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Biorobotic Experiments for the Discovery of Biological Mechanisms

Published online by Cambridge University Press:  01 January 2022

Edoardo Datteri  and
Guglielmo Tamburrini
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Abstract

Robots are being extensively used for the purpose of discovering and testing empirical hypotheses about biological sensorimotor mechanisms. We examine here methodological problems that have to be addressed in order to design and perform “good” experiments with these machine models. These problems notably concern the mapping of biological mechanism descriptions into robotic mechanism descriptions; the distinction between theoretically unconstrained “implementation details” and robotic features that carry a modeling weight; the role of preliminary calibration experiments; the monitoring of experimental environments for disturbing factors that affect both modeling features and theoretically unconstrained implementation details of robots. Various assumptions that are gradually introduced in the process of setting up and performing these robotic experiments become integral parts of the background hypotheses that are needed to bring experimental observations to bear on biological mechanism descriptions.

Type
Research Article
Information
Philosophy of Science , Volume 74 , Issue 3 , July 2007 , pp. 409 - 430
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

Both authors wish to thank Barbara Webb and an anonymous referee for stimulating comments and criticisms of an earlier version of this paper. We are grateful to Franco Giorgi, Hykel Hosni, and Massimo Mugnai for engaging discussions on the methodological problems addressed here.

