Abstract numerical discrimination learning in rats

Abstract

In this study, we examined rats' discrimination learning of the numerical ordering positions of objects. In Experiments 1 and 2, five out of seven rats successfully learned to respond to the third of six identical objects in a row and showed reliable transfer of this discrimination to novel stimuli after being trained with three different training stimuli. In Experiment 3, the three rats from Experiment 2 continued to be trained to respond to the third object in an object array, which included an odd object that needed to be excluded when identifying the target third object. All three rats acquired this selective-counting task of specific stimuli, and two rats showed reliable transfer of this selective-counting performance to test sets of novel stimuli. In Experiment 4, the three rats from Experiment 3 quickly learned to respond to the third stimulus in object rows consisting of either six identical or six different objects. These results offer strong evidence for abstract numerical discrimination learning in rats.

Rats respond where it counts

“…In both Experiments 1 and 2, rats chose the third box on 60 %-80 % of the test trials. These results convincingly showed that rats did show abstraction by transferring numerical discrimination to a novel set of objects.Having shown clear evidence for abstract numerical transfer, Taniuchi et al (2016) report some truly remarkable findings from Experiments 3 and 4. The two successful rats from Experiment 2 continued to be tested in Experiment 3 with the four types of objects used in that experiment-call them A, B, C, and D. Although the rats had always been tested on homogeneous sets, such as AAAA, BBBB, CCCCC, or DDDDDD, they were now tested on probe trials that contained an odd item.…”

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Summary Taniuchi, Sugihara, Wakashima, and Kamijo (2016) report the surprising finding that rats can transfer numerical discrimination to novel objects. Further experiments show that rat numerical discrimination is flexible, as it can both count homogeneous and heterogeneous objects and omit an odd object.

Keywords Numerical processing .

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“…The object at a fixed ordinal number from one side of the row is always the correct one, and the question of interest is whether an animal can learn to reliably choose that object. In an article in Learning & Behavior, Taniuchi, Sugihara, Wakashima, and Kamijo (2016) looked for the property of abstraction. Humans use numbers to count virtually any number of objects.…”

“…Even after many sessions of training, rats still make errors. Interestingly, Taniuchi et al (2016) found that these errors were usually at locations near the correct location.…”

“…Had Taniuchi et al (2016) further tested their rats at higher numbers of objects, say five or seven objects, scalar variability predicts that an increasing dispersion of errors would be found as the number of objects increased. In fact, this phenomenon has been found in a number of experiments studying numerical discrimination in animals and preverbal children.…”