<p>In this work we explore how close the artificial intelligence community has come to model the human mind regarding representation and processing of sequences. We analyse results produced by cognitive psychologists, who explore real minds, for features exhibited by human short- and long-term memory when representing and processing sequences. We compare these features with theories and models from the AI community divided into two types of theories: intrinsic and extrinsic theories. We conclude that the intrinsic theories have managed to explain most of the features, whereas the extrinsic theories still have a lot to do before exhibiting all features. We also present several suggestions for continued research to the AI community within the area of sequence representation and processing in the human mind.</p>

In this thesis, we build upon the work of Plate by advancing the theory and utility of Holographic Reduced Representations (HRRs). HRRs are a type of linear, associative memory developed by Plate and are an implementation of Hinton’s reduced representations. HRRs and HRR-like representations have been used to model human memory, to model understanding analogies, and to model the semantics of natural language. However, in previous research, HRRs are restricted to storing and retrieving vectors of random numbers, limiting both the ability of HRRs to model human performance in detail, and the potential applications of HRRs. We delve into the theory of HRRs and develop techniques to store and retrieve images, or other kinds of structured data, in an HRR. We also investigate square matrix representations as an alternative to HRRs, and use iterative training algorithms to improve HRR performance. This work provides a foundation for cognitive modellers and computer scientists to explore new applications of HRRs. / Thesis (Master, Computing) -- Queen's University, 2010-08-10 12:50:04.004

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This thesis explores the use of reflective practice in osteopathic medicine and uses the method to narrate my work as an osteopathic practitioner. It explores the development of perceptual diagnostic judgments, and the role of metacognition, intuition and palpation in osteopathic clinical reasoning. A qualitative interpretive approach was used with a novel narrative method as an organising structure. This was broadly based around reflective practice models of Gibbs, (1988), Kolb, (1984) and Carper (1978) and the ideas of Schön (1983). Descriptive texts were constructed from notes taken of my thoughts whilst in the presence of patients. This allowed access, as closely as possible, to my decision making process. Finally, the descriptive texts were expanded into narratives through dialogue with the existing literature and peer review. The narratives were then analysed using thematic analysis to derive an understanding of concepts arising from the data. This thesis argues that osteopathic clinical reasoning involves multisensory perceptual diagnostic judgments that begin as soon as the patient enters the clinic, and arise as a result of the use of mental and visual imagery and embodied senses. The multisensory information that is detected by a practitioner activates pattern recognition, analytic reasoning and provides explicit feedback used in decision making. Diagnosis occurs as a result of piecing together and interpreting the multisensory information whilst maintaining awareness of other diagnostic possibilities. The findings also suggest that osteopathic clinical reasoning involves the supervision of cognition by the metacognitive processes of meta-knowledge (MK), meta-experiences (ME), and meta-skills (MS). The latter are used to plan, monitor, analyse, predict, evaluate and revise the consultation and patient management as suggested by Pesut and Herman (1992). ME is demonstrated by the presence of judgments of learning used to ensure sufficient information has been gathered, and feelings of rightness that are used to perceive the correctness of information arriving and decisions made. The use of reflective practice in this research has developed the understanding of osteopathic clinical reasoning, and demonstrated that it provides a powerful conduit for change in practice. As a result, it enables the provision of better patient-centred osteopathic healthcare incorporating the biopsychosocial model of healthcare. Although rooted in my own osteopathic practice style and strategies, it should have resonance for those within the discipline of osteopathy and has implications for osteopathic education, training and research.

In this work we explore how close the artificial intelligence community has come to model the human mind regarding representation and processing of sequences. We analyse results produced by cognitive psychologists, who explore real minds, for features exhibited by human short- and long-term memory when representing and processing sequences. We compare these features with theories and models from the AI community divided into two types of theories: intrinsic and extrinsic theories. We conclude that the intrinsic theories have managed to explain most of the features, whereas the extrinsic theories still have a lot to do before exhibiting all features. We also present several suggestions for continued research to the AI community within the area of sequence representation and processing in the human mind.

