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SOS! An algorithm and software for the stochastic optimization of stimuli.

Blair C Armstrong¹, Christine E Watson, David C Plaut

¹Department of Psychology and Center for the Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. blairarm@andrew.cmu.edu

Behavior Research Methods

|February 22, 2012

Summary

Researchers can now automate stimulus selection for experiments using the Stochastic Optimization of Stimuli (SOS) algorithm. This tool enhances experimental validity and saves researchers time by optimizing item sets based on defined constraints.

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Area of Science:

Cognitive Science
Psychological Methods
Computational Psychology

Background:

Experimental stimuli characteristics critically influence theoretical question scope.
Manual stimulus selection is time-consuming and lacks methodological support.
Optimizing stimulus sets is crucial for experimental validity.

Purpose of the Study:

Introduce SOS, an algorithm and software package for stochastic optimization of stimuli.
Provide researchers with a tool to automate and optimize stimulus selection.
Enhance the rigor and efficiency of experimental design.

Main Methods:

Formalized a manual stimulus selection heuristic into a stochastic relaxation search algorithm.
Developed a software package (SOS) for implementing the optimization.
Defined a vocabulary of constraints for optimal stimulus set selection.

Main Results:

SOS rapidly and reliably selects optimal stimulus subsets satisfying experimental constraints.
The algorithm facilitates assessment and maximization of internal and external validity.
Demonstrated efficacy through a case study and Monte Carlo simulations.

Conclusions:

SOS automates stimulus selection, freeing researchers to focus on theoretical questions.
The tool supports various experimental designs, including factorial and regression models.
SOS enhances the internal and external validity of experimental items, improving research quality.