Abstract: Recent studies have examined linguistic abilities in apes. However, although human mathematical abilities seem to be derived from the same foundation as those in language, we have little evidence for mathematical abilities in apes (but for exceptions see refs 7-10). In the present study, a 5-yr-old female chimpanzee (Pan troglodytes), 'Ai', was trained to use Arabic numerals to name the number of items in a display. Ai mastered numerical naming from one to six and was able to name the number, colour and object of 300 types of samples. Although no particular sequence of describing samples was required, the chimpanzee favoured two sequences (colour/object/number and object/colour/number). The present study demonstrates that the chimpanzee was able to describe the three attributes of the sample items and spontaneously organized the 'word order'.

Abstract: Animals process sensory information according to specific computational rules and, subsequently, form representations of their environments that form the basis for decisions and choices. The specific computational rules used by organisms will often be evolutionarily adaptive by generating higher probabilities of survival, reproduction, and resource acquisition. Experiments with enclosed colonies of bumblebees constrained to foraging on artificial flowers suggest that the bumblebee's cognitive architecture is designed to efficiently exploit floral resources from spatially structured environments given limits on memory and the neuronal processing of information. A non-linear relationship between the biomechanics of nectar extraction and rates of net energetic gain by individual bees may account for sensitivities to both the arithmetic mean and variance in reward distributions in flowers. Heuristic rules that lead to efficient resource exploitation may also lead to subjective misperception of likelihoods. Subjective probability formation may then be viewed as a problem in pattern recognition subject to specific sampling schemes and memory constraints.

Abstract: Three avian species, a seed-caching corvid (Clark's nutcrackers; Nucifraga columbiana), a non-seed-caching corvid (jackdaws; Corvus monedula), and a non-seed-caching columbid (pigeons; Columba livia), were tested for ability to learn to find a goal halfway between 2 landmarks when distance between the landmarks varied during training. All 3 species learned, but jackdaws took much longer than either pigeons or nutcrackers. The nutcrackers searched more accurately than either pigeons or jackdaws. Both nutcrackers and pigeons showed good transfer to novel landmark arrays in which interlandmark distances were novel, but inconclusive results were obtained from jackdaws. Species differences in this spatial task appear quantitative rather than qualitative and are associated with differences in natural history rather than phylogeny.

Abstract: REASON FOR PERFORMING STUDY: Monitoring weight of foals is a useful management practice to aid in maximising athletic potential while minimising risks associated with deviations from normal growth. OBJECTIVE: To develop predictive equations for weight, based on linear measurements of growing Thoroughbreds (TBs). METHODS: Morphometric equations predicting weight from measurements of the trunk and legs were developed from data of 153 foals. The accuracy, precision and bias of the best fitting equation were compared to published equations using a naive data set of 22 foals. RESULTS: Accuracy and precision were maximised with a broken line relating calculated volumes (V(t + l)) to measured weights. Use of the broken line is a 2 step process. V(t + l) is calculated from linear measures (m) of girth (G), carpus circumference (C), and length of body (B) and left forelimb (F). V(t + I) = ([G2 x B] + 4[C2 x F]) 4pi. If V(t + l) < 0.27 m3, weight is estimated: Weight (kg) = V(t + l) x 1093. If V(t + l) > or = 0.27 m3: Weight (kg) = V(t + l) x 984 + 24. The broken line was more accurate and precise than 3 published equations predicting the weight of young TBs. CONCLUSIONS: Estimation of weight using morphometric equations requires attention to temporal changes in body shape and density; hence, a broken line is needed. Including calculated leg volume in the broken line model is another contributing factor to improvement in predictive capability. POTENTIAL RELEVANCE: The broken line maximises its value to equine professionals through its accuracy, precision and convenience.