There is an extensive debate in the adult literature on whether recognition memory can better be explained by a single- or a dual-process account. Single-process accounts assume that a single memory strength signal underlies recognition. Dual-process accounts propose two independent processes, namely recollection (slow and associated with contextual details) and familiarity (fast and automatic). The aim of this dissertation was to advance this debate using a cognitive developmental approach. By investigating age-related changes of recognition memory across childhood as a function of theoretically motivated experimental manipulations, predictions drawn from single- and dual-process models of recognition memory were tested. We adapted the Process Dissociation Paradigm (PDP; Jacoby, 1991) to disentangle processes underlying recognition memory in 5-, 7-, and 11-year-olds and adults using a Dual-Process Signal Detection cognitive modelling approach (DPSD; Yonelinas, 1996). Experiments 1 – 6 demonstrated that 5-year-olds are able to recollect items based on perceptual details. Consistent with dual-process theory, across all age groups a response time limit decreased recollection while leaving familiarity unaffected (Chapter 2). Converging evidence consistent with dissociations during childhood was found after repeated item presentation (Chapter 3). Finally, after a thorough empirical validation of our approach, the new paradigm was used to investigate the developmental perceptual to semantic shift (Chapter 4). These findings, using a double dissociation logic, have advanced the theoretical debate on the nature of recognition memory by showing that one process is insufficient to account for the developmental and experimental findings reported here. Recollection and familiarity follow different developmental trajectories and are affected by encoding and retrieval manipulations (i.e., repetition and time limits). This provides a challenge for existing theories of recognition memory.

No description available.

Résumé : Pour assister efficacement les personnes en perte d'autonomie dans le contexte des habitats intelligents, il est essentiel d'identifier les difficultés auxquelles ces personnes sont confrontées dans leur quotidien. L'objectif de ce travail est d'observer les processus exécutifs durant les activités de la vie quotidienne, ainsi que leur dysfonctionnement lors du vieillissement cognitif (normal ou lié à la maladie d'Alzheimer), puis d'élaborer un modèle théorique et informatique capable de simuler les comportements observés. Une phase d'observation et de qualification des processus de contrôle exécutif (capacités de régulation de l'action, de correction et d'adaptation lors de situations imprévues) a d'abord été réalisée, dornnant lieu à la spécification d'un modèle théorique fondé sur le modèle de contrôle attentionnel de l'action de Norman et Shallice. Le modèle théorique a ensuite été implémenté informatiquement et permet de simuler une activité quotidienne spécifique. // Abstract : In order to assist patients who are loosing their autonomy, smart homes and cognitive assistance systems have to be based on a good knowledge of people's disorders and on the difficulties they are likely to encounter in daily life. The specific objective of this PhD is to observe executive processes involved in the completion of daily activities and their impairment during ageing and dementia of the Alzheimer's type, and then to design both theoretical and computational models which are able to generate the observed behaviours. An observation and a qualification phase, allowing to observe executive control processes (action regulation, correction and adaptation when unexpected situations occur) have been first realized, leading to the specification of a theoretical model based on the Norman and Shallice model. This theoretical model has then been implemented to obtain a computational model, which allows the simulation of a specific activity of daily living.

Making decisions is a dynamic process. Especially when we face a decision between conflicting options, different forces seem to drag our mind from one option to the other one (James, 1890), again and again. This process may last for a long time, sometimes only coming to a decision when we are finally forced to choose, e.g. by an important deadline. Psychology and many other disciplines were interested in how humans make decisions from their beginnings on. Many different influences on decisions were discovered (e.g. Kahneman & Tversky, 1979; Todd & Gigerenzer, 2000). In the face of these advances, it seems odd, that knowledge about the ongoing process of reaching a decision is rare and much of the investigation has focused on the final outcome