Here's (at least most of) a solution with `MCMCglmm`.

First fit the equivalent intercept-variance-only model with `MCMCglmm`:

    library(MCMCglmm)
    primingHeid.MCMCglmm = MCMCglmm(fixed=RT ~ RTtoPrime * ResponseToPrime + Condition, 
                                    random=~Subject+Word, data = primingHeid)

Comparing fits between `MCMCglmm` and `lmer`, first retrieving my hacked version of `arm::coefplot`:

    source(url("http://www.math.mcmaster.ca/bolker/R/misc/coefplot_new.R"))
      ## combine estimates of fixed effects and variance components
    pp  <- as.mcmc(with(primingHeid.MCMCglmm, cbind(Sol, VCV)))
      ## extract coefficient table
    cc1 <- coeftab(primingHeid.MCMCglmm,ptype=c("fixef", "vcov"))
      ## strip fixed/vcov indicators to make names match with lmer output
    rownames(cc1) <- gsub("(Sol|VCV).", "", rownames(cc1))
      ## fixed effects -- v. similar
    coefplot(list(cc1[1:5,], primingHeid.lmer))
      ## variance components -- quite different.  Worth further exploration?
    coefplot(list(cc1[6:8,], coeftab(primingHeid.lmer, ptype="vcov")),
             xlim=c(0,0.16), cex.pts=1.5)
    
Now try it with random slopes:

    primingHeid.rs.MCMCglmm = MCMCglmm(fixed=RT ~ RTtoPrime * ResponseToPrime + Condition,
                                       random=~Subject+Subject:Condition+Word, 
                                       data = primingHeid)        
    summary(primingHeid.rs.MCMCglmm)

This does give some sort of "MCMC p-values" ... you'll have to explore for yourself and see whether the whole thing makes sense ...