Mixed model on few individuals

Dear all,

First, I will try to be the most comprehensible, if not, do not hesitate to
ask for more information.

I am working on a project with collaborators, and I am trying to analyse
the data. For one question, however, I do not feel confident with my
method, but I do not know how I can analyse the data in a different way.

We are working on visual fixation of a prey while hunting, with a new
method of high accuracy. This method is very interesting but implies that
we cannot use many individuals (because of different things that are not of
interest here I think).

My major problem here is that I have "only" 3 individuals and 51 trials
(almost equilibrate per individual).
I also have 3 different conditions (i.e. 3 types of prey). All these
conditions have been repeated for the 3 individuals.
In total, the dataset is relatively large as I have more than 6000 points
(large for behavioural study), but, as I wrote, with only 3 individuals.

Regarding this type of dataset, I am wondering whether I can use mixed
model to compare the visual fixation according to the different conditions.
So here are my two questions :

1) Can I use mixed models with only 3 individuals? If not, do you know if I
can use another model? Or should I compare each individual independently?

2) My data are spatio-temporal (visual fixation in times: One Azimuth and
one elevation every X sec). How can I implement this autocorrelation in my
model?

I am sincerely grateful if someone can be of any help. Sorry If similar
question has been posted before, I did not find it.

Best regards,

Simon Potier