This thesis examines consumer behavior towards food risks in three different papers, focusing on two food concerns: genetically modified (GM) food and bovine spongiform encephalopathy (BSE). The first paper investigates the roles of different measures of trust on consumers stated choices for functional GM/nonGM canola oil products. These analyses show that consumers choices for GM/nonGM canola oil are influenced by both generalized trust and trust in food institutions. In general, trusting people are less likely to be in the group of respondents that can be characterized as being anti-GM; trusting people also tend to place a lower discount on the presence of a GM attribute. The second paper focuses on the modeling of consumers choices of foods with potential health and risk attributes. The analysis extends the linear compensatory utility model by allowing for use of attribute cutoffs in decision making. We find evidence that attribute cutoffs are commonly used by decision makers. Further, incorporating attribute cutoffs into the modeling of consumers choices significantly improved the model fit. This paper also examines a potential problem of endogeneity that may be associated with respondents self-reported cutoffs. Model estimates based on self-reported cutoffs differ substantially from those based on predicted cutoffs (where these are based on respondents demographic characteristics); potential reasons include the possibility that self-reported cutoffs may be endogenous. The third paper reports the impacts of habit and trust on consumers responses to a series of three BSE incidents in Canada. We observe that households reactions to the first two BSE events followed a similar pattern: households reduced their beef expenditure shares following the BSE announcements, but these subsequently recovered. We find that habit persistence reduced some households initial negative reactions to the first BSE incident, but that these households modified their beef consumption habits following recurring BSE incidents. Assessing the impacts of trust on households reactions to these BSE incidents, we find that trust tended to offset the negative effects of recurring BSE cases. / Agricultural and Resource Economics

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The CNN-based steganalysis model is able to capture some complex statistical dependencies and also learn feature representations. The proposed model uses a word embedding layer to map the words into dense vectors thus, achieving more accurate representations of the words. The proposed model extracts both, the syntax and semantic features. Files having less than 200 words are referred to as short text. Preprocessing for short text is done through word segmenting and encoding the words into indexes according to the position of words in the dictionary. Once this is performed, the index sequences are fed to the CNN to learn the feature representations. Files containing over 200 words are considered as long texts. Considering the wide range of length variation of these long texts, the proposed model tokenized long texts into their sentence components with a relatively consistent length prior to preprocessing the data. Eventually, the proposed model uses a decision strategy to make the final decision to check if the text file contains stego text or not.

Extracting structure from initially incomprehensible streams of events is fundamental to a range of human abilities: from navigating in a new environment to learning a language. These skills rely on our ability to extract spatial and temporal regularities, often with minimal explicit feedback, that is known as statistical learning. Despite the importance of statistical learning for making perceptual decisions, we know surprisingly little about the brain circuits and how they change when learning temporal regularities. In my thesis, I combine behavioural measurements, Diffusion Tensor Imaging (DTI) and resting-state fMRI (rs-fMRI) to investigate the structural and functional circuits that are involved in statistical learning of temporal structures. In particular, I compare structural connectivity as measured by DTI and functional connectivity as measured by rs-fMRI before vs. after training to investigate learning-dependent changes in human brain pathways. Further, I combine the two imaging modalities using graph theory and regression analyses to identify key predictors of individual learning performance. Using a prediction task in the context of sequence learning without explicit feedback, I demonstrate that individuals adapt to the environment’s statistics as they change over time from simple repetition to probabilistic combinations. Importantly, I show that learning of temporal structures relates to decision strategy that varies among individuals between two prototypical distributions: matching the exact sequence statistics or selecting the most probable outcome in a given context (i.e. maximising). Further, combining DTI and rs-fMRI, I show that learning-dependent plasticity in dissociable cortico-striatal circuits relates to decision strategy. In particular, matching relates to connectivity between visual cortex, hippocampus and caudate, while maximisation relates to connectivity between frontal and motor cortices and striatum. These findings have potential translational applications, as alternate brain routes may be re-trained to support learning ability when specific pathways (e.g. memory-related circuits) are compromised by age or disease.