References

Amit, D. J. (1998), “Simulation in Neurobiology: Theory or Experiment?”, Simulation in Neurobiology: Theory or Experiment? 21:231–237.Google ScholarPubMed
Barnes, C. A. (1988), “Spatial-Learning and Memory Processes—the Search for Their Neurobiological Mechanisms in the Rat”, Spatial-Learning and Memory Processes—the Search for Their Neurobiological Mechanisms in the Rat 11:163–169.Google ScholarPubMed
Bechtel, W., and Abrahamsen, A. (2005), “Explanation: A Mechanist Alternative”, Explanation: A Mechanist Alternative 36:421–444.Google ScholarPubMed
Bechtel, W., and Richardson, R. C. (1993), Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research. Princeton, NJ: Princeton University Press.Google Scholar
Beer, R. D. (1997), “The Dynamics of Adaptive Behavior: A Research Program”, The Dynamics of Adaptive Behavior: A Research Program 20:257–289.Google Scholar
Beer, R. D., et al. (1997), “Biologically Inspired Approaches to Robotics”, Biologically Inspired Approaches to Robotics 40:30–38.Google Scholar
Berthoz, A. (1997), Le sens du movement. Paris: Odile Jacob.Google Scholar
Burgess, N., et al. (1994), “A Model of Hippocampal Function”, A Model of Hippocampal Function 7:1065–1081.Google Scholar
Burgess, N. (1997), “Robotic and Neuronal Simulation of the Hippocampus and Rat Navigation”, Robotic and Neuronal Simulation of the Hippocampus and Rat Navigation 352:1535–1543.Google ScholarPubMed
Burgess, N. (2000), “Predictions Derived from Modeling the Hippocampal Role in Navigation”, Predictions Derived from Modeling the Hippocampal Role in Navigation 83:301–312.Google Scholar
Cacucci, F., et al. (2004), “Theta-Modulated Place-by-Direction Cells in the Hippocampal Formation in the Rat”, Theta-Modulated Place-by-Direction Cells in the Hippocampal Formation in the Rat 24:8265–8277.Google ScholarPubMed
Chou, C. P., and Hannaford, B. (1997), “Study of Human Forearm Posture Maintenance with a Physiologically Based Robotic Arm and Spinal Level Neural Controller”, Study of Human Forearm Posture Maintenance with a Physiologically Based Robotic Arm and Spinal Level Neural Controller 76:285–298.Google ScholarPubMed
Cordeschi, R. (2002), The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics. Dordrecht: Kluwer Academic Publishers.CrossRefGoogle Scholar
Craver, C. F. (2001), “Role Functions, Mechanisms and Hierarchy”, Role Functions, Mechanisms and Hierarchy 68:31–55.Google Scholar
Craver, C. F., and Darden, L. (2001), “Discovering Mechanisms in Neurobiology: The Case of Spatial Memory”, in Machamer, P. K., Grush, R., and McLaughlin, P. (eds.), Theory and Method in Neuroscience. Pittsburgh: University of Pittsburgh Press, 112–137.Google Scholar
Craver, C. F., and Darden, L. (2005), “Introduction”, Introduction 36:233–244.Google ScholarPubMed
Cressant, A., et al. (1997), “Failure of Centrally Placed Objects to Control the Firing Fields of Hippocampal Place Cells”, Failure of Centrally Placed Objects to Control the Firing Fields of Hippocampal Place Cells 17:2531–2542.Google ScholarPubMed
Cummins, R. (1975), “Functional Analysis”, Functional Analysis 72:741–765.Google Scholar
Dario, P., et al. (2002), “Design and Development of a Neurorobotic Human-Like `Guinea Pig'”, Design and Development of a Neurorobotic Human-Like `Guinea Pig' 3:2345–2346.Google Scholar
Dario, P. (2005), “Robotics as a Future and Emerging Technology”, Robotics as a Future and Emerging Technology 12:29–45.Google Scholar
Eskiizmirliler, S., et al. (2002), “A Model of the Cerebellar Pathways Applied to the Control of a Single-Joint Robot Arm Actuated by McKibben Artificial Muscles”, A Model of the Cerebellar Pathways Applied to the Control of a Single-Joint Robot Arm Actuated by McKibben Artificial Muscles 86:379–394.Google Scholar
Fox, S. E., and Ranck, J. B. (1975), “Localization and Anatomical Identification of Theta and Complex Spike Cells in Dorsal Hippocampus in Rats”, Localization and Anatomical Identification of Theta and Complex Spike Cells in Dorsal Hippocampus in Rats 49:299–313.Google Scholar
Franklin, A. (2005), No Easy Answers: Science and the Pursuit of Knowledge. Pittsburgh: University of Pittsburgh Press.CrossRefGoogle Scholar
Glennan, S. (1996), “Mechanisms and the Nature of Causation”, Mechanisms and the Nature of Causation 44:49–71.Google Scholar
Glennan, S. (2002), “Rethinking Mechanistic Explanation”, Rethinking Mechanistic Explanation 69 (Proceedings): S342–S353.Google Scholar
Glennan, S. (2005), “Modeling Mechanisms”, Modeling Mechanisms 36:443–464.Google ScholarPubMed
Grasso, F. W., et al. (2000), “Biomimetic Robot Lobster Performs Chemo-Orientation in Turbulence Using a Pair of Spatially Separated Sensors: Progress and Challenges”, Biomimetic Robot Lobster Performs Chemo-Orientation in Turbulence Using a Pair of Spatially Separated Sensors: Progress and Challenges 30:115–131.Google Scholar
Hafting, T., et al. (2005), “Microstructure of a Spatial Map in the Entorhinal Cortex”, Microstructure of a Spatial Map in the Entorhinal Cortex 436:801–806.Google ScholarPubMed
Hopkins, J. C., and Leipold, R. J. (1996), “On the Dangers of Adjusting the Parameters Values of Mechanism-Based Mathematical Models”, On the Dangers of Adjusting the Parameters Values of Mechanism-Based Mathematical Models 183:417–427.Google ScholarPubMed
Horiuchi, T., and Koch, C. (1996), “Analog VLSI Circuits for Visual Motion-Based Adaptation of Post-Saccadic Drift”, Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems, 60–66.CrossRefGoogle Scholar
Ishida, H., et al. (2001), “Plume-Tracking Robots: A New Application of Chemical Sensors”, Plume-Tracking Robots: A New Application of Chemical Sensors 200:222–226.Google Scholar
