Validity of reporting correlation with N = 4 Experimental Design
4 Animals repeated a binary choice task with 60 trials over 15 days. For each day, we calculated the proportion for which the animal chose the given 'correct' stimulus. In addition to this choice data, we have a single continuous behavioral measure for each animal (4 data points). We wish to relate this behavioral measure with the animals' overall proportion of 'correct' choices. You can think of this variable as a characteristic of each animal, such as its sex or gene variant, etc.  
Initial Analysis
We initially calculated the mean proportion for each animal across all days and calculated the Pearson Correlation of the mean proportion with the behavioral measure (-.99 [-.999,-.3160], 95% CI); however, a reviewer commented on how this was meaningless as we have such a small N. 
Question: What other method(s) should we utilize to demonstrate this relationship?

Data
As requested by whuber, the data is as follows:
 means   behavior
 0.672         13
 0.637         21
 0.596         23
 0.513         39

As per my paranoia, the numbers are changed quite minimally. This should not alter any of the interpretations.
 A: The reviewer is incorrect.  It's not very generalizable or strong evidence but it's also not entirely meaningless.  The reviewer may be referring to the fact that the distribution of correlations is nearly flat when the N is 4 and the true correlation is 0.  Any correlation is about equally likely as any other.  So, from that standpoint it looks meaningless.  But if you've got an argument that your correlation should be negative from the literature or logic then you're not on all that shaky a ground.  Furthermore, if you're arguing against a theory with a positive correlation you have a pretty strong set of data.  It depends alot on what you're arguing.
Make sure that your conclusions are adequately circumspect in recognition of the weaknesses here.
The method you've used to calculate your CI suggests r > -0.30 is unlikely.  If the reviewer doesn't like the method you used to assess that then let them argue that.  There are better and worse methods for getting the CI but they can't just ignore your statistics and ignore the magnitude of the effect.  You might consider that the achieved power here is 0.79 (test against 0 using a Fisher's Z transform method).  That's relatively good for behavioural science.  But keep in mind that power estimate assumes you've got a good estimate of the true value.  Very likely you've over estimated.  In that case the power gets substantially lower very quickly.  At a correlation of -0.95 the power is only 0.5.
