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Die vorliegende Arbeit beschreibt die Entwicklung eines interaktionalen Simulationsmodells zum späteren Einsatz in der VR-Simulation Clasivir 2.0 (Classroom Simulator in Virtual Reality), welche in der Lehrkräftebildung eingesetzt werden soll. Das Clasivir-Simulationsmodell wurde im Rahmen eines Prototyps implementiert und zwei anderen Simulationsmodellen in einem Fragebogen entgegengestellt. Ein Simulationsmodell beschreibt im Kontext einer digitalen Schulunterrichtssimulation, wie sich SuS in der Simulation verhalten.
Die drei Simulationsmodelle wurden über zwei unterschiedliche Typen von Video-Visualisierungen, genannt Mockup-Videos, dargestellt: Zum einen über eine 2D-Darstellung aus Vogelperspektive, zum anderen über eine 3D-Darstellung, in welcher 3D-Modelle von SuS animiert wurden. Bei dem realen Simulationsmodell handelt es sich um eine Übertragung einer authentischen Videoaufzeichnung von Unterricht einer hessischen Realschule in 2D/3D-Visualisierungen. Im randomisierten Simulationsmodell führen SuS ihre Verhalten zufällig aus. Alle Modelle basieren auf zweisekündigen Intervallen. Im Falle des realen Simulationsmodells wurde dies durch Analyse aller beobachtbaren einundzwanzig SuS gewonnen, im Falle des Clasivir-Simulationsmodells wurden die Vorhersagen des Simulationsmodells übertragen. Das Simulationsmodell von Clasivir basiert auf behavior trees, stellt eine Art von künstlicher Intelligenz dar und modelliert das SuS-Verhalten größtenteils in Abhängigkeit von Lehrkrafthandlungen. Die Entwicklung des interaktionalen Simulationsmodells von Clasivir ist eine Kernkomponente dieser Arbeit. Das Simulationsmodell basiert auf empirischen Ergebnissen aus den Bereichen der Psychometrie, der pädagogischen Psychologie, der Pädagogik und Ergebnissen der Simulations-/KI-Forschung. Ziel war die Entwicklung eines Modells, das nicht nur auf normativen Vorhersagen basiert, sondern empirisch und theoretisch valide ist. Nur wenige Simulationsmodelle in Unterrichtssimulationen werden mit dieser Art von Transparenz beschrieben, was eines der Alleinstellungsmerkmale dieser Arbeit ist. Es wurden Anstrengungen unternommen die vorliegenden empirischen Ergebnisse in einen kausalen Zusammenhang zu bringen, der mathematisch modelliert wurde. Im Zentrum steht die Konzentration von SuS, welche Ein uss auf Stör-, Melde- und Antwortverhalten hat. Diese Variable wird durch andere situative und personenbezogene Variablen (im Sinne von traits) ergänzt. Wo keine direkten empirischen Ergebnisse vorlagen wurde versucht plausibles Verhalten anhand der Übertragung von Konzeptionsmodellen zu gewinnen.
Da die bisherige Verwendung der angrenzenden Begriffe rund um die Simulationsentwicklung bislang sehr inkonsistent war, wurde es notwendig diese Termini zu definieren. Hervorzuheben ist die Entwicklung einer Taxonomie digitaler Unterrichtssimulationen, die so bislang nicht existierte. Anhand dieser Taxonomie und der erarbeiteten Fachtermini wurden Simulationen in der Lehrkräftebildung auf ihre Modellierung des Simulationsmodells hin untersucht. Die Untersuchung der Simulationen simSchool und VCS war, da sie einen verwandten Ansatz zu Clasvir verfolgen, besonders ergiebig.
