David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Behavioral and Brain Sciences 24 (6):1033-1050 (2001)
How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation.
|Keywords||animal behaviour levels models neuroethology realism robotics simulation|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Guglielmo Tamburrini & Edoardo Datteri (2005). Machine Experiments and Theoretical Modelling: From Cybernetic Methodology to Neuro-Robotics. [REVIEW] Minds and Machines 15 (3-4):335-358.
Edoardo Datteri & Guglielmo Tamburrini (2007). Biorobotic Experiments for the Discovery of Biological Mechanisms. Philosophy of Science 74 (3):409-430.
Martijn Meeter, Janneke Jehee & Jaap Murre (2007). Neural Models That Convince: Model Hierarchies and Other Strategies to Bridge the Gap Between Behavior and the Brain. Philosophical Psychology 20 (6):749 – 772.
Whit Schonbein (2010). Can Computational Simulations of Language Emergence Support a 'Use' Theory of Meaning? Philosophical Psychology 23 (1):59-74.
Similar books and articles
Jørn Hokland & Beatrix Vereijken (2001). Can Robots Without Hebbian Plasticity Make Good Models of Adaptive Behaviour? Behavioral and Brain Sciences 24 (6):1060-1062.
Ronald N. Giere (2001). The Nature and Function of Models. Behavioral and Brain Sciences 24 (6):1060-1060.
Nagarjuna G. (forthcoming). Towards a Model of Life and Cognition. In B. V. Srikantan (ed.), Foundations of Science. Center for Studies in Civilizations
Gert de Cooman & Peter Walley (2002). A Possibilistic Hierarchical Model for Behaviour Under Uncertainty. Theory and Decision 52 (4):327-374.
Titus R. Neumann, Susanne Huber & Heinrich H. Bülthoff (2001). Artificial Systems as Models in Biological Cybernetics. Behavioral and Brain Sciences 24 (6):1071-1072.
Added to index2009-01-28
Total downloads17 ( #203,284 of 1,789,800 )
Recent downloads (6 months)4 ( #196,844 of 1,789,800 )
How can I increase my downloads?