Serendipity in Animal Experimentation: Examples from Duration Scaling in Rats

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" Serendipity in Animal Experimentation: Examples from Duration Scaling in Rats "
Eisler, Hannes

Document Type	:	AL
Record Number	:	935272
Doc. No	:	LA9d59d6c4
Language of Document	:	English
Main Entry	:	Eisler, Hannes
Title & Author	:	Serendipity in Animal Experimentation: Examples from Duration Scaling in Rats [Article]\ Eisler, Hannes
Title of Periodical	:	International Journal of Comparative Psychology
Volume/ Issue Number	:	3/2
Date	:	1989
Abstract	:	In the scaling experiment proper, 8 rats had to reproduce 10 randomly presented time intervals ranging from 1.3 to 20 s. The beginning of the reproduction was separated from the end of the standard by a 300 ms interruption of the sound indicating the durations. The rat determined the length of the reproduction by pressing a lever, thereby terminating the sound. The scaling model and the final result of the experiment are briefly described. However, before this final phase of the experiment, the rats had to learn to attend to the interruption. This was achieved in Phase 2 of the experiment by defining lever presses during presentation of the standard as incorrect behavior, which was penalized by withholding the reward and lengthening the actual standard duration by 4 s. Scrutiny of the Phase 2 data revealed two unexpected— serendipitous—findings. 1. The rats learned that they had committed an error (a faulty lever press during the standard) before they learned to suspend their lever presses; shorter response latencies for to-be-rewarded than for not-to-be-rewarded trials clearly indicated: ''Knowing before doing.'' 2. A study of the distribution of first lever presses during the standard showed (a) that these lever presses were not evenly distributed (the hypothesis of a negative exponential distribution was rejected), and, more interestingly, (b) that 7 of the 8 rats hardly ever pressed the lever during a certain interval (for 5 of the rats the interval 3.3-4.5 s): ''Temporal holes in the latency distributions.'' It pays to look not only at data when learning has been accomplished, but also during acquisition!