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Wiedererkennung ungefilterter und Fourier-gefilterter Schwarzweißmuster duch Honigbienen (Apis mellifera L.)Efler, Daniela Margarete 02 July 2004 (has links)
Honigbienen (Apis mellifera L.) sind in der Lage mit ihren Komplexaugen visuelle Muster wahrzunehmen und die Musterinformation im Zentralen Nervensystem zu speichern und für Ähnlichkeitsbewertungen wieder abzurufen. Die vorliegende Arbeit zeigt klare Evidenz gegen eine ausschließliche Bewertung von Schwarzweißmustern mit Hilfe von Template-Matching-Mechanismen. Mit systematisch abgewandelten Dressurparadigmen trainierte Bienen bewerteten Muster unabhängig von der erfolgten Dressur stets bevorzugt gemäß eher grober Mustereigenschaften, wie zum Beispiel die Parameter "schwarzer Musterzentralbereich" und "Musterzerstreutheit". Veränderte man in einem weiteren Versuchansatz die Musterinformation der Schwarzweißmuster zudem gezielt durch geeignete Fourier-Filterung, zeigte sich, dass Bienen zur Musterdiskriminierung bereits die Frequenzinformation von 2 - 8 Schwingungen/Bildbreite genügte. Diese Unschärfe der bewerteten Bildinformation ließ sich nicht ausschließlich aus den optischen Eigenschaften des visuellen Apparates der Bienen ableiten. Videodokumentationen und Einzelbildanalyse des Flugverhaltens der Bienen vor den Mustern ergaben zudem keinerlei Hinweise für eine Nutzung des Flugverhaltens als Bewertungsgrundlage zur Musterdiskriminierung. Die erhaltenen Ergebnisse zur Musterdiskriminierung wurden vor dem Hintergrund eines ökonomischen Entscheidungsmodells für menschliches Verhalten, den Frugalheuristiken, diskutiert und Hinweise auf eine ökonomische Bewertungsstrategie der Bienen entsprechend einer Take-The-Best-Heuristik gefunden. / Honeybees (Apis mellifera L.) are able to perceive visual patterns through their compound eyes and store the visual information in the central nervous system for subsequent use in pattern discrimination tasks. This thesis provides clear evidence against the assumption that pattern discrimination relies exclusively on template matching mechanisms. Bees discriminated pairs of patterns preferential using extracted pattern parameters. Within this thesis the preferred parameters of the bees following the training paradigms were coarse parameters such as "black centre" and "pattern disruption". In experiments with Fourier filtered patterns the frequency information of the patterns were additionally reduced. The results showed that bees could discriminate patterns using only 2 - 8 cycles/pattern-width of the frequency information. The fuzziness of the exploited visual information could not be assigned to restrictions of the visual system of bees. Additional documentation and single picture analysis of the videotaped flight behaviour in front of the patterns provided no evidence for bees using their flight behaviour in order to enhance the pattern discrimination ability. Application of economic human decision models (frugal heuristics) to the behavioural results showed clues that bees'' decisions could be explained with the help of the Take-The-Best-heuristic.
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Bounded Rationality and Exemplar ModelsPersson, Magnus January 2003 (has links)
<p>Bounded rationality is the study of how human cognition with limited capacity is adapted to handle the complex information structures in the environment. This thesis argues that in order to understand the bounded rationality of decision processes, it is necessary to develop decision theories that are computational process models based upon basic cognitive and perceptual mechanisms. The main goal of this thesis is to show that models of perceptual categorization based on the storage of exemplars and retrieval of similar exemplars whenever a new object is encountered (D. L. Medin & M. M. Schaffer, 1978), can be an important contribution to theories of decision making. Study I proposed, PROBEX (PROBabilities from Exemplars), a model for inferences from generic knowledge. It is a “lazy” algorithm that presumes no pre-computed abstractions. In a computer simulation it was found to be a powerful decision strategy, and it was possible to fit the model to human data in a psychologically plausible way. Study II was a theoretical investigation that found that PROBEX was very robust in conditions where the decision maker has very little information, and that it worked well even under the worst circumstances. Study III empirically tested if humans can learn to use exemplar based or one reason decision making strategies (G. Gigerenzer, P. Todd, & the ABC Research Group, 1999) where it is appropriate in a two-alternative choice task. Experiment 1 used cue structure and presentation format as independent variables, and participants easily used one reason strategies if the decision task presented the information as normal text. The participants were only able to use exemplars if they were presented as short strings of letters. Experiment 2 failed to accelerate learning of exemplar use during the decision phase, by prior exposure to exemplars in a similar task. In conclusion, this thesis supports that there are at least two modes of decision making, which are boundedly rational if they are used in the appropriate context. Exemplar strategies may, contrary to study II, only be used late in learning, and the conditions for learning need to be investigated further.</p>
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Bounded Rationality and Exemplar ModelsPersson, Magnus January 2003 (has links)
Bounded rationality is the study of how human cognition with limited capacity is adapted to handle the complex information structures in the environment. This thesis argues that in order to understand the bounded rationality of decision processes, it is necessary to develop decision theories that are computational process models based upon basic cognitive and perceptual mechanisms. The main goal of this thesis is to show that models of perceptual categorization based on the storage of exemplars and retrieval of similar exemplars whenever a new object is encountered (D. L. Medin & M. M. Schaffer, 1978), can be an important contribution to theories of decision making. Study I proposed, PROBEX (PROBabilities from Exemplars), a model for inferences from generic knowledge. It is a “lazy” algorithm that presumes no pre-computed abstractions. In a computer simulation it was found to be a powerful decision strategy, and it was possible to fit the model to human data in a psychologically plausible way. Study II was a theoretical investigation that found that PROBEX was very robust in conditions where the decision maker has very little information, and that it worked well even under the worst circumstances. Study III empirically tested if humans can learn to use exemplar based or one reason decision making strategies (G. Gigerenzer, P. Todd, & the ABC Research Group, 1999) where it is appropriate in a two-alternative choice task. Experiment 1 used cue structure and presentation format as independent variables, and participants easily used one reason strategies if the decision task presented the information as normal text. The participants were only able to use exemplars if they were presented as short strings of letters. Experiment 2 failed to accelerate learning of exemplar use during the decision phase, by prior exposure to exemplars in a similar task. In conclusion, this thesis supports that there are at least two modes of decision making, which are boundedly rational if they are used in the appropriate context. Exemplar strategies may, contrary to study II, only be used late in learning, and the conditions for learning need to be investigated further.
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