R-S and S(-O)-R: alternative designs for neural networks

Article Abstract:

A commentary addressing Donahoe, Palmer, and Burgos's argument of using computational techniques for evaluating a neural network model based on the S-O-R logic, but disclosing the idea of S-R views, presented some ideas that clearly pointed out its compatibility with both S-R and S-O-R views. Analysis shows that the approach of Donahoe et al used both R-S and S(-O)-R in behavioral law and biological mechanism levels of analysis. Although, neural network simulations are of no accountable use in behavior analysis, the idea of a selectionist neural network in analyzing behavior was introduced.

Models, Analysis, Operant behavior, Operant conditioning

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Replacing relative reinforcing efficacy with behavioral economic demand curves

Article Abstract:

Relative reinforcing efficacy refers to the behavior-strengthening or maintaining property of a reinforcer when compared to that of another reinforcer. Reinforcing efficacy is a homogenous construct that refers to a reinforcer's ability to maintain behavior, therefore, different measures of reinforcing efficacy should agree when ranking reinforcers, including drugs, by their ability to maintain behavior.

Science & research, Psychological aspects, Smokers, Behavioral assessment, Behavioural assessment

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Situational descriptions of behavioral procedures: the In Situ testbed

Article Abstract:

The In Situ testbed, developed to evaluate computational models of learning, appears to be a reliable procedure for comparing learning models. This testbed models reinforcement contingencies and is applicable to artificial neural networks.

Statistical Data Included, Computer simulation

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