» Articles » PMID: 28018044

Representations of Complexity: How Nature Appears in Our Theories

Overview
Journal Behav Anal
Specialty Psychiatry
Date 2016 Dec 27
PMID 28018044
Citations 6
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Creating and Studying the Behavior of Artificial Organisms Animated by an Evolutionary Theory of Behavior Dynamics.

McDowell J Perspect Behav Sci. 2023; 46(1):119-136.

PMID: 37006601 PMC: 10050662. DOI: 10.1007/s40614-023-00366-1.


Empirical Matching, Matching Theory, and an Evolutionary Theory of Behavior Dynamics in Clinical Application.

McDowell J Perspect Behav Sci. 2022; 44(4):561-580.

PMID: 35098025 PMC: 8738809. DOI: 10.1007/s40614-021-00296-w.


The Effect of Reinforcement, and the Roles of Mutation Rate and Selection Pressure, in an Evolutionary Theory of Behavior Dynamics.

McDowell J Behav Anal. 2020; 40(1):75-82.

PMID: 31976961 PMC: 6701230. DOI: 10.1007/s40614-017-0094-9.


Computational model for behavior shaping as an adaptive health intervention strategy.

Berardi V, Carretero-Gonzalez R, Klepeis N, Machiani S, Jahangiri A, Bellettiere J Transl Behav Med. 2018; 8(2):183-194.

PMID: 29462488 PMC: 6454451. DOI: 10.1093/tbm/ibx049.


Theory and Behavior Analysis.

Donahoe J Behav Anal. 2016; 36(2):361-371.

PMID: 28018045 PMC: 5147449. DOI: 10.1007/BF03392320.


References
1.
McDowell J, Caron M, Kulubekova S, Berg J . A computational theory of selection by consequences applied to concurrent schedules. J Exp Anal Behav. 2008; 90(3):387-403. PMC: 2582210. DOI: 10.1901/jeab.2008.90-387. View

2.
Marr M . The eternal antithesis: a commentary on donahoe, palmer, and burgos. J Exp Anal Behav. 1997; 67(2):232-5. PMC: 1284599. DOI: 10.1901/jeab.1997.67-232. View

3.
Cording J, McLean A, Grace R . Testing the linearity and independence assumptions of the generalized matching law for reinforcer magnitude: a residual meta-analysis. Behav Processes. 2011; 87(1):64-70. DOI: 10.1016/j.beproc.2011.02.011. View

4.
Skinner B . Selection by consequences. Science. 1981; 213(4507):501-4. DOI: 10.1126/science.7244649. View

5.
Kulubekova S, McDowell J . Computational model of selection by consequences: patterns of preference change on concurrent schedules. J Exp Anal Behav. 2013; 100(2):147-64. DOI: 10.1002/jeab.40. View