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Lewis, K. P., Jaffe, S., & Brannon, E. M. (2005). Analog number representations in mongoose lemurs (Eulemur mongoz): evidence from a search task. Anim. Cogn., 8(4), 247–252.
Abstract: A wealth of data demonstrating that monkeys and apes represent number have been interpreted as suggesting that sensitivity to number emerged early in primate evolution, if not before. Here we examine the numerical capacities of the mongoose lemur (Eulemur mongoz), a member of the prosimian suborder of primates that split from the common ancestor of monkeys, apes and humans approximately 47-54 million years ago. Subjects observed as an experimenter sequentially placed grapes into an opaque bucket. On half of the trials the experimenter placed a subset of the grapes into a false bottom such that they were inaccessible to the lemur. The critical question was whether lemurs would spend more time searching the bucket when food should have remained in the bucket, compared to when they had retrieved all of the food. We found that the amount of time lemurs spent searching was indicative of whether grapes should have remained in the bucket, and furthermore that lemur search time reliably differentiated numerosities that differed by a 1:2 ratio, but not those that differed by a 2:3 or 3:4 ratio. Finally, two control conditions determined that lemurs represented the number of food items, and neither the odor of the grapes, nor the amount of grape (e.g., area) in the bucket. These results suggest that mongoose lemurs have numerical representations that are modulated by Weber's Law.
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Czeschlik, T. (1998). Animal cognition – the phylogeny and ontogeny of cognitive abilities. Anim. Cogn., 1(1), 1–2.
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Griffin, A. S., Tebbich, S., & Bugnyar, T. (2017). Animal cognition in a human-dominated world. Anim. Cogn., 20(1), 1–6.
Abstract: In the USA, each year, up to one billion birds are estimated to die from colliding with windowpanes (Sabo et al. 2016). A further 573,000 are struck down by wind turbines, along with 888,000 bats (Smallwood 2013). Worldwide, unintended capture in fishing devices is recognized as the single most serious global threat to migratory, long-lived marine taxa including turtles, birds, mammals and sharks (Wallace et al. 2013). Estimates put the number of amphibians killed per year on Australian roads at 5 million (Seiler 2003). The likelihood of a green turtle erroneously ingesting plastic debris, often by mistaking them for food, rose from 30% in 1985 to almost 50% in 2012 (Schuyler et al. 2013). Human-induced rapid environmental change (HIREC, sensu Sih et al. 2011) is filling animals’ environments with new threats which bear little or excessive similarity to those they have encountered in their evolutionary history (Dwernychuk and Boag 1972; Patten and Kelley 2010; Witherington 1997). As a consequence, many of the stimuli involved fall outside the adaptive processing space of animals’ evolutionary perceptual, learning, memory and decision-making systems, making individuals particularly vulnerable to their impact.
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Helton, W. S. (2005). Animal expertise, conscious or not. Anim. Cogn., 8(2), 67–74.
Abstract: Rossano (Cognition 89:207, 2003) proposes expertise as an indicator of consciousness in humans and other animals. Since there is strong evidence that the development of expertise requires deliberate practice (Ericsson in The road to excellence: the acquisition of expert performance in the arts and sciences, sports and games 1996), and deliberate practice appears to be outside of the bounds of unconscious processing, then any signs of expertise development in an animal are indicators of consciousness. Rossano's argument may lead to an unsolvable debate about animal consciousness while causing researchers to overlook the underlying reality of animal expertise. This article provides evidence indicative of animals meeting each of the three definitions of expertise established in the scientific literature: expertise as a social construction, expertise as exceptional performance, and expertise as knowledge. In addition, cases of deliberate practice by non-human animals are offered. Acknowledging some animals as experts, regardless of consciousness, is warranted by the research findings and would prove useful in solving many issues remaining in the human expertise literature.
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Watanabe, S., & Huber, L. (2006). Animal logics: decisions in the absence of human language. Anim. Cogn., 9(4), 235–245.
Abstract: Without Abstract
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Herrmann, E., Melis, A. P., & Tomasello, M. (2006). Apes' use of iconic cues in the object-choice task. Anim. Cogn., 9(2), 118–130.