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Cette thèse s’intéresse aux réseaux de confusion comme représentation compacte et structurée des hypothèses multiples produites par un moteur de reconnaissance de parole et transmises à un module de post-traitement applicatif. Les réseaux de confusion (CN pour Confusion Networks) sont générés à partir des graphes de mots et structurent l’information sous la forme d’une séquence de classes contenant des hypothèses de mots en concurrence. Le cas d’usage étudié dans ces travaux est celui des hypothèses de reconnaissance transmises à un module de compréhension de la parole dans le cadre d’une application de dialogue déployée par France Telecom. Deux problématiques inhérentes à ce contexte applicatif sont soulevées. De façon générale, un système de dialogue doit non seulement reconnaître un énoncé prononcé par un utilisateur, mais aussi l’interpréter afin de déduire sons sens. Du point de vue de l’utilisateur, les performances perçues sont plus proches de celles de la chaîne complète de compréhension que de celles de la reconnaissance vocale seule. Ce sont ces performances que nous cherchons à optimiser. Le cas plus particulier d’une application déployée implique de pouvoir traiter des données réelles et donc très variées. Un énoncé peut être plus ou moins bruité, dans le domaine ou hors-domaine, couvert par le modèle sémantique de l’application ou non, etc. Étant donnée cette grande variabilité, nous posons la question de savoir si le fait d’appliquer les mêmes traitements sur l’ensemble des données, comme c’est le cas dans les approches classiques, est une solution adaptée. Avec cette double perspective, cette thèse s’attache à la fois à enrichir l’algorithme de construction des CNs dans le but d’optimiser globalement le processus de compréhension et à proposer une stratégie adéquate d’utilisation des réseaux de confusion dans le contexte d’une application réelle. Après une analyse des propriétés de deux approches de construction des CNs sur un corpus de données réelles, l’algorithme retenu est celui du "pivot". Nous en proposons une version modifiée et adaptée au contexte applicatif en introduisant notamment un traitement différencié des mots du graphe qui privilégie les mots porteurs de sens. En réponse à la grande variabilité des énoncés à traiter dans une application déployée, nous proposons une stratégie de décision à plusieurs niveaux qui vise à mieux prendre en compte les spécificités des différents types d’énoncés. Nous montrons notamment qu’il est préférable de n’exploiter la richesse des sorties multiples que sur les énoncés réellement porteurs de sens. Cette stratégie permet à la fois d’optimiser les temps de calcul et d’améliorer globalement les performances du système / The work presented in this PhD deals with the confusion networks as a compact and structured representation of multiple aligned recognition hypotheses produced by a speech recognition system and used by different applications. The confusion networks (CN) are constructed from word graphs and structure information as a sequence of classes containing several competing word hypothesis. In this work we focus on the problem of robust understanding from spontaneous speech input in a dialogue application, using CNs as structured representation of recognition hypotheses for the spoken language understanding module. We use France Telecom spoken dialogue system for customer care. Two issues inherent to this context are tackled. A dialogue system does not only have to recognize what a user says but also to understand the meaning of his request and to act upon it. From the user’s point of view, system performance is more accurately represented by the performance of the understanding process than by speech recognition performance only. Our work aims at improving the performance of the understanding process. Using a real application implies being able to process real heterogeneous data. An utterance can be more or less noisy, in the domain or out of the domain of the application, covered or not by the semantic model of the application, etc. A question raised by the variability of the data is whether applying the same processes to the entire data set, as done in classical approaches, is a suitable solution. This work follows a double perspective : to improve the CN construction algorithm with the intention of optimizing the understanding process and to propose an adequate strategy for the use of CN in a real application. Following a detailed analysis of two CN construction algorithms on a test set collected using the France Telecom customer care service, we decided to use the "pivot" algorithm for our work. We present a modified and adapted version of this algorithm. The new algorithm introduces different processing techniques for the words which are important for the understanding process. As for the variability of the real data the application has to process, we present a new multiple level decision strategy aiming at applying different processing techniques for different utterance categories. We show that it is preferable to process multiple recognition hypotheses only on utterances having a valid interpretation. This strategy optimises computation time and yields better global performance