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Bayesian Algorithmic Modeling in Cognitive Science Julien Diard Bayesian Algorithmic Modeling in Cognitive Science Julien Diard To cite this version: Julien Diard. Bayesian Algorithmic Modeling in Cognitive Science. Computer science. Université Grenoble Alpes, 2015. tel-01237127 HAL Id: tel-01237127 https://hal.archives-ouvertes.fr/tel-01237127 Submitted on 2 Dec 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. HABILITATION A DIRIGER DES RECHERCHES École Doctorale Mathématiques, Sciences et Technologies de l’Information, Informatique Spécialité : Mathématiques Appliquées et Informatique Présentée par Julien DIARD préparée au sein du Laboratoire de Psychologie et NeuroCognition Bayesian Algorithmic Modeling in Cognitive Science Habilitation à diriger des recherches soutenue publiquement le 27 Novembre 2015, devant le jury composé de : Dr Frédéric ALEXANDRE DR INRIA, INRIA Bordeaux Sud-Ouest Examinateur Pr Laurent BESACIER PU Université Joseph Fourier, Laboratoire d’Informatique de Grenoble Examinateur Dr Pierre BESSIÈRE DR CNRS, Institut des Systèmes Intelligents et de Robotique Examinateur Pr Philippe LERAY PU Polytech’Nantes, Laboratoire d’Informatique de Nantes Atlantique Rapporteur Dr Pierre-Yves OUDEYER DR INRIA, INRIA Bordeaux Sud-Ouest Rapporteur Pr Mark PITT Professor Ohio State University, Language Perception Laboratory Rapporteur Contents Contents i List of Figures iii 1 Introduction 5 2 Bayesian Programming 11 2.1 Preliminary: Probabilities and probabilistic calculus . 11 2.2 Bayesian Programming methodology . 13 2.3 Tools for structured Bayesian Programming . 19 2.4 Relation with other probabilistic frameworks . 21 2.5 Graphical representation of Bayesian Programs . 24 3 Bayesian modeling of reading and writing 25 3.1 Bayesian modeling of letter reading and writing: BAP . 25 3.2 Bayesian modeling of word recognition and visual attention: BRAID . 34 4 Bayesian modeling of speech perception and production 43 4.1 Bayesian modeling of communicating agents: COSMO . 43 4.2 Bayesian modeling of language universals: COSMO-Emergence . 46 4.3 Bayesian modeling of speech perception: COSMO-Perception . 52 4.4 Bayesian modeling of speech production: COSMO-Production . 57 4.5 Discussion . 63 5 Bayesian modeling of cognition or modeling Bayesian cognition? 65 5.1 Marr’s hierarchy of models in cognitive science . 67 5.2 Bayesian models in Marr’s hierarchy . 73 5.3 Is Bayesian modeling a psychological theory? . 79 5.4 Can Bayesian models be psychological theories? . 81 5.5 Summary and position . 85 6 Conclusion 89 6.1 Main contribution . 89 6.2 Main perspectives . 90 Bibliography 93 A Other projects: bullet list summaries 111 A.1 Bayesian modeling of robotic navigation . 111 i Contents A.2 Bayesian modeling of human navigation . 112 A.3 Bayesian modeling of human proxemics . 114 A.4 Bayesian modeling of eye writing: BAP-EOL . 116 A.5 Bayesian modeling of performance: PARSEVAL . 116 A.6 Bayesian modeling of distinguishability of models: DEAD . 117 B Curriculum Vitæ 119 ii List of Figures 2.1 Commented structure of the Bayesian Programming methodology. 13 2.2 Taxonomies of probabilistic frameworks. 22 3.1 Graphical representation of the structure of the BAP model. 26 3.2 Example of sample traces and the learned probability distributions in the letter rep- resentation model. 28 3.3 Illustration of between-writer variability when writing as. 31 3.4 Illustration of inter-trial variability when writing as. 31 3.5 Examples of trajectory and letter copying. 32 3.6 Example cases where motor knowledge helps identify the correct letter. 33 3.7 Graphical representation of the structure of the BRAID model. 36 3.8 Spatial repartition of visual attention in BRAID. 37 3.9 Study of a portion of BRAID’s parameter space. 39 3.10 Temporal evolution of word recognition in BRAID. 41 4.1 General models of a communication situation and a communicating agent. 44 4.2 Bayesian questions and inferences for speech production and perception tasks. 45 4.3 Emergence of communication codes in societies of COSMO-Emergence agents. 49 4.4 Emergence of vowel systems in societies of COSMO-Emergence agents. 50 4.5 Emergence of stop consonant systems in societies of COSMO-Emergence agents. 51 4.6 Schema of the supervised learning scenario in COSMO. 52 4.7 Graphical representation of the structure of the COSMO-Perception model. 54 4.8 Performance of perception processes for syllables presented at various levels of noise. 57 4.9 Graphical representation of the structure of the COSMO-Production model. 58 4.10 Acoustic categorization in COSMO-Production. 60 4.11 Production of the /aki/ phoneme sequence in COSMO-Production. 61 4.12 Distances between motor commands of /aki/ sequences in GEPPETO and COSMO- Production. 