Problem with repeated-measures GLMM in SPSS for psycholinguistic study: Failure to converge

#1
I'm trying to run a generalized linear mixed model in SPSS, with a continuous response variable (ResponseTime), SubjectID as a random factor, Items as a repeated measure, and five fixed factors: three categorical subject variables (Sex, Handedness, Familial Handedness) and two categorical item variables (Prime Type, Probe Latency), and one continuous covariate (Probe Size-not a variable of interest). I think the problem I'm having may have to do with specifying the random effects structure and/or data structure, but after reading everything I can find on it, nothing has helped and I'm at my wit's end. The main error message I get is this one:

glmm: The maximum number of iterations was reached but convergence was not achieved. Output for the last iteration is displayed. The procedure continues despite this warning. Subsequent results produced are based on the last iteration. Validity of the model fit is uncertain.

I am also sometimes getting this error:

glmm: The final Hessian matrix is not positive definite although all convergence criteria are satisfied. The procedure continues despite this warning. Subsequent results produced are based on the last iteration. Validity of the model fit is uncertain.

I have tried increasing the number of iterations, but to no avail; it just seems to exacerbate another problem I'm having, which is that SPSS seems to run forever (sometimes hours) when I use the repeated measures command. This seems to be a problem with the software (https://www.ibm.com/support/pages/r...runs-days-and-have-force-quit-application-mac). I don't mind waiting hours for the model to run, but I'd like to solve the nonconvergence error problem first. ANY ADVICE WOULD BE MUCH APPRECIATED!!!

Currently I have the following syntax:
GENLINMIXED /DATA_STRUCTURE SUBJECTS=SubjID REPEATED_MEASURES=Item /FIELDS TARGET=ResponseTime TRIALS=NONE OFFSET=NONE /TARGET_OPTIONS DISTRIBUTION=INVERSE_GAUSSIAN LINK=IDENTITY /FIXED EFFECTS=Sex Handedness FamilialHandedness PrimeType ProbeLatency Probesize PrimeTypeProbeLatency FamilialHandednessECType FamilialHandednessProbeLatency FamilialHandednessPrimeTypeProbeLatency HandednessPrimeType HandednessProbeLatency HandednessPrimeType*ProbeLatency USE_INTERCEPT=TRUE /RANDOM USE_INTERCEPT=TRUE COVARIANCE_TYPE=VARIANCE_COMPONENTS SOLUTION=FALSE /BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=ASCENDING
 
#2
I think you have too many variables. Roughly you need to have 50 responses for each variable you want to include. Do you have 400 responses? I would try being more selective about the items you include based on your theoretical assumptions. What do you think is the most likely thing to affect response time? Is familial handedness likely to be a key predictor? Maybe you could reduce the weights on some of the less likely ones.