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Investigations on Cognitive Computation and Computational Cognition DIPARTIMENTO DI SCIENZE DELL’INFORMAZIONE DOTTORATO DI RICERCA IN INFORMATICA DELLA SCUOLA DI DOTTORATO IN INFORMATICA – XXII CICLO TESI DI DOTTORATO DI RICERCA INF/01 Investigations on cognitive computation and computational cognition Isabella Cattinelli Relatore: Prof. N. Alberto Borghese Correlatore: Prof. Eraldo Paulesu Il Direttore della Scuola di Dottorato in Informatica: Prof. Ernesto Damiani ANNO ACCADEMICO 2009–2010 ii iii Abstract This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clust- ering-based meta-analysis of neuroimaging data, which is a technique that allows to collec- tively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchi- cal clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algo- rithm, aimed at identifying specific cerebral circuits involved in single word reading. More- over, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible expla- nation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weak- nesses. Finally, (3) we have turned to consider how features that are typical of human cogni- tion can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by rein- forcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an inter- action. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems. iv To all the people who have always believed in me. v vi Acknowledgements “Live all you can - it’s a mistake not to. It doesn’t so much matter what you do in particular, so long as you have your life.” — Henry James, 1843–1916 Writing the acknowledgments for one’s Thesis is always an emotional moment. You have reached the end of a journey, made of full dedication and passion, sacrifices and effort, and you finally get to see the fruits of your labor. Reaching the end of such a journey is in itself a truly rewarding moment; what is even more rewarding, however, is looking back to what brought you here, and realizing how many people supported you along the way. The work in this Thesis would not exist if it were not for my supervisors and collabora- tors. In particular, I owe much to my advisor, prof. Borghese, who has guided and supported me with patience and genuine interest, and encouraged me to be autonomous and rely on my intuitions. I wish to thank my co-advisor, Prof. Paulesu, for introducing me to the field of neuroscience: studying the human brain has fascinated me to the point that it became the fulcrum of my Ph.D. work, and it is now at the top of my research interests. I am thankful to both my supervisors for having always shown an uttermost confidence in my abilities, which made me often desire to be able to look at myself through their eyes. I am also indebted to Prof. Plaut for warmly welcoming me in Pittsburgh during my semester as a visiting student, and for giving me the opportunity to work with him on a topic (connectionist cognitive mod- elling) that is extremely appealing to me. I am also grateful to my referees, who generously gave their time to read my Thesis and help improve it. I wish to thank all those special people that I happened to meet at schools, conferences, during my travels, and that, sometimes unexpectedly, turned from interesting acquaintances into faithful friends with whom a genuine bond exists. I cannot name them all, but I am sure they know I am talking of them. I am thankful for having the opportunity of traveling, and also living abroad for a while: every journey has been an occasion for personal growth, and I have always come back with renewed enthusiasm for research. After thanking the distant ones, I turn now to the near ones: the AIS-lab boys and the Bicocca girls, who endured my presence in the respective labs day after day, and the “second- floor elite” at DSI, who shared philosophical meals with me in the very last period of my Ph.D. All of them have always been incredibly supportive of me, much beyond of what I think I deserve. I shared with them my ups and downs, and I must thank them for bearing with me and my idiosyncrasies. vii No acknowledgments could be complete and fair without mentioning my family. They are indeed my safety net and I know that, whatever happens and whatever my choices are, they will always be there for me. And finally, I want to thank myself, too: for, after all, I managed to get here, to the conclusion of this enterprise. And it is no small thing. E quindi uscimmo a riveder le stelle. viii Preface A proper subtitle for this Thesis could have been: “The Eclecticism of Computer Science”. In fact, computer science is a very flexible discipline whose tools can be successfully applied to tackle very diverse research questions, in many different scientific areas. Computational tech- niques are pervasively employed in virtually all branches of research for both automatized data analysis and modelling. More rarely, but not less importantly, research in computer sci- ence itself incorporates results from other areas to enhance the effectiveness of its techniques and tools. Cognitive science and neuroscience have been especially fruitful ground for this synergy with computer science. On the one hand, neuroscience and psychology are increasingly taking advantage of com- putational tools to help advancing our knowledge of how the brain works, both on a small scale (single neurons, small neural populations) and on a larger one (cognition and behavior). Computational and statistical techniques are already widely used to rigorously interpret data collected by means of behavioral or neuroimaging studies (i.e. statistical testing, cluster anal- ysis, image processing); also, the modelling power of computer science has proved useful to provide support (or refutation) for theories on cognitive and neural processing. Thus, com- puter science has been a valuable servant to the study of the brain and the mind. At the same time, the design of computational systems and algorithms has benefited from the advances in understanding the neural and cognitive basis of natural intelligence that were made possible by the work of neuroscientists and psychologists: since the richest, full-functioning intelli- gent system in nature is the human brain, it naturally fits the role of prime inspiration, and model, for building artificial systems that might hopefully approach, one day, similar levels of performance, flexibility, and robustness. This Thesis embodies these principles of eclecticism, and mutual beneficial exchange be- tween such disciplines. Three perspectives are taken, to each of which a separate part of the Thesis is dedicated: 1. computer science as the provider of fine-tuned tools for analyzing neuroscientific data; 2. computer science as an effective way to model, and therefore interpret, cognitive pro- cesses; 3. computer science as the science of building artificial intelligent systems inspired by fea- tures of human cognition. ix Part I deals with the analysis of data from large collections of neuroimaging experiments, also called meta-analyses. Functional neuroimaging experiments have become, in the last 20 years, a major investigation technique to look into the functional-anatomical basis of cog- nition, with the aim of locating brain regions that are specifically involved in the cognitive process of interest. The meta-analytic approach allows researchers to combine information from several different studies, and extract from this extended dataset consistent and robust knowledge that can provide ground for novel conclusions on the topic at hand. Chapter 1 provides an introduction to basic neuroscientific notions, and presents the main ideas behind neuroimaging experiments and meta-analyses. One approach to the meta-analysis of func- tional neuroimaging data employs a clustering procedure to automatically group activation coordinates into clusters that can then be further analyzed to determine their functional role within the considered cognitive process. Adopting this approach, we worked in the direction of developing an improved meta-analytic methodology. In particular, we designed a variant of classical hierarchical clustering that successfully deals with the problem of non-uniqueness of the solution; this problem is not irrelevant, although usually neglected in applications, be- cause it can potentially lead to different interpretations being given of the same data if these are permuted differently.
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