What type of factors can be used as random effects in mixed linear regression? I am working on a picture naming task. Subjects named pictures as quickly as possible. Some images appear more times, while others appear once or twice. Therefore, we include the number of times the image is present as a fixed effect. I was told to use the trail ID as a random effect instead of the image ID.
For example: subject saw the images in this sequence: Pic A, Pic E, Pic B, Pic A, Pic C.... The Trail ID is 1, 2, 3, 4, 5.... As you can see, the number of trail ID equals the number of observations.
I would like to know why the trail ID is used as a random effect, is this a good approach? Or does it depend on the influence of the trail sequence, e.g. if Pic A comes before Pic D, then the influence might be different than Pic E that comes before Pic D?