While foraging or during social interactions, animals may benefit from judging relative quantity. Individuals may select larger prey or a patch with more food and likewise, it may pay to track the number and type of individuals and social interactions. We tested for spontaneous quantity discrimination in the gidgee skink (Egernia stokesii), a family-living lizard. Lizards were presented with food quantities differing in number or size and selected the larger quantity of food items when they differed in number, but not when items differed in size. We show, for the first time, superior spontaneous discrimination of items differing in number over size in a lizard species which contrasts with previous findings. Our simple method, however, did not include controls for the use of continuous quantities and further tests are required to determine the role of such information during quantity discrimination. Our results provide support for use of the parallel individuation system for the discrimination of small quantities (£ 4 items). Lizards might, however, still use the approximate number system if items in larger quantities (> 4) are presented. Overall, we uncovered evidence that species might possess specific cognitive abilities potentially adapted to their niche in respect to quantity information (discrete and/or continuous) and the processing system used when judging quantities. Importantly, our results highlight the need for testing multiple species using similar testing procedures to gain a better understanding of the underlying causes leading to differences across species.

Data were collected during a spontanious two choice quantity discrimination task. Lizards were presented with two options of quantities (number of food items and differently sized food items) and their first choice was recorded. Lizards were not rewarded for choices to prevent learning. All data manipulation is done in R. The provided data set represents the most raw unchanged form of the collected data.

A read me data file is provided which explains every variable in the dataset.