Modeling complex cognitive phenomena is a challenging task, especially when it is required to account for the functioning of a cognitive system interacting with an uncertain and changing environment. Psychometrics offers a heterogeneous corpus of computational tools to infer latent cognitive constructs from the observation of behavioral outcomes. However, there is not an explicit consensus regarding the optimal way to properly take into account the intrinsic dynamic properties of the environment, as well as the dynamic nature of cognitive states. In the present dissertation, we explore the potentials of relying on discrete state dynamic models to formally account for the unfolding of cognitive sub-processes in changing task environments. In particular, we propose Probabilistic Graphical Models (PGMs) as an ideal and unifying mathematical language to represent cognitive dynamics as structured graphs codifying (causal) relationships between cognitive sub-components which unfolds in discrete time. We propose several works demonstrating the advantage and the representational power of such a modeling framework, by providing dynamic models of cognition specified according to different levels of abstraction.

Predicting the behavior of groups and how it emerges from the behaviours of individuals is a difficult task. Not only are individuals and their behaviors affected by the group and vice versa, but the way individuals are affected by and react to various conditions is difficult to predict due to the complex nature of human beings. However, if one could build models that sufficiently capture the behavior of individuals, it would be possible to simulate groups and make a prediction for the emergent behavior that way. Public Goods Games (PGGs) are a type of economic game that explores how individuals engage in cooperation and where different types of collective behaviors emerge. In group-based settings such as PGGs, there is a high level behavior pattern belonging to the group as a whole. In this work, we study how the group behavior emerges from the collection of behaviors belonging to individuals in the group. To this end, we create a model that predicts the emergent collective behavior in a PGG given a set of individual behaviors present within the group. We devise a classification scheme that groups individuals into a small set of phenotypes based on the behavior they exhibit in a PGG. We then create a model that simulates the long term behavior of groups playing a PGG based on the relative distribution of these phenotypes. Our simulation uses cognitive modeling with ACT-R to individually simulate each participant in a game. We find that our model is able to simulate group behavior that resembles what is seen with human participants given only the relative distribution of phenotypes. However, the model is not able to generalize to a PGG where the rules of the game are slightly changed. In modifying the distribution of phenotypes present in simulations, we found that increasing the number of cooperative individuals resulted in a stronger upward trend in group average contribution, while increasing the number of non-cooperative individuals had the opposite effect. Increasing the number of conditional cooperative individuals resulted in slowing the movement of group average contribution trend. / Att förutspå gruppers beteenden och hur dessa uppstår från individernas beteenden är svårt av flera skäl. Dels påverkar individernas beteende gruppen och vice versa, och dels är det svårt att förutspå hur individer påverkas av och reagerar på olika situationer och förhållanden på grund av människans komplexa natur. Om man kunde bygga modeller som fångar individers beteenden tillräckligt väl skulle det vara möjligt att genom simulering kunna ge förutsägelser på gruppens beteende. Public Goods Games (PGGs) är en typ av ekonomiskt spel som utforskar hur individer väljer att sammarbeta och där kollektiva beteenden kan uppstå. Inom gruppbaserade miljöer, som till exempel PGGs, finns det beteenden som tillhör gruppen i sig. I detta arbete studerar vi hur det gruppbeteendet härstammar från samlingen av individuella beteenden inom gruppen. För det skapar vi en modell som ger förutsägelser om det framväxande kollektiva beteendet i en PGG, givet kunskap om fördelningen av olika typer av individuella beteenden som finns i gruppen. För att göra detta utvecklar vi ett klassificeringssystem som grupperar individer i olika fenotyper baserat på deras uppvisade beteende i ett PGG. Vi skapar sedan en modell som simulerar detta PGG med en given grupp av individer. Våran simulering använder kognitiv modellering med ACT-R för att simulera varje enskild deltagare i ett PGG. Vi finner att vår modell simulerar gruppbeteenden som liknar det som syns med mänskliga deltagare, givet att man vet fördelningen av fenotyper i grupper. Modellen kan dock inte generalisera till ett PGG där reglerna är ändrade. När vi ändrade distributionen av fenotyper i simuleringen fann vi att ett ökat nummer av sammarbetsvilliga individer gjorde så att trenden av gruppen genomsnittliga bidrag rörde sig uppåt, medans ett ökat nummer av ej sammarbetsvilliga individer hade motsatt effekt. Då vi ökade antalet vilkorligt sammarbetsvilliga individer fann vi att det saktade ner förändringar av gruppen genomsnittliga bidrag.