of choice situations (Townsend & Busemeyer, 1995). A very recent approach, called neuroeconomics, started out to investigate what happens behind the scenes of a final decision. Using modern neuroimaging methods, many neuroeconomists explain decision making in the brain in terms of a hierarchy of different neural modules that work together like a big corporation to finally make the best possible decision (Sanfey, Loewenstein, McClure, & Cohen, 2006). However, the focus on neural modules also limits this approach to a quite static view of decision making and many questions, related to the dynamic aspects of decision making, still remain open: How do we continuously control impulsive or habitual tendencies in our decisions when we pursue long-term goals? How do we shift attention back and forth between (goal) relevant properties of choice options? How do we adjust and readjust our focus of attention to relevant information in order to avoid distraction by irrelevant or misleading information? And how are we influenced by the environmental context when we make decisions? The present work aims to show how an approach based on the concepts of dynamic systems theory could complement the module oriented approach and enhance our knowledge of the processes of decision making. Chapter 2 elaborates the limits of the module oriented approach, with a special focus on decisions under conflict, when we are faced with conflicting information, and introduces the principles of a complementary dynamic approach. Chapter 3 deduces the dynamic hypothesis of this work: ongoing processes interactions at different time scales can explain specific cognitive functions without postulating specialized modules for this function. To approach this hypothesis, chapter 4 will develop a theoretical and empirical framework to study decision making dynamically. The empirical part, building on the empirical framework, starts with chapter 5 presenting an EEG experiment. Chapter 6 presents two mouse tracking experiments, and chapter 7 presents a modelling study, reproducing the empirical data of chapters 5 and 6. The general discussion in chapter 8 summarizes the theoretical and empirical results and discusses possible limitations. Finally, chapter 9 discusses the implications of the dynamic approach to decision making, presents an outlook on future research projects, and closes the work by offering a dynamic picture of the processes behind the stage of a final decision. / „Man kann nicht beides haben: Den Rahm und die Butter.“ - „Wer die Wahl hat, hat die Qual.“ Mit diesen Sprichwörtern beklagt der Volksmund, womit das Leben uns immer wieder konfrontiert: wir müssen entscheiden, und oftmals führt uns das in Entscheidungskonflikte. Im Dilemma solcher Konflikte mag es begründet sein, dass das Thema der vorliegenden Arbeit, die Entscheidungsforschung, nicht nur in der Psychologie schon immer eine wichtige Rolle spielte, sondern auch in anderen Disziplinen, wie der Ökonomie, der angewandten Mathematik und der Philosophie. Die langjährigen Bestrebungen, diese unterschiedlichen Fachbereiche zu integrieren (z.B. Kahneman & Tversky, 1979; von Neumann & Morgenstern, 1944; Savage, 1972), münden aktuell in das Forschungsgebiet der Neuroökonomie (Camerer, Loewenstein, & Prelec, 2005; Loewenstein, Rick, & Cohen, 2008; Sanfey, Loewenstein, McClure, & Cohen, 2006). Neuroökonomen nutzen vielfach die Methoden der bildgebenden Hirnforschung, um durch die Lokalisierung der neuronalen Basis hierarchisch gegliederter Module Entscheidungsprozesse zu erklären (z.B. Sanfey et al., 2006; Fellows, 2004). Während die Anwendung bildgebender Methoden Potential birgt (z.B. Harrison, 2008), ist es vor allem der modulorientierte Ansatz, der das Risiko einer zu eingeschränkten Sichtweise auf Entscheidungsprozesse trägt (z.B. Ortmann, 2008; Oullier & Kelso, 2006). Dies zeigt sich zum Beispiel im von der kognitiven Psychologie intensiv erforschten Bereich von Entscheidungen unter Konflikt. Eine zentrale Rolle bei dieser Art von Entscheidungen spielen kognitive Kontrollprozesse, die der Umsetzung zielorientierten Verhaltens (Norman & Shallice, 2000) durch Konfliktlösung und -anpassung dienen. Als Bindeglied dieser beiden Prozesse gilt die Detektion von Entscheidungskonflikten, welche die vorherrschende Conflict Monitoring Theory (Botvinick, Braver, Barch, Carter, & Cohen, 2001) entsprechend dem modulorientiertem Ansatz einem speziellen neuronalen Modul zuordnet, das im anterioren cingulären Cortex lokalisiert ist (Botvinick, Cohen, & Carter, 2004). Die Probleme eines einseitigen modulorientierten Ansatzes verdeutlichen hier unter anderem die widersprüchliche Befundlage (z.B. Mansouri, Tanaka, & Buckley, 2009) und die letztlich weiterhin ungeklärte Frage nach