International Journal of Comparative Psychology

What effects reinforcement is assumed to have and what data are collected depend on what behavioral variability means. It has extremely different meanings in molecular, molar, and unified behavior analyses. In molecular analyses the term relates reinforcement and moment-to-moment behaving of an individual organism, as when hand shaping creates new complex paterns extended in time or as when cumulative records show complex patterns. Molecular behavioral variability is easy to see, as in these two examples, but is hard to describe quantitatively. Behavioral variability in the context of molar analyses requires first aggregating behaviors, then counting them or finding their cumulative durations, and finally quantitatively summarizing the aggregate by a statistic, usually an average rate of occurrence of, or an average time allocated to, the aggregated behaviors. The statistic can also be a measure of variabiity, like the U statistic, rather than of central tendency.  Molar behavioral variability can also be quantitatively defined as the variabiity of a statistic describing some property of an aggregate as a function of time, individuals, or, most commonly, experimental parameters. Some molar accounts interpret the aggregate statistic itself (average rate, time allocation, or variaibity) as an operant response. Quantitative theories account for over 90 percent of this kind of variability in thousands of molar analyses. Molar variability, however, seldom describes or explains molecular variability, and a common molar interpretation of free-operant behaving is that molecular behavior varies ony randomly over time with a constant probability. There is little, if any, evidence for this interpretation and a considerable literature that suggests it is incorrrect. A unified analysis combines automated shaping of molecular, quantitative patterns of behaviors, a molar aggregate of those patterns, and one or more statistics descriptive of the aggregate.  A unified analysis involves both kinds of quantitative behavioral variability: moment-to-moment variability of shaped patterns resembling target patterns, and molar variability of a statistic defined over an aggregate of such shaped patterns, such as the variability of the average rate of, or time allocated to, a shaped pattern. Only simulation theories seem sufficiently powerful to produce a general and unified theory to account for both moment-to-moment behaving and statistics that describe molar aggregates.