The human visual system gathers information about three-dimensional object shape from a wide range of sources. How effectively we can use these sources, and how they are combined to form a consistent and accurate percept of the 3D world is the focus of much research. In complex scenes inter-reflections of light between surfaces (mutual illumination) can occur, creating chromatic illumination gradients. These gradients provide a source of information about 3D object shape, but little research has been conducted into the capabilities of the visual system to use such information. The experiments described here were conducted with the aim of understanding the influence of chromatic gradients from mutual illumination on 3D shape perception. Psychophysical experiments are described that were designed to investigate: If the human visual system takes account of mutual illumination when estimating 3D object shape, and how this might occur; How colour shading cues are integrated with other shape cues; The relative influence on 3D shape perception of achromatic (luminance) shading and chromatic shading from mutual illumination. In addition, one chapter explores a selection of mathematical models of cue integration and their applicability in this case. The results of the experiments suggest that the human visual system is able to quickly assess and take account of colour mutual illuminations when estimating 3D object shape, and use chromatic gradients as an independent and effective cue. Finally, mathematical modelling reveals that the chromatic gradient cue is likely integrated with other shape cues in a way that is close to statistically optimal.

Perfectionism is a transdiagnostic process implicated in several disorders, and is defined in the literature as having standards of performance that are excessively high and often unrealistic, rigidly pursuing these standards, and subsequently measuring one’s own self-worth on their ability to meet these self-set standards (Egan et al., 2011). Perfectionism is related to many negative outcomes for physical and mental health, warranting the need to identify effective treatments that are accessible to individuals experiencing clinical perfectionism. There is a growing need for discrete, single session therapeutic interventions, and research has found that patients who were provided with a brief intervention exhibited accelerated rates of change, compared to patients whose treatment was longer (Baldwin et al., 2009; Kroska, 2018). Therefore, the purpose of this study was to examine the effects of a 90-minute, single-session ACT intervention targeting psychological flexibility for perfectionistic beliefs and behaviors on perfectionism, psychological distress, and well-being utilizing a multiple baseline across participants experimental design. Four individuals completed the following self-report measures at each time point: the Personalized Psychological Flexibility Inventory (PPFI), the Multidimensional Psychological Flexibility Inventory (MPFI), the Frost Multi-Dimensional Perfectionism Scale (FMPS), the Self-Compassion Scale (SCS), the Depression, Anxiety, and Stress Scale-21 item (DASS-21), and the Flourishing Scale (FS). These measures were completed once per week for the five-week baseline period. After baseline, participant engaged in a 90-minute single-session ACT intervention targeting the development of psychological flexibility. For follow-up, participants completed the same measures twice a week for four weeks. Researchers hypothesized that the intervention would increase psychological flexibility, flourishing, self-compassion, and progress towards an idiographic goal, and decrease perfectionism, psychological inflexibility, and psychological distress post-intervention compared to the baseline assessment. A TAR trend analysis was conducted, and Bayes Factors were computed for each individual for each outcome variable to examine within-participant results. A between-case standardized mean difference effect size for SCED was calculated for each outcome variable to examine the results across participants, resulting in a d-statistic. Within participants, while two individuals completed the study with perfectionistic concerns scores below cut offs, this outcome did not change significantly from baseline, with greater evidence for a null effect on this outcome variable for most participants. However, there was evidence for treatment effects for decreasing perfectionistic strivings, psychological distress, and psychological inflexibility and increasing psychological flexibility and flourishing. Across participants, the intervention demonstrated small to large effect sizes. There were small effects on perfectionistic concerns, perfectionistic strivings, psychological distress, and psychological flexibility towards an individual goal. There were medium effects for psychological flexibility and flourishing. Large effects were demonstrated for psychological inflexibility and self-compassion. Overall, the results demonstrate promising evidence for increasing well-being within the context of clinical perfectionism using a single session intervention.

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No / The recent development of information techniques, especially the state-of-the-art “big data” solutions, enables the extracting, gathering, and processing large amount of toxicity information from multiple sources. Facilitated by this technology advance, a framework named integrated testing strategies (ITS) has been proposed in the predictive toxicology domain, in an effort to intelligently jointly use multiple heterogeneous toxicity data records (through data fusion, grouping, interpolation/extrapolation etc.) for toxicity assessment. This will ultimately contribute to accelerating the development cycle of chemical products, reducing animal use, and decreasing development costs. Most of the current study in ITS is based on a group of consensus processes, termed weight of evidence (WoE), which quantitatively integrate all the relevant data instances towards the same endpoint into an integrated decision supported by data quality. Several WoE implementations for the particular case of toxicity data fusion have been presented in the literature, which are collectively studied in this paper. Noting that these uncertainty handling methodologies are usually not simply developed from conventional probability theory due to the unavailability of big datasets, this paper first investigates the mathematical foundations of these approaches. Then, the investigated data integration models are applied to a representative case in the predictive toxicology domain, with the experimental results compared and analysed.

Recollection is a pattern completion process that enables retrieval of arbitrarily associated information following minimal study. These attributes enable recollection to support retrieval of many kinds of mnemonic representations, from highly associative contextual information to very specific low-level representations. However, recollection is typically studied in the context of declarative memory tasks, in which participants exhibit recollection by explicitly reporting on the recollected information. Is it the case that recollection is limited to declarable representations, or is it a more general process that occurs for any representation? Two experiments and a novel analysis technique are presented to answer this question. The results suggest that recollection is not limited to declarable representations. These results argue against theories of recognition memory that restrict the representational input allowed to mnemonic processes; mnemonic processes in general may act on arbitrary representations.

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2010-2011 / L’objet principale de cette thèse est l’étude théorique et la conception d’algorithmes d’apprentissage concevant des classificateurs par vote de majorité. En particulier, nous présentons un théorème PAC-Bayes s’appliquant pour borner, entre autres, la variance de la perte de Gibbs (en plus de son espérance). Nous déduisons de ce théorème une borne du risque du vote de majorité plus serrée que la fameuse borne basée sur le risque de Gibbs. Nous présentons également un théorème permettant de borner le risque associé à des fonctions de perte générale. À partir de ce théorème, nous concevons des algorithmes d’apprentissage construisant des classificateurs par vote de majorité pondérés par une distribution minimisant une borne sur les risques associés aux fonctions de perte linéaire, quadratique, exponentielle, ainsi qu’à la fonction de perte du classificateur de Gibbs à piges multiples. Certains de ces algorithmes se comparent favorablement avec AdaBoost. / The main purpose of this thesis is the theoretical study and the design of learning algorithms returning majority-vote classifiers. In particular, we present a PAC-Bayes theorem allowing us to bound the variance of the Gibbs’ loss (not only its expectation). We deduce from this theorem a bound on the risk of a majority vote tighter than the famous bound based on the Gibbs’ risk. We also present a theorem that allows to bound the risk associated with general loss functions. From this theorem, we design learning algorithms building weighted majority vote classifiers minimizing a bound on the risk associated with the following loss functions : linear, quadratic and exponential. Also, we present algorithms based on the randomized majority vote. Some of these algorithms compare favorably with AdaBoost.