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Wie schätzen Menschen quantitative Größen wie zum Beispiel den Verkaufspreis eines Autos? Oft benutzen Menschen zur Lösung von Schätzproblemen sogenannte Cues, Informationen, die probabilistisch mit dem zu schätzenden Kriterium verknüpft sind. Um den Verkaufspreis eines Autos zu schätzen, könnte man zum Beispiel Informationen über das Baujahr, die Automarke, oder den Kilometerstand des Autos verwenden. Um menschliche Schätzprozesse zu beschreiben, werden häufig linear additive Modelle herangezogen. In meiner Dissertation schlage ich alternative ein heuristisches Modell zur Schätzung quantitativer Größen vor: das Mapping-Modell. Im ersten Kapitel meiner Dissertation teste ich das Mapping-Modell gegen weitere, in der Literatur etablierte, Schätzmodelle. Es zeigte sich, dass das Mapping-Modell unter unterschiedlichen Bedingungen in der Lage war, die Schätzungen der Untersuchungsteilnehmer akkurat vorherzusagen. Allerdings bestimmte die Struktur der Aufgabe - im Einklang mit dem Ansatz der „adaptiven Werkzeugkiste“ - im großen Maße, welches Modell am besten geeignet war, die Schätzungen zu erfassen. Im zweiten Kapitel meiner Dissertation greife ich diesen Ansatz auf und untersuche, in wie weit die Aufgabenstruktur bestimmt, welches Modell die Schätzprozesse am Besten beschreibt. Meine Ergebnisse zeigten, dass das Mapping-Modell am Besten dazu geeignet war die Schätzungen der Versuchsteilnehmer zu beschreiben, wenn explizites Wissen über die Aufgabe vorhanden war, während ein Exemplar-Modell den Schätzprozess erfasste, wenn die Abstraktion von Wissen schwierig war. Im dritten Kapitel meiner Dissertation, wende ich das Mapping-Modell auf juristische Entscheidungen an. Eine Analyse von Strafakten ergab, dass das Mapping-Modell Strafzumessungsvorschläge von Staatsanwälten besser vorhersagte als eine lineare Regression. Dies zeigt, dass das Mapping-Modell auch außerhalb von Forschungslaboratorien dazu geeignet ist menschliche Schätzprozesse zu beschreiben. / How do people make quantitative estimations, such as estimating a car’s selling price? Often people rely on cues, information that is probabilistically related to the quantity they are estimating. For instance, to estimate the selling price of a car they could use information, such as the car’s manufacturer, age, mileage, or general condition. Traditionally, linear regression type models have been employed to capture the estimation process. In my dissertation, I propose an alternative cognitive theory for quantitative estimation: The mapping model which offers a heuristic approach to quantitative estimations. In the first part of my dissertation l test the mapping model against established alternative models of estimation, namely, linear regression, an exemplar model, and a simple estimation heuristic. The mapping model provided a valid account of people’s estimates outperforming the other models in a variety of conditions. Consistent with the “adaptive toolbox” approach on decision, which model was best in predicting participants’ estimations was a function of the task environment. In the second part of my dissertation, I examined further how different task features affect the performance of the models make. My results indicate that explicit knowledge about the cues is decisive. When knowledge about the cues was available, the mapping model was the best model; however, if knowledge about the task was difficult to abstract, participants’ estimations were best described by the exemplar model. In the third part of my dissertation, I applied the mapping model in the field of legal decision making. In an analysis of fining and incarceration decisions, I showed that the prosecutions’ sentence recommendations were better captured by the mapping model than by legal policy modeled with a linear regression. These results indicated that the mapping model is a valid model which can be applied to model actual estimation processes outside of the laboratory.

Le Québec est une immense province à l’intérieur de laquelle existe une grande diversité de conditions bioclimatiques et où les perturbations anthropiques et naturelles du couvert végétal sont nombreuses. À l’échelle provinciale, ces multiples facteurs interagissent pour sculpter la composition et la distribution des paysages. Les objectifs généraux de cette recherche visaient à explorer et comprendre la distribution spatiale des patrons des paysages du Québec, de même qu’à caractériser les patrons observés à partir d’images satellitaires. Pour ce faire, les patrons des paysages ont été quantifiés avec un ensemble complet d’indices calculés à partir d’une cartographie de la couverture végétale. Plusieurs approches ont été développées et appliquées pour interpréter les valeurs d’indices sur de vastes étendues et pour cartographier la distribution des patrons des paysages québécois. Les résultats ont révélé que les patrons de la végétation prédits par le Ministère des Ressources naturelles du Québec divergent des patrons de la couverture végétale observée. Ce mémoire dresse un portrait des paysages québécois et les synthétise de manière innovatrice, en plus de démontrer le potentiel d’utilisation des indices comme attributs biogéographiques à l’échelle nationale. / Quebec is a vast province in which bioclimatic conditions, land-uses and land-cover changes are highly diverse. At this scale, multiple drivers interact to have an impact on the composition and configuration of landscape patterns. The main objectives of this research were to explore and better understand the spatial distribution of landscape patterns across Quebec, and to characterize observed patterns as seen from satellite. To achieve these objectives, we quantified landscape patterns with an extensive set of metrics measured from a categorical land-cover map. We developed and applied several approaches to interpret metric values across large areas, and to map the distribution of Quebec landscape patterns. Results revealed that ecological subzones developed by the Ministère des Ressources naturelles et de la Faune were substantially inconsistent with observed land-cover patterns. This master thesis portrays and synthesizes Quebec landscapes in an innovative way, highlighting the considerable potential of use of landscape metrics for broad-scale biogeographic mapping.