Jaax, K. N., and Hannaford, B. (2002), “A Biorobotic Structural Model of the Mammalian Muscle Spindle Primary Afferent Response”, A Biorobotic Structural Model of the Mammalian Muscle Spindle Primary Afferent Response 30:84–96.Google ScholarPubMed
Jarrad, L. E. (1993), “On the Role of the Hippocampus in Learning and Memory in the Rat”, On the Role of the Hippocampus in Learning and Memory in the Rat 60:9–26.Google Scholar
Jeffery, K. J., et al. (1997), “Directional Control of Hippocampal Place Fields”, Directional Control of Hippocampal Place Fields 117:131–142.Google ScholarPubMed
Lambrinos, D., et al. (2000), “A Mobile Robot Employing Insect Strategies for Navigation”, A Mobile Robot Employing Insect Strategies for Navigation 30:39–64.Google Scholar
Lloyd, E. A. (1987), “Confirmation of Ecological and Evolutionary Models”, Confirmation of Ecological and Evolutionary Models 2:277–293.Google Scholar
Loeb, J. (1918), Forced Movements, Tropism, and Animal Conduct. Philadelphia: Lippincott.CrossRefGoogle Scholar
Machamer, P., Darden, L., and Craver, C. F. (2000), “Thinking about Mechanisms”, Thinking about Mechanisms 67:1–25.Google Scholar
Marr, D. (1982), Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco: Freeman.Google Scholar
Muller, R. U., and Kubie, J. L. (1987), “The Effects of Changes in the Environment on the Spatial Firing of Hippocampal Complex-Spike Cells”, The Effects of Changes in the Environment on the Spatial Firing of Hippocampal Complex-Spike Cells 7:1951–1968.Google ScholarPubMed
O'Keefe, J., and Burgess, N. (1996), “Geometric Determinants of the Place Fields of Hippocampal Neurons”, Geometric Determinants of the Place Fields of Hippocampal Neurons 381:425–428.Google ScholarPubMed
O'Keefe, J., and Dostrovsky, J. (1971), “The Hippocampus as a Spatial Map: Preliminary Evidence from Unit Activity in the Freely Moving Rat”, The Hippocampus as a Spatial Map: Preliminary Evidence from Unit Activity in the Freely Moving Rat 34:171–175.Google ScholarPubMed
O'Keefe, J., and Nadel, L. (1978), The Hippocampus as a Cognitive Map. Oxford: Clarendon Press.Google Scholar
O'Keefe, J., and Recce, M. (1993), “Phase Relationship between Hippocampal Place Units and the EEG Theta Rhythm”, Phase Relationship between Hippocampal Place Units and the EEG Theta Rhythm 3:317–330.Google ScholarPubMed
Prescott, T. J., et al. (2002), “The Robot Basal Ganglia: Action Selection by an Embedded Model of the Basal Ganglia”, in Nicholson, L. F. B. and Faulls, R. (eds.), Basal Ganglia VII. New York: Plenum Press, 349–356.CrossRefGoogle Scholar
Quinn, R. D., and Ritzmann, R. E. (2001), “Construction of a Hexapod Robot with Cockroach Kinematics Benefits Both Robotics and Biology”, in Webb, B. and Consi, T. R. (eds.), Biorobotics: Methods and Applications. Cambridge, MA: AAAI Press, 87–105.Google Scholar
Reeve, R., et al. (2005), “New Technologies for Testing a Model of Cricket Phonotaxis on an Outdoor Robot”, New Technologies for Testing a Model of Cricket Phonotaxis on an Outdoor Robot 51:41–54.Google Scholar
Reger, B. D., et al. (2000), “Connecting Brains to Robots: An Artificial Body for Studying the Computational Properties of Neural Tissues”, Connecting Brains to Robots: An Artificial Body for Studying the Computational Properties of Neural Tissues 6:307–324.Google ScholarPubMed
Rosenblueth, A., and Wiener, N. (1945), “The Role of Models in Science”, The Role of Models in Science 12:316–321.Google Scholar
Rosenblueth, A., Wiener, N., and Bigelow, J. (1943), “Behavior, Purpose and Teleology”, Behavior, Purpose and Teleology 10:18–24.Google Scholar
Schaal, S., and Sternad, D. (2001), “Origins and Violations of the 2/3 Power Law in Rhythmic Three-Dimensional Arm Movements”, Origins and Violations of the 2/3 Power Law in Rhythmic Three-Dimensional Arm Movements 136:60–72.Google Scholar
Tabery, J. (2004), “Synthesizing Activities and Interactions in the Concept of a Mechanism”, Synthesizing Activities and Interactions in the Concept of a Mechanism 71:1–15.Google Scholar
Tamburrini, G., and Datteri, E. (2005), “Machine Experiments and Theoretical Modeling: from Cybernetic Methodology to Neuro-Robotics”, Machine Experiments and Theoretical Modeling: from Cybernetic Methodology to Neuro-Robotics 15:335–358.Google Scholar
Taylor, R. (1950), “Comments on a Mechanistic Conception of Purposefulness”, Comments on a Mechanistic Conception of Purposefulness 17:310–317.Google Scholar
Tolman, E. C. (1948), “Cognitive Maps in Rats and Men”, Cognitive Maps in Rats and Men 55:189–208.Google ScholarPubMed
Von Eckardt, B., and Poland, J. S. (2004), “Mechanism and Explanation in Cognitive Neuroscience”, Mechanism and Explanation in Cognitive Neuroscience 71:972–984.Google Scholar
Webb, B. (2001), “Can Robots Make Good Models of Biological Behaviour?”, Can Robots Make Good Models of Biological Behaviour? 24:1033–1050.Google ScholarPubMed
Webb, B. (2002), “Robots in Invertebrate Neuroscience”, Robots in Invertebrate Neuroscience 417:359–363.Google ScholarPubMed
Webb, B. (2006), “Validating Biorobotic Models”, Validating Biorobotic Models 3:R25–R35.Google ScholarPubMed
Webb, B., and Consi, T. R. (2001), Biorobotics: Methods and Applications. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Wilson, M. A., and McNaughton, B. L (1993), “Dynamics of the Hippocampal Ensemble Code for Space”, Dynamics of the Hippocampal Ensemble Code for Space 261:1055–1058.Google Scholar
Woodward, J. (2002), “What Is a Mechanism? A Counterfactual Account”, What Is a Mechanism? A Counterfactual Account 69 (Proceedings): S366–S377.Google Scholar