Across a range of sectors, including transportation and healthcare, the use of automation to assist humans with increasingly complex tasks is also demanding that such systems are more interactive with human users. Given the role of cognitive factors in human decision-making during their interactions with automation, models enabling human cognitive state estimation and prediction could be used by autonomous systems to appropriately adapt their behavior. However, accomplishing this requires mathematical models of human cognitive state evolution that are suitable for algorithm design. In this thesis, a computational model of coupled human trust and self-confidence dynamics is proposed. The dynamics are modeled as a partially observable Markov decision process that leverages behavioral and self-report data as observations for estimation of the cognitive states. The use of an asymmetrical structure in the emission probability functions enables labeling and interpretation of the coupled cognitive states. The model is trained and validated using data collected from 340 participants. Analysis of the transition probabilities shows that the model captures nuanced effects, in terms of participants' decisions to rely on an autonomous system, that result as a function of the combination of their trust in the automation and self-confidence. Implications for the design of human-aware autonomous systems are discussed, particularly in the context of human trust and self-confidence calibration.

Die vorliegende Dissertation nähert sich aktuellen Kontroversen in der Forschung zur kognitiven Beanspruchung in Lehr-Lernsituationen im Zusammenhang mit der Abgrenzung und dem Zusammenspiel ressourcenbeanspruchender Faktoren unter einer zeitbezogenen Perspektive. In einem neuartigen Forschungsansatz werden zu diesem Zweck experimentelle Aufgaben aus der kognitiven Grundlagenforschung angewendet und verschiedene Methoden zur Erfassung der kognitiven Beanspruchung und der Betrachtung zugrunde liegender kognitiver Prozesse kombiniert. Zusammenfassend betonen die gewonnenen Erkenntnisse eine prozessgeleitete Rekonzeptualisierung des bestehenden theoretischen Rahmenmodells der kognitiven Beanspruchung und unterstreichen zusätzlich die Bedeutung eines multimethodischen Ansatzes zur kontinuierlichen Erfassung der kognitiven Beanspruchung. Auf praktischer Seite lassen sich zentrale Hinweise für die Entwicklung adaptiver Algorithmen sowie eine an den Lernenden orientierte Gestaltung instruktionaler Prozesse ableiten, welche den Weg zu intelligenten Lehr-Lernsystemen eröffnen. / This thesis addresses ongoing controversies in cognitive load research related to the scope and interplay of resource-demanding factors in instructional situations on a temporal perspective. In a novel approach, it applies experimental task frameworks from basic cognitive research and combines different methods for assessing cognitive load and underlying cognitive processes. Taken together, the obtained evidence emphasizes a process-related reconceptualization of the existing theoretical cognitive load framework and underlines the importance of a multimethod-approach to continuous cognitive load assessment. On a practical side, it informs the development of adaptive algorithms and the learner-aligned design of instructional support and thus leverages a pathway towards intelligent educational assistants.

No description available.

This thesis proposes a novel way to describe habit learning and the resulting balancing of goal-directed and habitual behavior using cognitive computational modeling. This approach builds on experimental evidence that habits may be understood as context-dependent automated sequences of behavior embedded in a hierarchical model. These assumptions were implemented in a Bayesian model, where goal-directed action sequences are encoded using a Markov decision process, and habits are interpreted to arise from a Bayesian prior over such sequences. Simulations show that this modeling approach yields key properties of habit learning, such as increased habit strength with increased training duration. This novel mechanistic description may lead to an improved understanding of habit learning mechanisms and individual learning trajectories, which may have implications for mental disorders which are believed to be accompanied by a maladapted balance between goal-directed an habitual control. / Diese Arbeit stellt eine neue mechanistische Beschreibung von Gewohnheitslernen und der daraus resultierenden Balance zwischen zielgerichtetem und habituellem Verhalten vor, die auf einem mathematischen kognitiven Modell aufbaut. Der Ansatz beruht auf experimenteller Evidenz, dass Gewohnheiten als kontext-abhängige, automatisierte Verhaltenssequenzen verstanden werden können, die in ein hierarchisches Modell eingebettet sind. Diese Annahmen werden mathematisch in einem Bayes'schen Modell umgesetzt, in dem zielgerichtetes Handeln als ein Markov'scher Entscheidungsprozess implementiert ist und Gewohnheiten aus einer Bayes'schen a-priori Wahrscheinlichkeit von Verhaltenssequenzen entstehen. Simulationen zeigen, dass dieser Ansatz wichtige Eigenschaften von Gewohnheitslernen reproduzieren kann, wie beispielsweise dass längere Trainingsdauern zu stärkeren Gewohnheiten führen. Diese neue mechanistische Beschreibung kann zu einem besseren Verständis individueller Lerntrajektorien und der Mechanismen beitragen, die dem Gewohnheitslernen zugrundeliegen. Dies könnte auch Auswirkungen auf das Verständnis psychischer Erkrankungen haben, bei denen davon ausgegangen wird, dass sie von einer maladaptiven Balance zwischen zielgerichtetem und habituellem Verhalten begleitet werden.

