Representations of Complexity: How Nature Appears in Our Theories.
A theory that ignores biology can still predict behavior accurately.
01Research in Context
What this study did
The author looked at how well functional theories predict behavior. He used the evolutionary theory of behavior dynamics as his example. The paper is conceptual, not a lab study.
What they found
The theory made good forecasts even though it never described brain parts or genes. It tracked only the functional relations between behavior and its consequences.
How this fits with other research
Lalli et al. (1995) first said behavioral lineages evolve like genes, but they still hunted for a neural 'unit' that keeps the line going. McDowell (2013) drops the hunt and shows the math alone is enough.
Blackman (1970) told us to define language by what it does, not how it sounds. The new paper widens that stance to every theory: keep the function, skip the physical blueprint.
Luiselli (1993) warned us not to let critics tag behavior analysis as 'mechanistic.' McDowell (2013) gives the same defense with fresh evidence: a theory can stay non-mechanistic and still win on predictions.
Why it matters
You can trust lean, functional models when you write programs. Build your graph of reinforcement contingencies and test it. If the data line up, you have a working account—no need to add brain talk or chemistry. Save time and stay focused on what you can change: the environment.
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02At a glance
03Original abstract
In science we study processes in the material world. The way these processes operate can be discovered by conducting experiments that activate them, and findings from such experiments can lead to functional complexity theories of how the material processes work. The results of a good functional theory will agree with experimental measurements, but the theory may not incorporate in its algorithmic workings a representation of the material processes themselves. Nevertheless, the algorithmic operation of a good functional theory may be said to make contact with material reality by incorporating the emergent computations the material processes carry out. These points are illustrated in the experimental analysis of behavior by considering an evolutionary theory of behavior dynamics, the algorithmic operation of which does not correspond to material features of the physical world, but the functional output of which agrees quantitatively and qualitatively with findings from a large body of research with live organisms.
The Behavior analyst, 2013 · doi:10.1007/BF03392319