post-hoc tests of lmerTest models using lsmeans

Dear R experts,

  I have a question on testing simple effects after finding out
significant interaction using lmerTest. My data has two within-subjects
fixed factors: Movement (synchronous or asynchronous), and Finger (one
finger, or four fingers). The two levels of Movement factor were
dummy-coded as 0.5 and -0.5, so were the two levels of Finger factor. The
DV is called Shift. My model looks like this:

  Shift.lm <- lmer(Shift~Movement*Finger+(1|subject), data=data)

  There was a significant interaction of Movement and Finger, so I wanted
to test simple effects by comparing synchronous and asynchronous movements
at each level of Finger. I was planning to use lsmeans to do this, but I
got two questions. First, when I just typed > lsmeans(Shift.lm) to look at
descriptive info in each condition, R output nothing other than the headers:

  > lsmeans(Shift.lm)

Least Squares Means table:
     Estimate Standard Error DF t-value Lower CI Upper CI p-value

  In addition, I typed the command below to test simple effects, but
getting this error message:

lsmeans(Shift.lm, pairwise~Finger * Movement ,adjust="tukey")

Error in match(x, table, nomatch = 0L) :
  'match' requires vector arguments

  I don't know what I am missing here, I also don't know if lsmeans is the
best way to do it. Could anyone give any suggestions?

Thank you for your help,
Yuqi

        [[alternative HTML version deleted]]