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Categorization is a complex decision-making process that requires observers to collect information about stimuli using their senses. While research on visual or auditory categorization is extensive, there has been little attention given to tactile categorization. Here we developed a paradigm for studying tactile categorization using 3D-printed objects. Furthermore, we derived a categorization model using Bayesian inference and tested its performance against human participants in our categorization task. This model accurately predicted participant performance in our task but consistently outperformed them, even after extending the learning period for our participants. Through theoretical exploration and simulations, we demonstrated that the presence of sensory measurement noise could account for this performance gap, which we determined was a present factor in participants undergoing our task through a follow-up experiment. Including measurement noise led to a better-fitting model that was able to match the performance of our participants much more closely. Overall, the work in this thesis provides evidence for the efficacy of a tactile categorization experimental paradigm, demonstrates that a Bayesian model is a good fit and predictor for human categorization performance, and underscores the importance of accounting for sensory measurement noise in categorization models. / Dissertation / Doctor of Philosophy (PhD) / The process of categorization is an essential part of our daily life as we encounter various things in the world. Here we explore a model that attempts to explain this process. This model is derived using Bayesian inference and was applied to human behavioural data in a categorization task. We found that the model accounted for most of the performance of our participants but consistently outperformed them. We conducted simulations to explore and demonstrate that this difference is primarily due to the presence of sensory noise in participants. Once we accounted for this noise, we found that our model predicted human performance even more accurately. The work in this thesis demonstrates that a Bayesian Categorization Model which accounts for sensory noise is a good fit and predictor for human performance on categorization tasks.

Depuis plus de vingt ans, l'accès à la technologie est devenu très facile étant donné son omniprésence dans le quotidien de chacun et son faible coût. Cet accès aux technologies du numérique permet à toute personne équipée d'un ordinateur ou d'un smartphone de visualiser et de modifier des contenus digitaux. Avec les progrès en matière de stockage en ligne, la quantité de contenus digitaux tels que le son, l'image ou la vidéo sur internet a explosé et continue d'augmenter.Savoir identifier la source d'une image et certifier si celle-ci a été modifiée ou non sont des informations nécessaires pour authentifier une image et ainsi protéger la propriété intellectuelle et les droits d’auteur par exemple. Une des approches pour résoudre ces problèmes est le tatouage numérique. Il consiste à insérer une marque dans une image qui permettra de l'authentifier.Dans cette thèse, nous étudions premièrement le tatouage numérique dans le but de proposer des méthodes plus robustes aux modifications d'image grâce aux codes correcteurs. En étudiant la structure des erreurs produites par la modification d’une image marquée, un code correcteur sera plus efficace qu’un autre. Nous proposons aussi d’intégrer de nouveaux codes correcteurs appelés codes en métrique rang pour le tatouage.Ensuite, nous proposons d’améliorer l'invisibilité des méthodes de tatouage pour les images couleur. A l’insertion d’une marque, les dégradations de l’image sont perçues différemment par le système visuel humain en fonction de la couleur. Nous proposons un modèle biologique de la perception des couleurs qui nous permet de minimiser les distorsions psychovisuelles de l’image à l’insertion.Toutes ces techniques sont testées sur des images naturelles dans un contexte d’insertion d’information. / For more than twenty years, technology has become more and more easy to access. It is omnipresent in everyday life and is low cost. It allows anyone using a computer or a smartphone to visualize and modify digital contents. Also, with the impressive progress of online massive data storage (cloud), the quantity of digital contents has soared and continues to increase. To ensure the protection of intellectual property and copyright, knowing if an image has been modified or not is an important information in order to authenticate it. One approach to protect digital contents is digital watermarking. It consists in modifying an image to embed an invisible mark which can authenticate the image. In this doctorate thesis, we first study how to improve the robustness of digital image watermarking against image processings thanks to error correcting codes. By studying the error structure produced by the image processing applied on a watermarked image, we can find an optimal choice of error correcting code for the best correction performances. Also, we propose to integrate a new type of error correcting codes called rank metric codes for watermarking applications. Then, we propose to improve the invisibility of color image watermarking methods. At the embedding step, a host image suffers some distortions which are perceived differently in function of the color by the human visual system. We propose a biological model of color perception which allows one to minimize psychovisual distortions applied on the image to protect.

Sensory adaptation is an important aspect of perception. A seemingly non-beneficial consequence of adaptation is that it produces perceptual illusions. For instance, following focal adaptation, the perceived separation between stimuli straddling the adapted attribute or region is often exaggerated. This type of illusion, known as perceptual repulsion, is both a consequence of and a clue to the brain’s coding strategies and how they are influenced by recent sensory events. Adaptation-induced perceptual repulsion has been well documented in vision (e.g. the tilt aftereffect) and to a lesser extent in audition, but rarely studied in touch. The present thesis investigated the effects of adaptation on tactile spatial perception using a combination of human psychophysics and computational modeling. In a two-interval forced choice task, participants compared the perceived separation between two point-stimuli applied on the forearms successively. The point of subjective equality was extracted as a measure of perceived two-point distance. We showed that tactile spatial perception is subject to an adaptation-induced repulsion illusion: vibrotactile adaptation focally reduced tactile sensitivity and significantly increased the perceived distance between points straddling the adapted skin site (Chapter 2). This repulsion illusion, however, was not observed when the intervening skin was desensitized with topical anesthesia instead of vibrotactile adaptation, suggesting that peripheral desensitization alone is insufficient to induce the illusion (Chapter 3). With Bayesian perceptual modeling, we showed that the illusion was consistent with the hypothesis that the brain decodes tactile spatial input without awareness of the adaptation state in the nervous system (Chapter 4). Together, the empirical and theoretical work furthers the understanding of dynamic tactile spatial coding as the somatosensory system adapts to the sensory environment. Its main findings are consistent with the adaptation- induced repulsion illusions reported in vision and audition, suggesting that perception in different sensory modalities shares common processing features and computational principles. / Thesis / Doctor of Philosophy (PhD) / Sensory adaptation can shape how we perceive the world. In this thesis, we showed that the perception of space in touch is pliable and subject to the influence of adaptation. Psychophysical testing in human participants showed that vibratory adaptation induced an illusion that expanded the perceived distance between stimuli on the skin. This illusion provides clues into how information about space in touch is normally processed and interpreted by the brain. In addition, we developed a computational model that used a powerful statistical framework – Bayesian inference – to probe touch on a theoretical basis. To the best of our knowledge, the present thesis provides the first combined psychophysical and computational study on the effects of adaptation on tactile spatial perception. Our findings suggest that touch shares some common information processing principles with vision and hearing, and adaptation plays a functionally similar role in mediating this process across the senses.