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Alexander, F., & Collett, R. A. (1974). Pethidine in the horse. Res Vet Sci, 17(1), 136–137.
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Alexander, F., Davies, M. E., & Muir, A. R. (1970). Bacteriophage-like particles in the large intestine of the horse. Res Vet Sci, 11(6), 592–593.
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Alexander, F. (1955). Factors affecting the blood sugar concentration in horses. Q J Exp Physiol Cogn Med Sci, 40(1), 24–31.
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Alexander, F. (1952). Some functions of the large intestine of the horse. Q J Exp Physiol Cogn Med Sci, 37(4), 205–214.
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Alexander, F., & Benzie, D. (1951). A radiological study of the digestive tract of the foal. Q J Exp Physiol Cogn Med Sci, 36(4), 213–217.
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Gardner, E. L., & Engel, D. R. (1971). Imitational and social facilitatory aspects of observational learning in the laboratory rat. Psychon. Sci., 25(1), 5–6.
Abstract: Rats acquired a food-motivated leverpressing response by “observational learning” or by trial-and-error learning under conditions of social facilitation or isolation. Both the observational learning and social facilitation Ss learned faster than did the isolated trial-and-error Ss. There was no difference in speed of learning between the observational learning and social facilitation groups. It is suggested that some previous studies purporting to demonstrate observational learning may have demonstrated socially facilitated trial-and-error learning instead.
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Clement, T. S., & Zentall, T. R. (2000). Development of a single-code/default coding strategy in pigeons. Psychol Sci, 11(3), 261–264.
Abstract: We tested the hypothesis that pigeons could use a cognitively efficient coding strategy by training them on a conditional discrimination (delayed symbolic matching) in which one alternative was correct following the presentation of one sample (one-to-one), whereas the other alternative was correct following the presentation of any one of four other samples (many-to-one). When retention intervals of different durations were inserted between the offset of the sample and the onset of the choice stimuli, divergent retention functions were found. With increasing retention interval, matching accuracy on trials involving any of the many-to-one samples was increasingly better than matching accuracy on trials involving the one-to-one sample. Furthermore, following this test, pigeons treated a novel sample as if it had been one of the many-to-one samples. The data suggest that rather than learning each of the five sample-comparison associations independently, the pigeons developed a cognitively efficient single-code/default coding strategy.
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Daniel J. Povinelli, & Timothy J. Eddy. (2006). Chimpanzees: Joint Visual Attention. Psychol Sci, 7(3), 129–135.
Abstract: Gaze following is a behavior that draws the human infant into perceptual contact with objects or events in the world to which others are attending One interpretation of the development of this phenomenon is that it signals the emergence of joint or shared attention, which may be critical to the development of theory of mind An alternative interpretation is that gaze following is a noncognitive mechanism that exploits social stimuli in order to orient the infant (or adult) to important events in the world We report experimental results that chimpanzees display the effect in response to both movement of the head and eyes in concert and eve movement alone Additional tests indicate that chimpanzees appear able to (a) project an imaginary line of sight through invisible space and (b) understand How that line of sight can be impeded by solid, opaque objects This capacity may have arisen because of its reproductive payoffs in the context of social competition with conspecifics, predation avoidance, or both.
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Beran, M. J., Smith, J. D., & Perdue, B. M. (2013). Language-Trained Chimpanzees (Pan troglodytes) Name What They Have Seen but Look First at What They Have Not Seen. Psychol Sci, .
Abstract: Metacognition can be defined as knowing what one knows, and the question of whether nonhuman animals are metacognitive has driven an intense debate. We tested 3 language-trained chimpanzees in an information-seeking task in which the identity of a food item was the critical piece of information needed to obtain the food. The chimpanzees could either report the identity of the food immediately or first check a container in which the food had been hidden. In two experiments, the chimpanzees were significantly more likely to visit the container first on trials in which they could not know its contents but were more likely to just name the food item without looking into the container on trials in which they had seen its contents. Thus, chimpanzees showed efficient information-seeking behavior that suggested they knew what they had or had not already seen when it was time to name a hidden item.
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Bücheler, T., & Sieg, J. H. (2011). Understanding Science 2.0: Crowdsourcing and Open Innovation in the Scientific Method. Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET 11), 7, 327–329.
Abstract: The innovation process is currently undergoing significant change in many industries. The World Wide Web has created a virtual world of collective intelligence and helped large groups of people connect and collaborate in the innovation process [1]. Von Hippel [2], for instance, states that a large number of users of a given technology will come up with innovative ideas. This process, originating in business, is now also being observed in science. Discussions around “Citizen Science” [3] and “Science 2.0” [4] suggest the same effects are relevant for fundamental research practices. “Crowdsourcing” [5] and “Open Innovation” [6] as well as other names for those paradigms, like Peer Production, Wikinomics, Swarm Intelligence etc., have become buzzwords in recent years. However, serious academic research efforts have also been started in many disciplines. In essence, these buzzwords all describe a form of collective intelligence that is enabled by new technologies, particularly internet connectivity. The focus of most current research on this topic is in the for-profit domain, i.e. organizations willing (and able) to pay large sums to source innovation externally, for instance through innovation contests. Our research is testing the applicability of Crowdsourcing and some techniques from Open Innovation to the scientific method and basic science in a non-profit environment (e.g., a traditional research university). If the tools are found to be useful, this may significantly change how some research tasks are conducted: While large, apriori unknown crowds of “irrational agents” (i.e. humans) are used to support scientists (and teams thereof) in several research tasks through the internet, the usefulness and robustness of these interactions as well as scientifically important factors like quality and validity of research results are tested in a systematic manner. The research is highly interdisciplinary and is done in collaboration with scientists from sociology, psychology, management science, economics, computer science, and artificial intelligence. After a pre-study, extensive data collection has been conducted and the data is currently being analyzed. The paper presents ideas and hypotheses and opens the discussion for further input.
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