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<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>

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 &amp; 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, &amp; 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.

This thesis investigates the cognitive processes and representations underlying human judgment in a multiple-cue judgment task. Several recent models as-sume that people have several qualitatively distinct and competing levels of knowledge representations (Ashby, Alfonso-Reese, Turken, &amp; Waldron, 1998; Erickson &amp; Kruschke, 1998; Nosofsky, Palmeri, &amp; McKinley, 1994; Sloman, 1996). The most successful cognitive models in categorization and multiple-cue judgment are, respectively, exemplar-based models and cue abstraction models. The models are different in the computations and processes implied, but the structure of the task is similar. Study 1 investigated if the different theoretical conclusions in categorization and multiple-cue judgment derive from genuine differences in the processes, or are accidental to the different research methods. In Study 2, we expected learning in dyads to promote explicit cue abstraction as a consequence of verbalization (a social abstraction effect) and performance to improve due to the larger joint exemplar knowledge base (an exemplar pooling effect). In Study 3 we used the generalized model Sigma to illustrate how change in task environments (linear vs. nonlinear) can shape the knowledge representation that is used. We expected that people are not able to use cue ab-straction when judging objects with a non-linear structure between the visual cues (features) of the objects and the criterion, and therefore they are forced to use exemplar-based processes. Taken together, the results of these studies indicate that differences that characterize typical categorization and multiple-cue judgment tasks are conducive of qualitatively different cognitive processes, and that the task environment plays an important role for which cognitive processes are used in multiple cue judgments.

Over the years, exemplar-based methods have yielded significant improvements over their model-based counterparts in image synthesis applications. Notably, texture synthesis algorithms using an exemplar-based approach have shown success where traditional stochastic methods failed. As an illustrative example, I will present an exemplar-based approach that yields substantial benefits for user-guided terrain synthesis using Digital Elevation Models (DEMs). This success is realized through exploitation of structural properties of natural terrain. In addition to their proliferation in the image synthesis domain, as annotated image datasets become increasingly available, exemplar-based methods are also gaining in popularity for image analysis applications. This thesis addresses the intersection between exemplar-based analysis and the problem of content-based image retrieval (CBIR). A basic problem in CBIR is the process by which the search criteria are refined by the user through the manipulation of returned exemplars. Exemplar-based analysis is particularly well-suited to query refinement due to its interpretability and the ease with which it can be incorporated into an interactive system. I investigate this connection in the domain of Computer-Assisted Diagnosis (CAD) of dermatological images. I demonstrate that exemplar-based approaches in CBIR can be effective for diagnosing pigmented skin lesions (PSLs). I will present an exemplar-based algorithm for segmenting PSLs in dermatoscopic images. In addition, I will present a generalized representation of dermoscopic features for detection and matching. This representation not only leads to an exemplar-based PSL diagnosis scheme, but it also enables us to realize interactive region-of-interest retrieval, which includes a relevance feedback mechanism to facilitate more flexible query-by-example analysis. Finally, I will assess the benefit of this CBIR-CAD approach through both quantitative evaluations and user studies.

The concept of home care nursing workload has not been widely studied and no evidence was found that an analysis of the concept had been undertaken. Consequently, there was a knowledge gap regarding the definition and attributes for the concept of home care nursing workload as it is currently experienced. To address that gap, a descriptive, three-phase, mixed methods (quantitative and qualitative) study was conducted. In Phase One, Rodgers’ (2000) evolutionary method was used to analyze the concept of home care nursing workload based on the empirical literature. Phase Two was situated within the naturalistic inquiry paradigm and involved observation of ten home care registered nurses during their visits to 61 patients. In Phase three a questionnaire was administered to validate the draft definition and attributes for the concept of home care nursing workload. It was completed by 88 home care nursing experts from clinical practice, education, management and research. Qualitative findings were analyzed using inductive content analysis. Quantitative data were analyzed descriptively using SPSS. Data triangulation was used extensively within and between the study phases. Of 14 attributes in the phase three draft concept definition, respondents assigned the highest level of relevance to the attribute of cognitive effort and the lowest to physical effort. The final definition contained 20 attributes and includes the following excerpt: “Home care nursing workload is the totality of the cognitive, emotional and physical effort home care nurses expend to meet the expectations of all stakeholders in providing holistic, outcome directed and patient/family focused care within the context of a short or long-term therapeutic relationship.” Respondents reported high levels of agreement with the accuracy and completeness of the definition and the majority indicated the definition would be useful or very useful in their day-to-day work. The comprehensive concept exemplar that emerged from the study includes each of the identified attributes. The study findings provided evidence of the complexity and challenge inherent in quantitatively measuring home care nursing workload. Accordingly, implications of the findings are shared for the management and monitoring of workload and associated outcomes, as well as for nursing practice, education and research.