Practitioner Development

THE epistemologies of parsimony: A review of <i>Ockham's razors: A user's manual</i> by Elliott Sober

Smith (2017) · Journal of the Experimental Analysis of Behavior 2017
★ The Verdict

Recording more than one response at a time could give numbers that challenge pure environmental accounts.

✓ Read this if BCBAs who train students or write single-response protocols.
✗ Skip if RBTs looking for quick skill-acquisition tactics.

01Research in Context

01

What this study did

Smith read Sober’s book Ockham’s Razors and wrote a short review.

He asked one question: could animal data ever prove Skinner wrong?

The paper is pure philosophy—no new lab work, just clear reasoning.

02

What they found

Sober says Bayesian and frequentist ideas of “simple” are not the same.

If you record many response types, not just one, the numbers could favor a mental explanation over pure environment.

That would shake the radical-behaviorist claim that only outside events control behavior.

03

How this fits with other research

Leigland (1999) and Watson et al. (2007) use pragmatism to defend Skinner. They say values and science can live together. Smith (2017) adds a warning: hard data might still overturn Skinner.

Marr (1989) wants Newton-style equations for response rate. Smith likes the numbers idea but says the equation must fit multi-response data or it hides mental causes.

Malott (2018) tells practitioners to trust JABA first. Smith answers: before you trust, design the test that could fail—then you really know.

04

Why it matters

Next time you write a protocol, add a second response class. Watch both with equal care. If the simpler environmental story breaks, you will spot it early—and stay honest to the science.

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Add one extra measure—like latency or topography—to your current target and graph both.

02At a glance

Intervention
not applicable
Design
theoretical
Finding
not reported

03Original abstract

Sober analyzes two paradigms of parsimony that have been used successfully in science. These are associated with two interpretations of probability: Bayesian and frequentist. Sober applies these paradigms to problems in biology, psychology, and philosophy. In the chapter on psychology, he argues that objective data consisting of environmental input and two or more concurrent responses could be used to refute empirically the radical behaviorist thesis that probability of learned responses can be accounted for solely on the basis of environmental variables. Sober believes that such data are readily available and offers a thought experiment to illustrate his point. Behavior analysts, however, would want actual experimental data, undoubtedly with animals, before accepting any such refutation. Nonetheless, Sober's philosophical point about the type of experiment that would be capable of refuting this thesis is valid. The behavior analytic program, however, does not depend upon the truth of this thesis.

Journal of the Experimental Analysis of Behavior, 2017 · doi:10.1002/jeab.292