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Topál, J., Byrne, R. W., Miklósi, Á., & Csányi, V. (2006). Reproducing human actions and action sequences: “Do as I Do!” in a dog. Anim. Cogn., 9(4), 355–367.
Abstract: We present evidence that a dog (Philip, a 4-year-old tervueren) was able to use different human actions as samples against which to match his own behaviour. First, Philip was trained to repeat nine human-demonstrated actions on command ('Do it!'). When his performance was markedly over chance in response to demonstration by one person, testing with untrained action sequences and other demonstrators showed some ability to generalise his understanding of copying. In a second study, we presented Philip with a sequence of human actions, again using the 'Do as I do' paradigm. All demonstrated actions had basically the same structure: the owner picked up a bottle from one of six places; transferred it to one of the five other places and then commanded the dog ('Do it!'). We found that Philip duplicated the entire sequence of moving a specific object from one particular place to another more often than expected by chance. Although results point to significant limitations in his imitative abilities, it seems that the dog could have recognized the action sequence, on the basis of observation alone, in terms of the initial state, the means, and the goal. This suggests that dogs might acquire abilities by observation that enhance their success in complex socio-behavioural situations.
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Huber, L., & Gajdon, G. K. (2006). Technical intelligence in animals: the kea model. Anim. Cogn., 9(4), 295–305.
Abstract: The ability to act on information flexibly is one of the cornerstones of intelligent behavior. As particularly informative example, tool-oriented behavior has been investigated to determine to which extent nonhuman animals understand means-end relations, object affordances, and have specific motor skills. Even planning with foresight, goal-directed problem solving and immediate causal inference have been a focus of research. However, these cognitive abilities may not be restricted to tool-using animals but may be found also in animals that show high levels of curiosity, object exploration and manipulation, and extractive foraging behavior. The kea, a New Zealand parrot, is a particularly good example. We here review findings from laboratory experiments and field observations of keas revealing surprising cognitive capacities in the physical domain. In an experiment with captive keas, the success rate of individuals that were allowed to observe a trained conspecific was significantly higher than that of naive control subjects due to their acquisition of some functional understanding of the task through observation. In a further experiment using the string-pulling task, a well-probed test for means-end comprehension, we found the keas finding an immediate solution that could not be improved upon in nine further trials. We interpreted their performance as insightful in the sense of being sensitive of the relevant functional properties of the task and thereby producing a new adaptive response without trial-and-error learning. Together, these findings contribute to the ongoing debate on the distribution of higher cognitive skills in the animal kingdom by showing high levels of sensorimotor intelligence in animals that do not use tools. In conclusion, we suggest that the 'Technical intelligence hypothesis' (Byrne, Machiavellian intelligence II: extensions and evaluations, pp 289-211, 1997), which has been proposed to explain the origin of the ape/monkey grade-shift in intelligence by a selection pressure upon an increased efficiency in foraging behavior, should be extended, that is, applied to some birds as well.
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Blaisdell, A. P., Sawa, K., Leising, K. J., & Waldmann, M. R. (2006). Causal reasoning in rats. Science, 311(5763), 1020–1022.
Abstract: Empirical research with nonhuman primates appears to support the view that causal reasoning is a key cognitive faculty that divides humans from animals. The claim is that animals approximate causal learning using associative processes. The present results cast doubt on that conclusion. Rats made causal inferences in a basic task that taps into core features of causal reasoning without requiring complex physical knowledge. They derived predictions of the outcomes of interventions after passive observational learning of different kinds of causal models. These competencies cannot be explained by current associative theories but are consistent with causal Bayes net theories.
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