Diss. (sammanfattning) Umeå : Umeå universitet, 2007. / Härtill 4 uppsatser.

Judgments that demand consideration of pieces of information in the environment occur repeatedly throughout our lives. One professional example is that of a physician that considers multiple symptoms to make a judgment about a patient’s disease. The scientific study of such, so called, multiple-cue judgments that involve multiple pieces of information (cues: e.g., symptoms) and continuous criterion (e.g., blood pressure) has been concerned with the statistical modelling of judgment data (see Brehmer, 1994; Cooksey, 1996; Hammond & Stewart, 2001). In this thesis behavioural experiments, cognitive modelling and brain imaging is used to investigate an adaptive division of labor between multiple memory representations in multiple-cue judgment. It is hypothesized that the additive, independent linear effect of each cue can be explicitly abstracted and integrated by a serial, additive judgment process (Einhorn, Kleinmuntz, & Kleinmuntz, 1979). It is further hypothesized that a variety of sophisticated task properties, like non-additive cue combination, nonlinear relations, and inter-cue correlation, are carried implicitly by exemplar-memory (Medin & Schaffer, 1978; Nosofsky, 1984; Nosofsky & Johansen, 2000). Study I and II investigates the effect of additive versus non-additive cue-combination and verify the predicted shift in cognitive representations as a function of the underlying cue-combination rule. The third study is a review that discusses the nature of these representational shifts; are they contingent upon early perceived learning performance instead of automatic and error-driven? Study IV verifies that this shift is evident also in the neural activity associated with making judgments in additive and non-additive tasks.

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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.

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.

C'est dans l'interaction sociale, lieu d'occurrence premier du langage parlé (Local, 2003) que la parole est apprise, qu'elle est produite quotidiennement et qu'elle évolue. De nouvelles approches interdisciplinaires de l'étude de la parole, notamment la sociophonétique ou les récents développements de l'interaction conversationnelle, ouvrent de nouvelles perspectives dans la modélisation du traitement de la parole. Une question centrale à cette entreprise est la caractérisation des représentations mentales associées aux sons de la parole. Pour traiter cette question, nous utilisons l'approche exemplariste du traitement de la parole, qui propose que les sons de la parole sont mémorisés en incorporant des informations contextuelles détaillées. Nous présentons une nouvelle tâche interactionnelle, GMUP (pour "Group ’em up"), destinée à recueillir les réalisations de matériel phonologique finement contrôlé produit par deux interactants dans un cadre expérimental écologiquement valide. Les variables phonologiques décrivent les différences existant entre deux variétés de français parlé, le français standard et le français méridional. Des outils de reconnaissance automatique de la parole ont été développés pour évaluer la convergence phonétique, observable de l'évolution des représentations mentales, à deux niveaux de granularité : au niveau catégoriel de la variable phonologique et au niveau plus fin, subphonémique. L’emploi de mesures acoustiques détaillées à grande échelle permet de caractériser finement les différences inter-individuelles dans l'évolution de la forme des réalisations acoustiques associées aux représentations mentales en interaction conversationnelle. / It is in social interaction, the primary site of the occurrence of spoken language (Local, 2003) that speech is learned, that it is produced everyday and that it evolves. New interdisciplinary approaches to the study of speech, particularly in sociophonetics and in recent developments in conversational interaction, open new avenues for modeling speech processing. A central question in this enterprise relates to the caracterization of the mental representations of speech sounds. We address this question using the exemplarist approach of speech processing, which proposes that speech sounds are stored in memory along with detailed contextual information. We present a new interactional task, GMUP (which stands for "Group ’em up"), designed to collect realizations of highly-controlled phonological material produced by two interactants in an ecologically valid experimental setting. The phonological variables describe differences between two varieties of spoken French, Northern French and Southern French. Automatic speech recognition tools were developed to evaluate phonetic convergence, an observable of the evolution of the mental representations of speech, at two levels of granularity: at the categorical level of the phonological variable and at a more fine-grained, subphonemic level. The use of large-scale detailed acoustic measures allows us to finely caracterize interindividual differences in the evolution of the acoustic realizations associated with the mental representations of speech in conversational interaction.