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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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Cohen, J. (2007). Animal behavior. The world through a chimp's eyes (Vol. 316).
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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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Gould, J. L. (2004). Animal cognition. Curr Biol, 14(10), R372–5.
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Epstein, R. (1985). Animal cognition as the praxist views it. Neurosci Biobehav Rev, 9(4), 623–630.
Abstract: The distinction between psychology and praxics provides a clear answer to the question of animal cognition. As Griffin and others have noted, the kinds of behavioral phenomena that lead psychologists to speak of cognition in humans are also observed in nonhuman animals, and therefore those who are convinced of the legitimacy of psychology should not hesitate to speak of and to attempt to study animal cognition. The behavior of organisms is also a legitimate subject matter, and praxics, the study of behavior, has led to significant advances in our understanding of the kinds of behaviors that lead psychologists to speak of cognition. Praxics is a biological science; the attempt by students of behavior to appropriate psychology has been misguided. Generativity theory is an example of a formal theory of behavior that has proved useful both in the engineering of intelligent performances in nonhuman animals and in the prediction of intelligent performances in humans.
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Pennisi, E. (2006). Animal cognition. Man's best friend(s) reveal the possible roots of social intelligence (Vol. 312).
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Pennisi, E. (2006). Animal cognition. Social animals prove their smarts (Vol. 312).
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Sarter, M. (2004). Animal cognition: defining the issues. Neurosci Biobehav Rev, 28(7), 645–650.
Abstract: The assessment of cognitive functions in rodents represents a critical experimental variable in many research fields, ranging from the basic cognitive neurosciences to psychopharmacology and neurotoxicology. The increasing use of animal behavioral tests as 'assays' for the assessment of effects on learning and memory has resulted in a considerable heterogeneity of data, particularly in the field of behavioral and psycho pharmacology. The limited predictive validity of changes in behavioral performance observed in standard animal tests of learning and memory indicates that a renewed effort to scrutinize the validity of these tests is warranted. In humans, levels of processing (effortful vs. automatic) and categories of information (procedural vs. episodic/declarative) are important variables of cognitive operations. The design of tasks that assess the recall of 'episodic' or 'declarative' information appears to represent a particular challenge for research using laboratory rodents. For example, the hypothesis that changes in inspection time for a previously encountered place or object are based on the recall of declarative/episodic information requires substantiation. In order to generalize findings on the effects of neuronal or pharmacological manipulations on learning and memory, obtained from one species and one task, to other species and other tasks, the mediating role of important sets of variables which influence learning and memory (e.g. attentional, affective) needs to be determined. Similar to the view that a neuronal manipulation (e.g. a lesion) represents a theory of the condition modeled (e.g. a degenerative disorder), an animal behavioral task represents a theory of the behavioral/cognitive process of interest. Therefore, the test of hypotheses regarding the validity of procedures used to assess cognitive functions in animals is an inherent part of the research process.
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Trillmich, F., & Rehling, A. (2006). Animal Communication: Parent-Offspring. In Keith Brown (Ed.), Encyclopedia of Language & Linguistics (pp. 284–288). Oxford: Elsevier.
Abstract: Parent-offspring communication has evolved under strong selection to guarantee that the valuable resource of parental care is expended efficiently on raising offspring. To ensure allocation of parental care to their own offspring, individual recognition becomes established in higher vertebrates when the young become mobile at a time when a nest site can no longer provide a safe cue to recognition. Such recognition needs to be established by rapid, sometimes imprinting-like, processes in animals producing precocial offspring. In parents, offering strategies that stimulate feeding and entice offspring to approach the right site have evolved. Such parental signals can be olfactory, acoustic, or visual. In offspring, begging strategies involve shuffling for the best place to obtain food – be this the most productive teat or the best position in the nest. This involves signals that make the offspring particularly obvious to the parent. Parents often feed young according to their signaling intensity but may also show favoritism for weaker offspring. Offspring signals also serve to communicate the continuing presence of the young and may thereby maintain brood-care behavior in parents. Internal processes in parents may end parental care irrespective of further signaling by offspring, thus ensuring that offspring cannot manipulate parents into providing substantially more care than is optimal for their own fitness.
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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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