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Models in palaeontological functional analysis
Author(s) -
Philip S. L. Anderson,
Jen A. Bright,
Pamela G. Gill,
Colin Palmer,
Emily J. Rayfield
Publication year - 2011
Publication title -
biology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.596
H-Index - 110
eISSN - 1744-957X
pISSN - 1744-9561
DOI - 10.1098/rsbl.2011.0674
Subject(s) - unobservable , bracketing (phenomenology) , biology , data science , principal (computer security) , computer science , missing data , cognitive science , epistemology , machine learning , psychology , philosophy , operating system
Models are a principal tool of modern science. By definition, and in practice, models are not literal representations of reality but provide simplifications or substitutes of the events, scenarios or behaviours that are being studied or predicted. All models make assumptions, and palaeontological models in particular require additional assumptions to study unobservable events in deep time. In the case of functional analysis, the degree of missing data associated with reconstructing musculoskeletal anatomy and neuronal control in extinct organisms has, in the eyes of some scientists, rendered detailed functional analysis of fossils intractable. Such a prognosis may indeed be realized if palaeontologists attempt to recreate elaborate biomechanical models based on missing data and loosely justified assumptions. Yet multiple enabling methodologies and techniques now exist: tools for bracketing boundaries of reality; more rigorous consideration of soft tissues and missing data and methods drawing on physical principles that all organisms must adhere to. As with many aspects of science, the utility of such biomechanical models depends on the questions they seek to address, and the accuracy and validity of the models themselves.

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