Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

ABSTRACT

The fields of developmental biology, biomedicine, and artificial life are being revolutionized by advances in synthetic morphology. The next phase of synthetic biology and bioengineering is resulting in the construction of novel organisms (biobots), which exhibit not only morphogenesis and physiology but functional behavior. It is now essential to begin to characterize the behavioral capacity of novel living constructs in terms of their ability to make decisions, form memories, learn from experience, and anticipate future stimuli. These synthetic organisms are highly diverse, and often do not resemble familiar model systems used in behavioral science. Thus, they represent an important context in which to begin to unify and standardize vocabulary and techniques across developmental biology, behavioral ecology, and neuroscience. To facilitate the study of behavior in novel living systems, we present a primer on techniques from the behaviorist tradition that can be used to probe the functions of any organism – natural, chimeric, or synthetic – regardless of the details of their construction or origin. These techniques provide a rich toolkit for advancing the fields of synthetic bioengineering, evolutionary developmental biology, basal cognition, exobiology, and robotics.

KEYWORDS:

• Behaviorism
• synthetic morphology
• biobot
• memory
• learning
• basal cognition

Acknowledgments

We thank Julia Poirier, Douglas Blackiston, and Haleh Fotowat for helpful comments on the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

M.L. is supported by the John Templeton Foundation (grant id 62212) and by the Templeton World Charity Foundation (the opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation, Inc.). The participation of C.I.A. was supported in part by NSF-REU grant 1950805 and NSF-PIRE grant 1743753.