Wie schätzen Menschen quantitative Größen wie zum Beispiel den Verkaufspreis eines Autos? Oft benutzen Menschen zur Lösung von Schätzproblemen sogenannte Cues, Informationen, die probabilistisch mit dem zu schätzenden Kriterium verknüpft sind. Um den Verkaufspreis eines Autos zu schätzen, könnte man zum Beispiel Informationen über das Baujahr, die Automarke, oder den Kilometerstand des Autos verwenden. Um menschliche Schätzprozesse zu beschreiben, werden häufig linear additive Modelle herangezogen. In meiner Dissertation schlage ich alternative ein heuristisches Modell zur Schätzung quantitativer Größen vor: das Mapping-Modell. Im ersten Kapitel meiner Dissertation teste ich das Mapping-Modell gegen weitere, in der Literatur etablierte, Schätzmodelle. Es zeigte sich, dass das Mapping-Modell unter unterschiedlichen Bedingungen in der Lage war, die Schätzungen der Untersuchungsteilnehmer akkurat vorherzusagen. Allerdings bestimmte die Struktur der Aufgabe - im Einklang mit dem Ansatz der „adaptiven Werkzeugkiste“ - im großen Maße, welches Modell am besten geeignet war, die Schätzungen zu erfassen. Im zweiten Kapitel meiner Dissertation greife ich diesen Ansatz auf und untersuche, in wie weit die Aufgabenstruktur bestimmt, welches Modell die Schätzprozesse am Besten beschreibt. Meine Ergebnisse zeigten, dass das Mapping-Modell am Besten dazu geeignet war die Schätzungen der Versuchsteilnehmer zu beschreiben, wenn explizites Wissen über die Aufgabe vorhanden war, während ein Exemplar-Modell den Schätzprozess erfasste, wenn die Abstraktion von Wissen schwierig war. Im dritten Kapitel meiner Dissertation, wende ich das Mapping-Modell auf juristische Entscheidungen an. Eine Analyse von Strafakten ergab, dass das Mapping-Modell Strafzumessungsvorschläge von Staatsanwälten besser vorhersagte als eine lineare Regression. Dies zeigt, dass das Mapping-Modell auch außerhalb von Forschungslaboratorien dazu geeignet ist menschliche Schätzprozesse zu beschreiben. / How do people make quantitative estimations, such as estimating a car’s selling price? Often people rely on cues, information that is probabilistically related to the quantity they are estimating. For instance, to estimate the selling price of a car they could use information, such as the car’s manufacturer, age, mileage, or general condition. Traditionally, linear regression type models have been employed to capture the estimation process. In my dissertation, I propose an alternative cognitive theory for quantitative estimation: The mapping model which offers a heuristic approach to quantitative estimations. In the first part of my dissertation l test the mapping model against established alternative models of estimation, namely, linear regression, an exemplar model, and a simple estimation heuristic. The mapping model provided a valid account of people’s estimates outperforming the other models in a variety of conditions. Consistent with the “adaptive toolbox” approach on decision, which model was best in predicting participants’ estimations was a function of the task environment. In the second part of my dissertation, I examined further how different task features affect the performance of the models make. My results indicate that explicit knowledge about the cues is decisive. When knowledge about the cues was available, the mapping model was the best model; however, if knowledge about the task was difficult to abstract, participants’ estimations were best described by the exemplar model. In the third part of my dissertation, I applied the mapping model in the field of legal decision making. In an analysis of fining and incarceration decisions, I showed that the prosecutions’ sentence recommendations were better captured by the mapping model than by legal policy modeled with a linear regression. These results indicated that the mapping model is a valid model which can be applied to model actual estimation processes outside of the laboratory.

