Imagine an experiment like this:
A participant is asked to optimize an unknown function (let's say minimize) . On each trial the participant provides several input values, and receives an output value. Now also imagine that the output is noisy, in that the same inputs lead to an output plus a random component.
To think of but one of many possible specific examples imagine the following function
$$Y = (X -3)^2 + (Z-2)^2 + (W-4)^2 + e,$$
where $e$ is normally distributed, mean = 0, sd = 3.
On each trial, the participant would provide a value for $X$, $Z$, and $W$. And they would obtain a $Y$ value based on this underlying function. Their aim would be to minimise the value of $Y$. They have not been told the underlying functional form. They only know that there is a global minimum and that there is a random component.
I'm interested in reading about the strategies used by humans to do this task in experimental settings. Note I'm not directly interested in how computers do the task or how programmers and mathematicians might complete this task.
- What are some good references for learning about the literature on how humans learn to optimize noisy multi-variable functions?
- What are some of the key findings on how humans optimize noisy multi-variable functions?