den zugrundeliegenden Prozessen. Die Arbeit hat deshalb zum Ziel, den modulorientierten Ansatz um einen komplementären Ansatz auf Basis der Theorie dynamischer Systeme (Dynamical Systems Theory, DST) zu ergänzen. Aus dem grundlegenden DST-Prinzip der kontinuierlichen (z.B. Spivey, 2007) Interaktion rückgekoppelter Komponenten (z.B. Kelso, 1995; Van Orden, Holden, & Turvey, 2003) wird zunächst die dynamische Hypothese abgeleitet, dass sich Effekte auf verschiedenen Zeitskalen gegenseitig bedingen und einander hervorbringen. Für Entscheidungen unter Konflikt bedeutet dies, dass sich die Prozesse der Konfliktlösung und anpassung durch ihre direkte Interaktion im kognitiven System gegenseitig erzeugen. Zur Überprüfung dieser Hypothese werden innerhalb der Arbeit generelle empirische Strategien entwickelt, welche die Untersuchung von Entscheidungsprozessen auf verschiedenen Zeitskalen ermöglichen. Im empirischen Teil der Arbeit werden sodann zwei dieser Strategien zur Anwendung gebracht, um den Erkenntnisgewinn des dynamischen Ansatzes zu illustrieren. Zunächst wird in einer EEG-Studie eine Frequency-Tagging-Methode (z.B. Müller & Hübner, 2002; Müller, Andersen, & Keil, 2007) auf die Untersuchung der kognitiven Kontrollprozesse in einer Flanker-Aufgabe (Eriksen & Eriksen, 1974) adaptiert. Die neue Kombination einer kontinuierlichen neurophysiologischen Methode und eines klassischen Konflikt-Paradigmas ermöglicht die gleichzeitige Untersuchung kontinuierlicher Veränderungen der Aufmerksamkeit auf relevante und irrelevante Information. Die Ergebnisse der Studie stützen die Hypothese einer direkten Interaktion von Prozessen der Konfliktlösung und -anpassung und stellen bereits einen Widerspruch zur Conflict Monitoring Theory dar. Als weitere empirische Strategie wird in zwei Experimenten die Methode des Maus-Tracking (z.B. Buetti & Kerzel, 2009; Song & Nakayama, 2009; Spivey, Grosjean, & Knoblich, 2005) im Rahmen einer Simon-Aufgabe (Simon, 1969) eingesetzt. Die erneute Kombination einer kontinuierlichen Methode, diesmal auf Reaktionsebene, mit einem klassischen Konflikt-Paradigma erlaubt die Messung von Verhaltenstendenzen im Verlauf des gesamten Entscheidungsprozesses. Mit Hilfe einer neu entwickelten regressionsbasierten Analysemethode werden die Subprozesse einzelner Entscheidungen separiert und Einblicke in die Dynamik von Konfliktlösung und -anpassung gewonnen. Die Ergebnisse zeigen ein komplexes Muster zeitlicher Interaktion zwischen den beiden kognitiven Kontrollprozessen, wobei die Konfliktanpassung zeitlich unabhängig von der Verarbeitung irrelevanter Information ist. Dies steht erneut im Widerspruch zu Annahmen der Conflict Monitoring Theory. Zusammenfassend stützen die empirischen Ergebnisse die dynamische Hypothese der kontinuierlichen Interaktion rückgekoppelter Komponenten und werden im nächsten Schritt in einem dynamisch-konnektionistischen Netzwerkmodell integriert. Als Alternative zum Modell der Conflict Monitoring Theory verzichtet es entsprechend dem dynamischen Ansatz auf ein Conflict Monitoring Modul (Botvinick et al., 2001). Es verfügt stattdessen über Verarbeitungs-Prozesse auf verschiedenen Zeitskalen (Kiebel, Daunizeau, & Friston, 2008) und eine Rückkopplung zwischen der Netzwerkschicht, die der Informationsverarbeitung dient, und jener, die der Zielrepräsentation dient (Gilbert & Shallice, 2002; Cohen & Huston, 1994). Die Ergebnisse der Simulation zeigen, dass das Modell sowohl die klassischen Befunde zur Konfliktlösung und anpassung (z.B. Gratton, Coles, & Donchin, 1992), als auch das in den empirischen Studien gefundene kontinuierliche Datenmuster von Entscheidungsprozessen reproduziert. Die empirischen Befunde und die Ergebnisse der Modellierung bestätigen somit die postulierte dynamische Hypothese, dass sich Effekte auf verschiedenen Zeitskalen gegenseitig bedingen und einander hervorbringen. Dies verdeutlicht den komplementären Wert des dynamischen Ansatzes zum modulorientierten Ansatz, welcher vielfach in der Neuroökonomie verfolgt wird. Der hier entwickelte DST-basierte Ansatz bietet somit sowohl ein komplementäres Denkmodell, welches wie der modulorientierte Ansatz eine Verbindung zwischen den Phänomenen auf neuronaler und Verhaltensebene herstellt, als auch neue empirische Methoden zur dynamischen Erforschung von Entscheidungen. Daraus wird abschließend eine Fokuserweiterung für die zukünftige Forschung abgeleitet: zum einen auf die kontinuierlichen Prozesse, welche zu einer Entscheidung führen, und zum anderen auf die Interaktionsdynamik dieser Prozesse. Die Arbeit schließt mit dem Bild eines Entscheidungsprozesses als einer selbstorganisierten, metastabilen Balance (z.B. Kelso, 1995) bei der Lösung verschiedener Entscheidungsdilemmata (Goschke, 2003).

