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The natural mathematics of behavior analysis
Author(s) -
Li Don,
Hautus Michael J.,
Elliffe Douglas
Publication year - 2018
Publication title -
journal of the experimental analysis of behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.75
H-Index - 61
eISSN - 1938-3711
pISSN - 0022-5002
DOI - 10.1002/jeab.330
Subject(s) - event (particle physics) , computer science , markov chain , multivariate statistics , variable (mathematics) , set (abstract data type) , bayesian probability , markov chain monte carlo , artificial intelligence , statistics , mathematics , machine learning , mathematical analysis , physics , quantum mechanics , programming language
Models that generate event records have very general scope regarding the dimensions of the target behavior that we measure. From a set of predicted event records, we can generate predictions for any dependent variable that we could compute from the event records of our subjects. In this sense, models that generate event records permit us a freely multivariate analysis. To explore this proposition, we conducted a multivariate examination of Catania's Operant Reserve on single VI schedules in transition using a Markov Chain Monte Carlo scheme for Approximate Bayesian Computation. Although we found systematic deviations between our implementation of Catania's Operant Reserve and our observed data (e.g., mismatches in the shape of the interresponse time distributions), the general approach that we have demonstrated represents an avenue for modelling behavior that transcends the typical constraints of algebraic models.