Abstract: In previous studies great apes have shown little ability to locate hidden food using a physical marker placed by a human directly on the target location. In this study, we hypothesized that the perceptual similarity between an iconic cue and the hidden reward (baited container) would help apes to infer the location of the food. In the first two experiments, we found that if an iconic cue is given in addition to a spatial/indexical cue – e.g., picture or replica of a banana placed on the target location – apes (chimpanzees, bonobos, orangutans, gorillas) as a group performed above chance. However, we also found in two further experiments that when iconic cues were given on their own without spatial/indexical information (iconic cue held up by human with no diagnostic spatial/indexical information), the apes were back to chance performance. Our overall conclusion is that although iconic information helps apes in the process of searching hidden food, the poor performance found in the last two experiments is due to apes' lack of understanding of the informative (cooperative) communicative intention of the experimenter.
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Gácsi, M., Miklósi, Á., Varga, O., Topál, J., & Csányi, V. (2004). Are readers of our face readers of our minds? Dogs (Canis familiaris) show situation-dependent recognition of human's attention. Anim. Cogn., 7(3), 144–153.
Abstract: The ability of animals to use behavioral/facial cues in detection of human attention has been widely investigated. In this test series we studied the ability of dogs to recognize human attention in different experimental situations (ball-fetching game, fetching objects on command, begging from humans). The attentional state of the humans was varied along two variables: (1) facing versus not facing the dog; (2) visible versus non-visible eyes. In the first set of experiments (fetching) the owners were told to take up different body positions (facing or not facing the dog) and to either cover or not cover their eyes with a blindfold. In the second set of experiments (begging) dogs had to choose between two eating humans based on either the visibility of the eyes or direction of the face. Our results show that the efficiency of dogs to discriminate between “attentive” and “inattentive” humans depended on the context of the test, but they could rely on the orientation of the body, the orientation of the head and the visibility of the eyes. With the exception of the fetching-game situation, they brought the object to the front of the human (even if he/she turned his/her back towards the dog), and preferentially begged from the facing (or seeing) human. There were also indications that dogs were sensitive to the visibility of the eyes because they showed increased hesitative behavior when approaching a blindfolded owner, and they also preferred to beg from the person with visible eyes. We conclude that dogs are able to rely on the same set of human facial cues for detection of attention, which form the behavioral basis of understanding attention in humans. Showing the ability of recognizing human attention across different situations dogs proved to be more flexible than chimpanzees investigated in similar circumstances.
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Hauser MD. (1997). Artifactual kinds and functional design features: what a primate understands without language. Cognition, 64, 285.
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Potì, P. (2000). Aspects of spatial cognition in capuchins (Cebus apella): frames of reference and scale of space. Anim. Cogn., 3(2), 69–77.
Abstract: Frames of reference (i.e. sets of loci defining spatial locations) determine animals' performances in object search tasks. Reference frames are used at different scales. Although much behavioural research has been conducted on search strategies in many animal species, relatively little has been done on nonhuman primates. The two experiments reported here focused on the relative strength and the level of functioning of different reference frames at the small-scale level in four capuchins (Cebus apella). Two identical boxes and a landmark were placed on a round platform that could be rotated. A reward was hidden in subject's view under one box, and then a sash-screen was lowered to hide the rotation of the platform; the sash-screen was then lifted and the subject allowed to search for the reward. In experiment 1 the rewarded box was always the closer to the landmark, in experiment 2 it could be either the box closer to or the box farther from the landmark. Capuchins were successful after invisible rotations in experiment 1, but they failed after invisible rotations in experiment 2. Two possible explanations are proposed: (1) capuchins relied heavily on the left-right body-axis as a frame, and they could only substitute it with a simple association between the rewarded position and the landmark; or (2) capuchins failed because they chose external cues in the room, therefore on a inappropriate scale. The latter explanation allows two further inferences: (a) the capuchins' choice was indirectly related to their body-axes; and (b) the capuchins revealed a cognitive asymmetry between small-scale and large-scale spaces, thus differing from humans.
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Fagot, J., Kruschke, J. K., Dépy, D., & Vauclair, J. (1998). Associative learning in baboons (Papio papio) and humans (Homo sapiens): species differences in learned attention to visual features. Anim. Cogn., 1(2), 123–133.
Abstract: We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features.
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