62 6.1 Graphical representation of the structure of a model integrating BAP, BRAID and COSMO components. 91 A.1 Illustration of the Sensorimotor Interaction of Bayesian Maps. 112 A.2 Examples of von Mises probability distributions for various µ, λ parameter values. 113 A.3 Path integration using von Mises probability distributions. 113 A.4 Bayesian cue combination in the linear case. 114 A.5 Bayesian cue combination in the orientation case. 114 A.6 Bayesian modeling of personal and interaction social spaces. 115 iii List of Figures A.7 Video game demonstrating the PARSEVAL algorithm. 117 A.8 Bayesian distinguishability of models illustrated on memory retention. 118 iv À Éva qui, elle, sait écrire. Acknowledgment Writing a habilitation thesis is an odd exercise. It consists of course of exposing scientific topics and results, in a domain which is hopefully well-known by the author. This part of the exercise is rather usual, with common trade-offs between details and conciseness, scientific rigor and care for pedagogical concerns, etc. However, this thesis is also, in part, a biographical account, as it summarizes the scientific journey of the author and appears as a single-author manuscript. Attributing anything found in the remaining pages to me would be a gross mischaracterization at best, an agreed-upon lie. There are of course many other actors to consider, from the well-known shoulders of giants, to support and inspiration found in all places, most of them outside the lab. It is also conventional to acknowledge here all lab directors, team leaders, supporting agencies, many colleagues, friends and family; consider it done. The silver lining to this biographical nature of this manuscript is that I took it as an op- portunity to list and count the supervisors, collaborators and students I directly worked with. Supervisors and students were easy to count; for collaborators, I settled upon the list of those I have co-authored papers with. In this manner, I find that there are N = 48 people I have worked with over the years. Most, but not all of them, are mentioned in the following pages. Out of these, I find that about n = 12 of them are, to my eyes, exceptional scientists. Of course, I will not name names, so as to stay cordial to the others. Also, and more importantly, this estimation is really tongue-in-cheek; I would not presume to judge anyone here, lest they judge me in return, which would evidently be unpleasant. In any case, that number n is certainly larger that expected. Let me offer a back of the envelope estimation of my luck. Assuming being “exceptional” is to lie beyond the µ + 2σ boundary, each colleague being exceptional has roughly probability p = :025. Having n = 12 out of N = 48 colleagues being exceptional has probability Npn(1 p)N−n = 1:6691 10−9: Looser criteria for being exceptional n − ∗ of course increase this number, but even if it included ten percent of the population, this number would still be very small (around 10−3). Thus, I find that having such an exceptional collaboration network had a very small, probability to occur. I therefore find myself extremely lucky, and I have demonstrated it. As a consequence, I feel extremely thankful, but you will have to take my word for it, as, of course, you do not have direct access to my cognitive constructs. It will be no surprise that probabilities and building models of presumed cognitive entities are themes that will permeate the following pages. 3 CHAPTER 1 Introduction My research domain is Bayesian modeling of sensorimotor systems. I am interested both in natural and artificial sensorimotor systems. In other words, my research is multidisciplinary by nature, at the crossroads, on the one hand, of mathematics and computer science, and, on the other hand, of cognitive science and experimental psychology. By “sensorimotor systems” above, I mean systems with sensors and actuators, like animals and robots, that perceive, reason and act, not necessarily in this order. Indeed, a classical, coarse- grained view of information processing in both experimental psychology and robotics considers a three-step process: first, information collection and pre-processing to acquire or update internal representations of external phenomena of interest (perception), second, computation involving these internal representations to prepare future actions (planning), and third, action execution and monitoring (action). This very simple model of information processing is, like most models, both wrong and useful. Indeed, it is useful, for instance, as a rough guide to understand information flow in neurobi- ology, and at the same time, it is wrong because it hides away some of the complexity of said information flow (sensory prediction, temporal loop effects, top-down tuning of perception, etc.).
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