L’activité de conduite automobile prend place dans un environnement dynamique en constante évolution. Le conducteur doit progresser sur la route au moyen de son véhicule, tout en interagissant adéquatement avec l'environnement et les autres usagers. Pour réaliser cette tâche, le conducteur doit percevoir son environnement, interpréter les événements pour se représenter correctement la situation de conduite, anticiper ces changements, et prendre des décisions afin d'engager des actions sur le véhicule lui permettant d'atteindre les buts qu'il se fixe à court et long terme. A cet égard, la complexité et la diversité des processus perceptifs, cognitifs et sensori-moteurs requis pour la conduite automobile font de cette activité un objet d'étude particulièrement riche pour les sciences de la cognition.Pour étudier l'activité du conducteur automobile afin de la comprendre, l'expliquer et peut-être la prédire, les sciences cognitives se dirigent vers la modélisation de la cognition humaine. Cette démarche permet une représentation et une description plus ou moins fine du système cognitif du conducteur automobile. Cependant, un modèle de la cognition ne permet qu'une description théorique. Grâce à son implémentation informatique, il devient possible de simuler les théories utilisées et déployer numériquement celles mises en jeu dans la modélisation cognitive.Ce travail de thèse s'articule autour de la modélisation cognitive du conducteur automobile, de son implémentation informatique sur une plateforme de développement virtuel et de sa simulation au sein de cette plateforme. Le modèle théorique que nous avons implémenté est COSMODRIVE, en développement au laboratoire du LESCOT à l'IFSTTAR, et la plateforme de développement accueillant le modèle est SIVIC, développée au LIVIC. C'est dans ce contexte que nous nous sommes engagés dans le développement computationnel et informatique du modèle COSMODRIVE, afin de pouvoir simuler l'activité perceptive et cognitive du conducteur automobile. Pour cela, nous nous sommes limités à certains processus cognitifs primordiaux, comme les fonctions stratégiques (planification d'itinéraires et réalisation de plans stratégiques), ou les fonctions perceptives (exploration et intégration de l'information visuelle), les fonctions cognitives tactiques (construction de représentations mentales, intégration perceptivo-cognitive de l'information, structuration des connaissances de conduite, etc), ou encore les fonctions d'exécution d'actions (régulation courte par zones enveloppes ou par points de poursuite).Par l'implémentation informatique du modèle COSMODRIVE sur SIVIC, il devient possible "d'incarner numériquement" des théories cognitives et de les "opérationnaliser" pour formuler des hypothèses de recherche sous la forme de prédictions de performances que l'on pourra évaluer empiriquement auprès de conducteurs humains. Ces hypothèses formulées, nous avons conduit des expérimentations sur un simulateur de conduite que nous avons construit. Afin d'éprouver notre modèle théorique et informatique du conducteur, nous avons comparé les performances des conducteurs humains avec les prédictions issues de la simulation. Les résultats obtenus ont permis de valider cette approche et de confirmer l'intérêt de la simulation cognitive pour appréhender les activités mentales du conducteur automobile. / Driving activity takes place in a dynamic and constantly changing environment. The driver has to make his car evolving on the road while ensuring adequate interactions with its close environment and other road users. In order to perform this task, the driver has to perceive the environment he is evolving in, to interpret events in order to correctly understand the current driving situation, to be able to anticipate its evolution and take decisions regarding vehicle control in order to reach his short and long term goals safely. As a result, both complexity and variety of perceptual, cognitive and sensorimotor processes involved in the driving activity make it very rich context for cognitive sciences.The modeling of human cognition, a specific method which belongs to cognitive sciences field, has been chosen to study driver's activity aiming at understanding, explaining or even predicting it. This approach allows a representation and a description of the driver's cognitive system with different levels of granularity. Thus, such a model offers only a theoretical description. When implemented on a computer, it opens the way to the simulation allowing the digital deployment of the theories involved in the cognitive model design.This thesis is focused on cognitive modeling of car driver, its implementation and its simulation using a virtual platform. The theoretical model that we implemented is COSMODRIVE, developed at IFSTTAR - LESCOT laboratory and the implementation platform we used for this, named SIVIC, is developed at IFSTTAR - LIVIC.This is the context where we started the computational development of the COSMODRIVE model in order to simulate the perceptual and cognitive activity of car driver. Indeed, we chose to limit our implementation to some crucial cognitive processes such as strategic functions (route planning and strategic plans execution), perceptual functions (exploration and integration of visual information), cognitive tactical functions (construction of mental representations, perceptual and cognitive integration of information, structuring of driving knowledge, etc.), or executive functions of actions (short control loop by ''envelopes zones'' or pursuit points).Through computer simulation, we used the numerical model as an innovative tool for scientific investigation in the field of cognitive sciences: The numerical simulation of cognitive functions identified and modeled by COSMODRIVE allowed us to define experimental hypotheses which leed us to conduct experiments in a driving simulator that we have built. To test the theoretical model and computer of the car driver, we compared the performance of human drivers on one hand and the predictions issued from the simulation on the other hand. It opens innovative opportunities for the development and the use of cognitive modeling and simulation of car driver.