Rilling, M. E., & Neiworth, J. J. (1991). How animals use images. Sci Prog, 75(298 Pt 3-4), 439–452.
Abstract: Animal cognition is a field within experimental psychology in which cognitive processes formerly studied exclusively with people have been demonstrated in animals. Evidence for imagery in the pigeon emerges from the experiments described here. The pigeon's task was to discriminate, by pecking the appropriate choice key, between a clock hand presented on a video screen that rotated clockwise with constant velocity from a clock hand that violated constant velocity. Imagery was defined by trials on which the line rotated from 12.00 o'clock to 3.00 o'clock, then disappeared during a delay, and reappeared at a final stop location beyond 3.00 o'clock. After acquisition of a discrimination with final stop locations at 3.00 o'clock and 6.00 o'clock, the evidence for imagery was the accurate responding of the pigeons to novel locations at 4.00 o'clock and 7.00 o'clock. Pigeons display evidence of imagery by transforming a representation of movement that includes a series of intermediate steps which accurately represent the location of a moving stimulus after it disappears.
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Bloom, P. (2004). Behavior. Can a dog learn a word? Science, 304(5677), 1605–1606.
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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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Mulcahy, N. J., & Call, J. (2006). Apes save tools for future use. Science, 312(5776), 1038–1040.
Abstract: Planning for future needs, not just current ones, is one of the most formidable human cognitive achievements. Whether this skill is a uniquely human adaptation is a controversial issue. In a study we conducted, bonobos and orangutans selected, transported, and saved appropriate tools above baseline levels to use them 1 hour later (experiment 1). Experiment 2 extended these results to a 14-hour delay between collecting and using the tools. Experiment 3 showed that seeing the apparatus during tool selection was not necessary to succeed. These findings suggest that the precursor skills for planning for the future evolved in great apes before 14 million years ago, when all extant great ape species shared a common ancestor.
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Janik, V. M. (2000). Whistle matching in wild bottlenose dolphins (Tursiops truncatus). Science, 289(5483), 1355–1357.
Abstract: Dolphin communication is suspected to be complex, on the basis of their call repertoires, cognitive abilities, and ability to modify signals through vocal learning. Because of the difficulties involved in observing and recording individual cetaceans, very little is known about how they use their calls. This report shows that wild, unrestrained bottlenose dolphins use their learned whistles in matching interactions, in which an individual responds to a whistle of a conspecific by emitting the same whistle type. Vocal matching occurred over distances of up to 580 meters and is indicative of animals addressing each other individually.
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Thornton, A., & McAuliffe, K. (2006). Teaching in wild meerkats. Science, 313(5784), 227–229.
Abstract: Despite the obvious benefits of directed mechanisms that facilitate the efficient transfer of skills, there is little critical evidence for teaching in nonhuman animals. Using observational and experimental data, we show that wild meerkats (Suricata suricatta) teach pups prey-handling skills by providing them with opportunities to interact with live prey. In response to changing pup begging calls, helpers alter their prey-provisioning methods as pups grow older, thus accelerating learning without the use of complex cognition. The lack of evidence for teaching in species other than humans may reflect problems in producing unequivocal support for the occurrence of teaching, rather than the absence of teaching.
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Pennisi, E. (2006). Animal cognition. Social animals prove their smarts (Vol. 312).
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Subiaul, F., Cantlon, J. F., Holloway, R. L., & Terrace, H. S. (2004). Cognitive imitation in rhesus macaques. Science, 305(5682), 407–410.
Abstract: Experiments on imitation typically evaluate a student's ability to copy some feature of an expert's motor behavior. Here, we describe a type of observational learning in which a student copies a cognitive rule rather than a specific motor action. Two rhesus macaques were trained to respond, in a prescribed order, to different sets of photographs that were displayed on a touch-sensitive monitor. Because the position of the photographs varied randomly from trial to trial, sequences could not be learned by motor imitation. Both monkeys learned new sequences more rapidly after observing an expert execute those sequences than when they had to learn new sequences entirely by trial and error.
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Pennisi, E. (1999). Are out primate cousins 'conscious'? (Vol. 284).
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Rapin, V., Poncet, P. A., Burger, D., Mermod, C., & Richard, M. A. (2007). [Measurement of the attention time in the horse]. Schweiz Arch Tierheilkd, 149(2), 77–83.
Abstract: A study carried out on 49 horses showed that it is possible to measure the attention time by operant conditioning. After teaching horses an instrumental task using a signal, we were then able to test their attention time by asking them to prolong it increasingly while setting success and failure criteria. Two tests were performed 3 weeks apart. The 2nd test was feasible without relearning, a proof of memory, and was repeatable, a proof of consistency in the attention time. A significant difference was observed between the 3 age groups. Young horses often performed very well during the 1st test but their attention dropped in the 2nd test while older horses were more stable with respect to attention and even increased it slightly. The study shows that there are individual differences but it was not possible to prove a significant influence of breed, gender and paternal influence. Consequently, learning appears to be one of the most interesting approaches for evaluating the attention of horses and for observing their behaviour.
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