Since individual training examples affect multiple memory locations, we use a simple technique for retrieving from memory when deciding whether to shoot or to pass. We round to the nearest for which Mem[ ] is defined, and then take as the value of . Thus, each Mem[ ] represents for . Notice that retrieval is much simpler when using this technique than when using kNN or kernel regression: we look directly to the closest fixed memory position, thus eliminating the indexing and weighting problems involved in finding the k closest training examples and (possibly) scaling their results. We used this retrieval technique throughout our experiments, concentrating the trickiness of our function learning at storage time.