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.

Behavioral animation has become popular for creating virtual characters that are autonomous agents and thus self-animating. This is useful for lessening the workload of human animators, populating virtual environments with interactive agents, etc. Unfortunately, current behavioral animation techniques suffer from three key problems: (1) deliberative behavioral models (i.e., cognitive models) are slow to execute; (2) interactive virtual characters cannot adapt online due to interaction with a human user; (3) programming of behavioral models is a difficult and time-intensive process. This dissertation presents a collection of papers that seek to overcome each of these problems. Specifically, these issues are alleviated through novel machine learning schemes. Problem 1 is addressed by using fast regression techniques to quickly approximate a cognitive model. Problem 2 is addressed by a novel multi-level technique composed of custom machine learning methods to gather salient knowledge with which to guide decision making. Finally, Problem 3 is addressed through programming-by-demonstration, allowing a non technical user to quickly and intuitively specify agent behavior.

Cognitive and behavioral models have become popular methods to create autonomous self-animating characters. Creating these models presents the following challenges: (1) Creating a cognitive or behavioral model is a time intensive and complex process that must be done by an expert programmer (2) The models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need of a programmer, to create new characters in less time and would be able to leverage each model's strengths to increase the character's performance, and to create new behaviors and animations. This thesis provides a framework that can aggregate together existing behavioral and cognitive models into an ensemble. An animator only has to rate how appropriately a character performed and through machine learning the system is able to determine how the character should act given the current situation. Empirical results from multiple case studies validate the approach taken.

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.