From Momentary Maximizing to Serial Response Times and Artificial Grammar Learning

Shimp, Charles P.; Herbranson, Walter T.; Fremouw, Thane

doi:10.1093/oxfordhb/9780195392661.013.0034

Cited by 4 publications

(5 citation statements)

References 60 publications

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“…Given these provisional considerations, Usage 10 has four parts, (1) methods for shaping of moment-to-moment behaving into desired complex operants, (2) constructing aggregates of the shaped operants, (3) counting operants in aggregates and constructing empirical functions that show how statistics (usually averages) of aggregated shaped patterns depend on experimental parameters, and (4) developing computational simulation theories to describe and explain the empirical functions. This summary provides the information necessary for present purposes; further details are available in Shimp (2013Shimp ( , 2014 and Shimp et al (2012).…”

Section: Usage 10: Combining Shaping and Aggregatingmentioning

confidence: 99%

Molecular (moment‐to‐moment) and molar (aggregate) analyses of behavior

Shimp

2020

J Exper Analysis Behavior

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Ten meanings or usages of the terms molecular and molar analyses are based on (1) numbers of responses, (2) durations of activities, (3) levels, (4) scales, (5) contiguity versus correlation, (6) behavioral standards, (7) function with or without structure, (8) local versus global phenomena, and (9) control by shaping of sequential moment-to-moment behavior. These usages reveal divisive viewpoints along with ambiguities in the Law of Effect, the definition of an operant, response strength, response probability, random behavior, time allocation, shaping, controlled versus uncontrolled operants, and roles for ordinary language. Usage 10 is less divisive and combines, and in that sense unifies, molecular behavior, defined as shaped moment-to-moment sequential behaving, and molar, defined as averages of aggregates of those shaped responses. It combines shaping, that establishes and changes operant behaviors, and strengthening that changes the amounts of those shaped behaviors. I conclude that general behavioral theory will combine strengthening with such methods as parametric, hybrid, or nonparametric shaping, and will use computational methods to simulate moment-to-moment behavior streams from which any aggregates of theoretical interest may be computed. Such a synthesis may not require different levels, scales, or new scientific paradigms. Key words: molecular and molar analyses, moment-to-moment and aggregate methods, shaping and strengthening effects of reinforcement, plain English and scientific language, combined theory I am indebted to Jack Marr whose editorial comments on earlier drafts of this paper contributed materially to it. His comments were always helpful role models of conceptual sophistication, breadth of knowledge, and scientific rigor.

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Section: Usage 10: Combining Shaping and Aggregatingmentioning

confidence: 99%

Molecular (moment‐to‐moment) and molar (aggregate) analyses of behavior

Shimp

2020

J Exper Analysis Behavior

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“…There have been some useful attempts at building theories of the sort that extend beyond description toward something like explanation (e.g., Catania, 2005; Killeen, 1994; McDowell, 2013; Nevin & Grace, 2000; Shimp, Herbranson, & Fremouw, 2012; Staddon, 2016). These models differ in domains, assumptions, analogs, and mathematical structures.…”

Section: Quantitative Approachesmentioning

confidence: 99%

Bounded in a nutshell: The uncertain future of EAB.

Marr¹

2018

Behavior Analysis: Research and Practice

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Discussions of the future of our field should be devoted not so much to predictions of things to come, but to what we should be doing if our field is to have a future. As reflected in much of its data, experimental analysis of behavior (EAB) has been largely in a steady-state mode and is likely to remain so for some time. This is not a "breakthrough" science; its content reflects mostly an evolution in exploration and sophistication in treatments of concepts like contingency and stimulus control. As such, there are few surprises-the overwhelming emphasis is on control, not prediction. Advances in nontrivial predictions will occur through developing more comprehensive quantitative "explanatory" models. EAB has emphasized steady-state studies with aggregate measures under quite constrained conditions, largely ignoring dynamic, moment-to moment processes such as shaping, basic to complex performance development and practice. There are long-term challenges within and outside the field that threaten EAB as we know it. Fundamental concepts typically presented in textbooks as "gospel" are being questioned, raising problems with interpreting the results of many studies, as well concerns over how best to train students. Trainers of future behavior analysts must recognize the need for gaining expertise in other fields from which we can both learn and contribute through collaboration.

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“…Once aggregates of quantitatively shaped responses are formed and statistics are computed to describe the aggregates, it becomes possible to plot conventional molar functions that show how the statistics depend on response durations and reinforcement parameters and contingencies (Anger, 1956;Galbicka & Platt, 1986;Shimp, 1968Shimp, , 1973Shimp, , 1974Shimp, , 2013Shimp, Herbranson, & Fremouw, 2012;Staddon, 1968). The literature on these results has been reviewed elsewhere (Shimp, 2013), so it suffices here just to mention that it strongly suggests that molar function depends on molecular structure (Doughty & Lattal, 2001;Shimp et al, 1994).…”

Section: Unified Empirical Functionsmentioning

confidence: 99%

“…Suppose that a theory predicts an individual's moment-to-moment or trial-to-trial behavioral variability and generates a behavior stream from which one can compute an individual's aggregate behavior over time. This type of theory usually uses computer simulation methodology (Catania, 2005;McDowell, 2013;Shimp, , 1992Shimp, Herbranson, & Fremouw, 2012;Tanno & Silberberg, 2012). Suppose another theory is a molar theory based on the idea that aggregates lead to more lawful results than moment-tomoment behavior (Baum, 2002b;Jensen et al, 2012;Neuringer, 2012;Rachlin & Laibson, 1997).…”

Section: Kinds Of Behavioral Variability and Theoretical Goalsmentioning

confidence: 99%

How Molecular, Molar, and Unified Analyses Change the Meaning ofBehavioral Variability

Shimp¹

2014

IJCP

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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.

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From Momentary Maximizing to Serial Response Times and Artificial Grammar Learning

Cited by 4 publications

References 60 publications

Molecular (moment‐to‐moment) and molar (aggregate) analyses of behavior

Molecular (moment‐to‐moment) and molar (aggregate) analyses of behavior

Bounded in a nutshell: The uncertain future of EAB.

How Molecular, Molar, and Unified Analyses Change the Meaning ofBehavioral Variability

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