A Multi-Objective Optimization Algorithm to Generate Unbiased Stimuli Sequences for Cognitive Tasks

Cognitive scientists want to ensure that particular cognitive tasks target particular cognitive functions that can be mapped to stable neural markers. Numerous cognitive tasks, like the n-back, involve generating sequence of trials which satisfy certain statistical properties.The common approach to generate these sequences however lacks a theoretical framework and induces unintentional structure in the sequences which affects both behavioral performance and might bias the people’s cognitive strategies when completing a task. For example, people might exploit local properties in a random sequence in their decision making process. We argue that optimized experimental design requires cognitive tasks to be served by stimulus sequence generators that satisfy multiple constraints, both at the global and at the local structures of the sequence and that these sequence properties need to be systematically incorporated in the behavioral data analysis pipeline. We then develop a framework to reformulate the sequence generation process as a compositional soft constraint satisfaction problem and offer a multi-objective, genetic-algorithm-based method to generate controlled sequences under behavioral and neural constraints. This approach provides a systematic and coherent framework to handle stimulus sequences which in turn will impact the insights that can be gained from the behavioral and neural data collected on people performing cognitive tasks using those sequences.