Iterative Science Experiments

In my computational modeling class on Friday, Dr. Palmeri discussed a type of iterative experimentation put forth by Jay Myung & Mark Pitt at Ohio State.

The basic work of the course is models of the mind that make predictions of human behavior. Specifically, at this point we are looking at when there are competing models and researchers wish to distinguish which is the best able to produce realistic data.

The example being used was the discrimination of memory retention models and the time points at which to test for retention so as to optimally distinguish between models. The process starts off and as data is collected, best-fitting values for the parameters of the models can be calculated.

Given those parameters then, there are memory retention test points where the competing models will be expected to give the most divergent predictions. The data collection algorithm then can adjust the experimental setup to collect at those points.

The data from those points, is then added to the dataset that distinguishes the best-fitting parameters and the process iterates again.

Essentially, the experimenter is an AI that is mathematically tuning the experiment in real time to maximize the probability of a conclusive finding. Science is awesome, isn’t it?

Leave a Reply

Your email address will not be published. Required fields are marked *