45(1):31–43.Google Scholar
Busemeyer, J. R. & Bruza, P. (2011) Quantum models of cognition and decision making. Cambridge University Press.Google Scholar
Cao, M. & Goodrich-Blair, H. (2017) Ready or not: Microbial adaptive responses in dynamic symbiosis environments. Journal of Bacteriology 199(15):e00883–16.Google Scholar
Clark, K. B. (2010a) Arrhenius-kinetics evidence for quantum tunneling in microbial “social” decision rates. Communicative & Integrative Biology 3(6):540–44.Google Scholar
Clark, K. B. (2010b) Bose-Einstein condensates form in heuristics learned by ciliates deciding to signal ‘social’ commitments. BioSystems 99(3):167–78.Google Scholar
Clark, K. B. (2010c) On classical and quantum error-correction in ciliate mate selection. Communicative & Integrative Biology 3(4):374–78.Google Scholar
Clark, K. B. (2012) Social biases determine spatiotemporal sparseness of ciliate mating heuristics. Communicative & Integrative Biology 5(1):3–11.Google Scholar
Clark, K. B. (2013a) Biotic activity of Ca2+-modulating nontraditional antimicrobial and -viral agents. Frontiers in Microbiology 4:381.Google Scholar
Clark, K. B. (2013b) Ciliates learn to diagnose and correct classical error syndromes in mating strategies. Frontiers in Microbiology 4:229.Google Scholar
Clark, K. B. (2015) Insight and analysis problem solving in microbes to machines. Progress in Biophysics and Molecular Biology 119:183–93.Google Scholar
Clark, K. B. & Hassert, D. L. (2013) Undecidability and opacity of metacognition in animals and humans. Frontiers in Psychology 4:171.Google Scholar
Dussutour, A., Latty, T., Beekman, M. & Simpson, S. J. (2010) Amoeboid organism solves complex nutritional challenges. Proceedings of the National Academy of Sciences USA 107(10):4607–11.Google Scholar
Eisenstein, E. M. & Eisenstein, D. (2006) A behavioral homeostasis theory of habituation and sensitization: II. Further developments and predictions. Reviews in Neuroscience 17:533–57.Google Scholar
Gödel, K. (1931) Über formal unentscheidbare Säze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik 38:173–98.Google Scholar
Gowdy, J. & Krall, L. (2016) The economic origins of ultrasociality. Behavioral and Brain Sciences 39:e92.Google Scholar
Hillesland, K. L., Velicer, G. J. & Lenski, R. E. (2009) Experimental evolution of a microbial predator's ability to find prey. Proceedings of the Royal Society B: Biological Sciences 276(1656):459–67.Google Scholar
Jobson, M. A., Jordan, J. M., Sandrof, M. A., Hibshman, J. D., Lennox, A. L. & Baugh, L. R. (2015) Transgenerational effects of early life starvation on growth, reproduction, and stress resistance in Caenorhabditis elegans. Genetics 201(1):201–12.Google Scholar
Ladyman, J., Presnell, S., Short, A. J. & Groisman, B. (2007) The connection between logical and thermodynamic irreversibility. Studies in History and Philosophy of Modern Physics 38:58–79.Google Scholar
Latty, T. & Beekman, M. (2011a) Irrational decision-making in an amoeboid organism: Transitivity and context-dependent preferences. Proceedings of the Royal Society B: Biological Sciences 278(1703):307–12.Google Scholar
Latty, T. & Beekman, M. (2011b) Speed-accuracy trade-offs during foraging decisions in the acellular slime mould physarum polycephalum. Proceedings of the Royal Society B: Biological Sciences 278(1705):539–45.Google Scholar
López Garcia de Lomana, A., Kaur, A., Turkarsian, S., Beer, K. D., Mast, F. D., Smith, J. J., Aitchison, J. D., & Baliga, N.S. (2017) Adaptive prediction emerges over short evolutionary time scales. Genome and Biological Evolution 9(6):1616–23.Google Scholar
Lumey, L. H., Stein, A. D. & Susser, E. (2011) Prenatal famine and adult health. Annual Review of Public Health 32:237–62.Google Scholar
Meyer, B., Ansorge, C. & Nakagaki, T. (2017) The role of noise in self-organized decision making by the true slime mold Physarum polycephalum. PLoS ONE 12(3):e0172933.Google Scholar
Nisbett, R. & Ross, L. (1980)Human inference: Strategies and shortcomings of social judgment. Prentice Hall.Google Scholar
Reichert, M.B., Christiansen, I.C., Seiter, M. & Schausberger, P. (2017) Transgenerational loss and recovery of early learning ability in foraging predatory mites. Experimental and Applied Acarology 71(3):243–58.Google Scholar
Trewavas, A. (2003) Aspects of plant intelligence. Annals of Botany 92:1–20.Google Scholar
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185:1124–31.Google Scholar
Vaiseman, A. M. (2014) Early-life nutritional programming of longevity. Journal of Developmental Origins of Health and Disease 5(5):325–38.Google Scholar
Werner, G. D. A., Strassmann, J. E., Ivens, A. B. F., Engelmoer, D. J. P., Verbruggen, E., Queller, D. C., Noë, R., Johnson, N. C., Hammerstein, P. & Kiers, E. T. (2014) Evolution of microbial markets. Proceedings of the National Academy of Sciences USA 111(4):1237–44.Google Scholar
Wolf, D. M., Vazirani, V. V. & Arkin, A. P. (2005) Diversity in times of adversity: Probabilistic strategies in microbial survival games. Journal of Theoretical Biology 234(2):227–53.Google Scholar