| Literature DB >> 23293622 |
Nicola Armstrong1, Alexis Garland, K C Burns.
Abstract
Most animals can discriminate between pairs of numbers that are each less than four without training. However, North Island robins (Petroica longipes), a food-hoarding songbird endemic to New Zealand, can discriminate between quantities of items as high as eight without training. Here we investigate whether robins are capable of other complex quantity discrimination tasks. We test whether their ability to discriminate between small quantities declines with (1) the number of cache sites containing prey rewards and (2) the length of time separating cache creation and retrieval (retention interval). Results showed that subjects generally performed above-chance expectations. They were equally able to discriminate between different combinations of prey quantities that were hidden from view in 2, 3, and 4 cache sites from between 1, 10, and 60 s. Overall results indicate that North Island robins can process complex quantity information involving more than two discrete quantities of items for up to 1 min long retention intervals without training.Entities:
Keywords: New Zealand robin; cache; field experiment; memory; number
Year: 2012 PMID: 23293622 PMCID: PMC3533374 DOI: 10.3389/fpsyg.2012.00584
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
Figure 1A robin makes his choice by pulling a flap attached to the apparatus and retrieving the contents.
Figure 2The success rate for each combination of independent variables for the three conditions. Y-axis shows the success rate as a percentage of “correct” choices (i.e., where the largest number of mealworms was selected). X-axis shows number of cache sites with bars grouped according to retention interval (0, 10, and 60 s). Indicates the percentage of successes expected by chance for each cache level. Error bars ± 1 standard error.
Single-sample .
| Trial | Mealworms | Cache sites | Time | Mean | ||
|---|---|---|---|---|---|---|
| 1 | 1v0 | 2 | 0 | 65.00 | 1.765 | 0.111 |
| 2 | 1v0 | 2 | 10 | 77.50 | 4.714 | 0.001 |
| 3 | 1v0 | 2 | 60 | 70.00 | 3.207 | 0.011 |
| 4 | 1v0 | 3 | 0 | 55.00 | 2.451 | 0.037 |
| 5 | 1v0 | 3 | 10 | 40.00 | 0.917 | 0.383 |
| 6 | 1v0 | 3 | 60 | 42.50 | 0.898 | 0.392 |
| 7 | 1v0 | 4 | 0 | 42.50 | 3.280 | 0.010 |
| 8 | 1v0 | 4 | 10 | 37.50 | 1.168 | 0.273 |
| 9 | 1v0 | 4 | 60 | 32.50 | 0.896 | 0.394 |
| 10 | 1v2 | 2 | 0 | 87.50 | 9.000 | 0.000 |
| 11 | 1v2 | 2 | 10 | 80.00 | 6.000 | 0.000 |
| 12 | 1v2 | 2 | 60 | 82.50 | 8.510 | 0.000 |
| 13 | 1v2 | 3 | 0 | 57.50 | 4.592 | 0.001 |
| 14 | 1v2 | 3 | 10 | 45.00 | 2.400 | 0.040 |
| 15 | 1v2 | 3 | 60 | 52.50 | 2.480 | 0.035 |
| 16 | 1v2 | 4 | 0 | 47.50 | 2.377 | 0.041 |
| 17 | 1v2 | 4 | 10 | 40.00 | 1.964 | 0.081 |
| 18 | 1v2 | 4 | 60 | 35.00 | 1.500 | 0.168 |
| 19 | 1v3 | 2 | 0 | 77.50 | 3.161 | 0.012 |
| 20 | 1v3 | 2 | 10 | 72.50 | 3.857 | 0.004 |
| 21 | 1v3 | 2 | 60 | 67.50 | 2.333 | 0.045 |
| 22 | 1v3 | 3 | 0 | 52.50 | 3.343 | 0.009 |
| 23 | 1v3 | 3 | 10 | 62.50 | 3.840 | 0.004 |
| 24 | 1v3 | 3 | 60 | 47.50 | 2.095 | 0.066 |
| 25 | 1v3 | 4 | 0 | 55.00 | 3.674 | 0.005 |
| 26 | 1v3 | 4 | 10 | 65.00 | 5.237 | 0.001 |
| 27 | 1v3 | 4 | 60 | 37.50 | 2.236 | 0.052 |
Results for general linear model analyses of variables 1v0 (top), 1v2 (middle), and 1v3 (bottom).
| SS | df | MS | |||
|---|---|---|---|---|---|
| Individual | 3090.278 | 9 | 343.364 | 0.405 | 0.903 |
| Cache sites (Cs) | 1335.556 | 2 | 667.778 | 0.865 | 0.438 |
| Retention interval (Ri) | 513.889 | 2 | 256.944 | 0.342 | 0.715 |
| Cs × Ri | 2069.444 | 4 | 517.361 | 0.766 | 0.554 |
| Individual | 46013.611 | 1 | 46013.611 | 89.138 | 0.313 |
| Cache sites (Cs) | 4645.833 | 9 | 516.204 | 1.570 | 0.004 |
| Retention interval (Ri) | 5293.889 | 2 | 2646.944 | 7.573 | 0.189 |
| Cs × Ri | 1430.556 | 2 | 715.278 | 1.828 | 0.896 |
| Individual | 50646.944 | 1 | 50646.944 | 135.337 | 0.567 |
| Cache sites (Cs) | 3368.056 | 9 | 374.228 | 0.964 | 0.637 |
| Retention interval (Ri) | 668.889 | 2 | 334.444 | 0.462 | 0.003 |
| Cs × Ri | 3930.556 | 2 | 1965.278 | 8.035 | 0.601 |
SS, sums-of-squares; df, degrees of freedom; MS, mean squares; .