[R-lang] Mixed model for eyetracking data anlaysis

[Jeonghwa Shin]

Dear ling-r-lang users,

I'm writing to get some advice on the use of logit mixed model for
eyetracking data anlaysis. My experiment has three IVs with two levels for
each - language (English vs.Korean),stress pattern (trochaic vs.  iambic),
phonation type (aspirated vs.lax), and  one continuous IV, time. And the
DV is binary, either 0 or 1. What I want in the test is where in the time
course of word recognition, the trochaic and iambic words are different in
their activation of the target words, and how they are interacting with
the phonation types in the two language groups.

For this, I first tried logit mixed effect model as below.

lmer(gaze~stress*lg*phonation*time+(1|subj)+(1|item), data
,family=="binomial")

The issue that I had with this model is that it doesn't show interactions
between specific levels of factors. For example, I couldn't test whether
English speakers' behavior for aspiraed trochaic words (default level) is
different from the one for aspirated iambic ones.

So I have made a dummy combinatorial variable column, "int", which
combines the levels of lg, stress pattern, and phontion type (e.g.,
"eiasp" as a combination of English spk's respose for aspirated iambic
words) and ran the model as below:

lmer(gaze~int*time+(1|subj)+(1|item), data, family=="binomial")

My question is whether having such a dummy combinatorial variable is a
legitimate for the mixed effect model. If it's not legitimate, I'd like to
know what's the way to examine the interactions between specified levels
of different factors of interest.

My another question is how can we test where in the timecourse the two
levels of interest are sigificantly different from each other (in terms of
slope change). For this , I have segmented time into every 100ms window
and treated the window as a factor. It looks the outcome supports slope
change in plot (in logit) and compares two levels of interest in each time
window. But again, I'm not sure whether this is a right way to examine the
time effect. If not, what model or approach do I have to make?

My questions might have been arisen by my misunderstanding of the model,
so it would be greatly appreaciated if you would be able to give me your
valuable advice.

Thank you!

Best regards,
Jeonghwa Shin