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Real, L. A. (1991). Animal choice behavior and the evolution of cognitive architecture. Science, 253(5023), 980–986.
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.
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Kirkwood, J. K. (2000). Animal minds and animal welfare. Vet. Rec., 146(11), 327.
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Reznikova, Z. I. (2006). [The study of tool use as the way for general estimation of cognitive abilities in animals]. Zh Obshch Biol, 67(1), 3–22.
Abstract: Investigation of tool use is an effective way to determine cognitive abilities of animals. This approach raises hypotheses, which delineate limits of animal's competence in understanding of objects properties and interrelations and the influence of individual and social experience on their behaviour. On the basis of brief review of different models of manipulation with objects and tools manufacturing (detaching, subtracting and reshaping) by various animals (from elephants to ants) in natural conditions the experimental data concerning tool usage was considered. Tool behaviour of anumals could be observed rarely and its distribution among different taxons is rather odd. Recent studies have revealed that some species (for instance, bonobos and tamarins) which didn't manipulate tools in wild life appears to be an advanced tool users and even manufacturers in laboratory. Experimental studies of animals tool use include investigation of their ability to use objects physical properties, to categorize objects involved in tool activity by its functional properties, to take forces affecting objects into account, as well as their capacity of planning their actions. The crucial question is whether animals can abstract general principles of relations between objects regardless of the exact circumstances, or they develop specific associations between concerete things and situations. Effectiveness of laboratory methods is estimated in the review basing on comparative studies of tool behaviour, such as “support problem”, “stick problem”, “tube- and tube-trap problem”, and “reserve tube problem”. Levels of social learning, the role of imprinting, and species-specific predisposition to formation of specific domains are discussed. Experimental investigation of tool use allows estimation of the individuals' intelligence in populations. A hypothesis suggesting that strong predisposition to formation of specific associations can serve as a driving force and at the same time as obstacle to animals' activity is discussed. In several “technically gifted” species (such as woodpecker finches, New Caledonian crows, and chimpanzees) tool use seems to be guided by a rapid process of trial and error learning. Individuals that are predisposed to learn specific connections do this too quickly and thus become enslaved by stereotypic solutions of raising problems.
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Straub, A. (2007). An intelligent crow beats a lab. Science, 316(5825), 688.
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Marchal, P., & Anderson, J. R. (1993). Mirror-image responses in capuchin monkeys (Cebus capucinus): social responses and use of reflected environmental information. Folia Primatol (Basel), 61(3), 165–173.
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Moses, S. N., Villate, C., & Ryan, J. D. (2006). An investigation of learning strategy supporting transitive inference performance in humans compared to other species. Neuropsychologia, 44(8), 1370–1387.
Abstract: Generalizations about neural function are often drawn from non-human animal models to human cognition, however, the assumption of cross-species conservation may sometimes be invalid. Humans may use different strategies mediated by alternative structures, or similar structures may operate differently within the context of the human brain. The transitive inference problem, considered a hallmark of logical reasoning, can be solved by non-human species via associative learning rather than logic. We tested whether humans use similar strategies to other species for transitive inference. Results are crucial for evaluating the validity of widely accepted assumptions of similar neural substrates underlying performance in humans and other animals. Here we show that successful transitive inference in humans is unrelated to use of associative learning strategies and is associated with ability to report the hierarchical relationship among stimuli. Our work stipulates that cross-species generalizations must be interpreted cautiously, since performance on the same task may be mediated by different strategies and/or neural systems.
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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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Seyfarth, R. M., Cheney, D. L., & Bergman, T. J. (2005). Primate social cognition and the origins of language. Trends. Cognit. Sci., 9(6), 264–266.
Abstract: Are the cognitive mechanisms underlying language unique, or can similar mechanisms be found in other domains? Recent field experiments demonstrate that baboons' knowledge of their companions' social relationships is based on discrete-valued traits (identity, rank, kinship) that are combined to create a representation of social relations that is hierarchically structured, open-ended, rule-governed, and independent of sensory modality. The mechanisms underlying language might have evolved from the social knowledge of our pre-linguistic primate ancestors.
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Cheney, D., Seyfarth, R., & Smuts, B. (1986). Social relationships and social cognition in nonhuman primates. Science, 234(4782), 1361–1366.
Abstract: Complex social relationships among nonhuman primates appear to contribute to individual reproductive success. Experiments with and behavioral observations of natural populations suggest that sophisticated cognitive mechanisms may underlie primate social relationships. Similar capacities are usually less apparent in the nonsocial realm, supporting the view that at least some aspects of primate intelligence evolved to solve the challenges of interacting with conspecifics.
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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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