Multiple time scales is well named.
Multiple-time-scales theory is too vague to test, so keep using scalar timing models that give clear numbers.
01Research in Context
What this study did
The author looked at two timing theories. One is scalar expectancy theory. The other is multiple-time-scales theory.
He asked: does MTS give clear, testable rules? He checked the math and the data.
What they found
MTS has no firm numbers you can check, the paper says. Without numbers, any result can fit.
Because of that, the author calls MTS unfalsifiable. He says stay with older, clearer models.
How this fits with other research
Webb et al. (1999) came out the same year and says the opposite. They claim MTS beats scalar theory.
The clash is sharp. Both papers are pure theory, yet one cheers MTS and one boos it. The gap is stance, not data.
Allen et al. (1989) gives pigeon data that fit scalar timing. That study shows the kind of clean curve the target paper wants to keep.
Why it matters
When you pick a timing model, pick one you can plug numbers into. MTS, this paper warns, is too loose. Stick with scalar or other models that give hard predictions you can graph for staff and parents.
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02At a glance
03Original abstract
Staddon and Higa's article is a critique of scalar expectancy theory, and a proposed alternative, multiple time scales. The critique is generally flawed, both factually and logically. The alternative is bewildering in its flexibility, opaque in its quantitative description, and never addressed to real data.
Journal of the experimental analysis of behavior, 1999 · doi:10.1901/jeab.1999.71-272