repeated measures ANOVA

Dear list members:

I have the following data:
group <- rep(rep(1:2, c(5,5)), 3)
time <- rep(1:3, rep(10,3))
subject <- rep(1:10, 3)
p.pa <- c(92, 44, 49, 52, 41, 34, 32, 65, 47, 58, 94, 82, 48, 60, 47,
46, 41, 73, 60, 69, 95, 53, 44, 66, 62, 46, 53, 73, 84, 79)
P.PA <- data.frame(subject, group, time, p.pa)

The ten subjects were randomly assigned to one of two groups and
measured three times. (The treatment changes after the second time
point.)

Now I am trying to find out the most adequate way for an analysis of
main effects and interaction. Most social scientists would call this
analysis a repeated measures ANOVA, but I understand that mixed- 
effects
model is a more generic term for the same analysis. I did the analysis
in four ways (one in SPSS, three in R):

1. In SPSS I used "general linear model, repeated measures",  
defining a
"within-subject factor" for the three different time points. (The data
frame is structured differently in SPSS so that there is one line for
each subject, and each time point is a separate variable.)
Time was significant.

2. Analogous to what is recommended in the first chapter of Pinheiro &
Bates' "Mixed-Effects Models" book, I used
library(nlme)
summary(lme ( p.pa ~ time * group, random = ~ 1 | subject))
Here, time was NOT significant. This was surprising not only in
comparison with the result in SPSS, but also when looking at the  
graph:
interaction.plot(time, group, p.pa)

3. I then tried a code for the lme4 package, as described by Douglas
Bates in RNews 5(1), 2005 (p. 27-30). The result was the same as in 2.
library(lme4)
summary(lmer ( p.pa ~ time * group + (time*group | subject), P.PA ))

4. The I also tried what Jonathan Baron suggests in his "Notes on the
use of R for psychology experiments and questionnaires" (on CRAN):
summary( aov ( p.pa ~ time * group + Error(subject/(time * group)) ) )
This gives me yet another result.

So I am confused. Which one should I use?

Thanks

Christian