生活於數位時代，巨量的個人生命記憶使得人們難以輕易解讀，必須經過檢索或標籤化才可以進一步瞭解背後的意涵。本研究著力個人記憶裡繁瑣及週期性的廣泛事件，進行於「情節記憶語意化」以及「何以權衡大眾與個人資訊」兩議題之探討。透過生命記憶平台裡影像標籤自動化功能，我們以時空資訊為索引提出持續性概念重構模型，整合共同知識、個人近況以及個人偏好三項因素，模擬人們對每張照片下標籤時的認知歷程，改善其廣泛事件上註釋困難。在實驗設計上，實作大眾資訊模型、個人資訊模型以及本研究持續性概念重構模型，並招收九位受試者來剖析其認知歷程以及註釋效率。實驗結果顯示持續性概念重構模型解決了上述大眾與個人兩模型上的極限，即舊地重遊、季節性活動、非延續性活動性質以及資訊邊界註釋上的問題，因此本研究達成其個人生命記憶在廣泛事件之語意標籤自動化示範。 / In the digital era, labeling and retrieving are ways to understand the meaning behind a huge amount of lifetime archive. Foucusing on tedious and periodic general events, this study will discuss two issues: (1) the semantics of episodic memory (2) the trade-off between common and personal knowledge. Using the automatic image-tagging technique of lifelong digital archiving system, we propose the Coutinuous Reconceptualization Model which models the cognitive processing of examplar categorization based on temporal-spatial information. Integrating the common knowlegde, current personal life and hobby, the Continuous Reconceptualization Model improves the tagging efficiency. In this experiment, we compare the accuracy of cognitive modeling and tagging efficiency of the three distinct models: the common knowledge model, personal knowledge model and Coutinuous Reconceptualization Model. Nine participants were recruited to label the photos. The results show that the Continous Reconceptualization Model overcomes the limitations inherent in other models, including the auto-tagging problems of modeling certain situations, such as re-visiting places, seasonal activities, noncontinuous activities and information boundary. Consequently, the Continuous Reconceptualization Model demonstrated the efficiency of the automatic image-tagging technique used in the semantic labeling of the general event of personal memory.

Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.

Submitted by Daniel Chada (danielc2112@gmail.com) on 2017-01-10T13:25:02Z No. of bitstreams: 1 chada.phd.2017.01.09.pdf: 5177057 bytes, checksum: a6174d9f2ba0b373776e750def2a23aa (MD5) / Approved for entry into archive by ÁUREA CORRÊA DA FONSECA CORRÊA DA FONSECA (aurea.fonseca@fgv.br) on 2017-01-12T14:03:51Z (GMT) No. of bitstreams: 1 chada.phd.2017.01.09.pdf: 5177057 bytes, checksum: a6174d9f2ba0b373776e750def2a23aa (MD5) / Made available in DSpace on 2017-01-23T11:48:10Z (GMT). No. of bitstreams: 1 chada.phd.2017.01.09.pdf: 5177057 bytes, checksum: a6174d9f2ba0b373776e750def2a23aa (MD5) Previous issue date: 2016-12-08 / Este trabalho consiste em três contribuições independentes do âmbito da modelagem cognitiva ao campo de management science. O primeiro aborda Experience Recognition, uma teoria inicialmente introduzida por Linhares e Freitas [91]. Aqui ela é estendida e delineada, além de se discutir suas contribuições para a ciência cognitiva e management science. A segunda contribuição introduz a framework cognitiva chamada Rotational Sparse Distributed Memory, e fornece uma aplicação-exemplo de suas características como substrato para um fortemente relevante campo da management science: redes semânticas. A contribuição final aplica Rotational Sparse Distributed Memory para a modelagem de motifs de rede, flexibilidade dinâmica e organização hierárquica, três resultados de forte impacto na literatura recente de neurociência. A relevância de uma abordagem baseada na modelagem neurocientífica para a decision science é discutida. / This work is comprised of three independent contributions from the realm of cognitive modeling to management science. The first addresses Experience Recognition, a theory first introduced by Linhares and Freitas [91]. Here it is extended and better defined, and also its contribution to cognitive science and management science are discussed. The second contribution introduces a cognitive framework called Rotational Sparse Distributed Memory, and provides a sample application of its characteristics as a substrate for a highly relevant subject in management science: semantic networks. The final contribution applies Rotational Sparse Distributed Memory to modeling network motifs, dynamic flexibility and hierarchical organization, all highly impactful results in recent neuroscience literature. The relevance of a neuroscientific modeling approach towards a cognitive view of decision science are discussed.