Nach der Generierung der Mockup-Videos wurden N=105 Studierende, N=102 davon Lehramtsstudierende, aufgefordert, in einem Online-Fragebogen zwei der Simulationsmodelle miteinander zu vergleichen. Lehramtsstudierende wurden ausgewählt, da sie die Zielgruppe der Simulation sind. Welche Modelle die Partizipantinnen verglichen, war abhängig von der Gruppe der sie zugeteilt wurden. Hierbei wurde neben den Simulationsmodellen auch die visuelle Darstellung variiert. Insbesondere wurden die Partizipantinnen darum gebeten, den Fidelitätsgrad des Simulationsmodells, also den Maßstab, wie realistisch die Partizipantinnen das Verhalten der SuS in der Simulation fanden, zu bewerten. Inferenzstatistisch bestätigte sich, dass Partizipantinnen keinen Unterschied zwischen dem realen Simulationsmodell und dem Clasivir-Simulationsmodell erkennen konnten (t=1.463, df=178.9, p=.1452), aber das randomisierte Simulationsmodell mit einer moderaten Effektstärke von d=.634 als signifikant schlechter einschätzten (t=-2.5231, df=33.581, p=.008271). Die Art der Darbietung (2D oder 3D) hatte keinen statistisch signifikanten Einfluss auf die wahrgenommene Schwierigkeit der Bewertung (z=1.2426, p=.107). Damit kann festgestellt werden, dass eine komplexe und zeitintensive 3D-Visualisierung eines Simulationsmodells bei noch nicht vorliegender Simulation nicht erforderlich ist. Das Clasivir-Simulationsmodell wird als realistisch wahrgenommen. Es kann damit empfohlen werden, es in der VR-Simulation zu verwenden.
Im Ausblick werden bereits während des Schreibens der Arbeit gemachte Entwicklungen beschrieben und Konzepte zum weiteren Einsatz der Ergebnisse entwickelt. Es wird darauf verwiesen, dass eine erste Version eines VR-Simulators entwickelt wurde (Clasivir 1.0), der jedoch rein deterministisch funktioniert und noch nicht das in dieser Arbeit entwickelte Simulationsmodell inkludiert.
Agility, as the ability to react rapidly to unforeseen events, is an essential component of football performance. However, existing agility diagnostics often do not reflect the complex motor–cognitive interaction required on the field. Therefore, this study evaluates the criterion and ecological validity of a newly developed motor–cognitive dual-task agility approach in elite youth football players and compare it to a traditional reactive agility test. Twenty-one male youth elite football players (age:17.4 ±0 .6; BMI:23.2 ± 1.8) performed two agility tests (reactive agility, reactive agility with integrated multiple-object-tracking (Dual-Task Agility)) on the SKILLCOURT system. Performance was correlated to motor (sprint, jump), cognitive (executive functions, attention, reaction speed) and football specific tests (Loughborough soccer passing test (LSPT)) as well as indirect game metrics (coaches' rating, playing time). Reactive agility performance showed moderate correlations to attention and choice reaction times (r = 0.48−0.63), as well as to the LSPT (r = 0.51). The dual-task agility test revealed moderate relationships with attention and reaction speed (r = 0.47−0.58), executive functions (r = 0.45−0.63), as well as the game metrics (r = 0.51−0.61). Finally, the dual-task agility test significantly differentiated players based on their coaches' rating and playing time using a median split (p < 0.05; d = 0.8–1.28). Motor–cognitive agility performance in elite youth football players seems to be primarily determined by cognitive functions. The integration of multiple object tracking into reactive agility testing seems to be an ecologically valid approach for performance diagnostics in youth football.
Highlights
* The study introduces a novel motor–cognitive dual-task agility approach (incorporation of multiple-object-tracking in agility testing), evaluating its criterion and ecological validity in elite youth football players compared to a standard agility test.
* The standard agility test was shown to have moderate correlations with attention and choice reaction times, while the dual-task agility approach additionally incorporates executive functions
* While the agility test correlates to football-specific test performance, the dual-task agility test significantly discriminates players based on their potential ratings and in-season playing time, highlighting its potential as a valuable tool for assessing performance in youth football.
* The findings suggest that agility performance in elite youth football is primarily determined by cognitive functions
* Incorporating more complex cognitive elements such as multiple-object-tracking in agility testing may improve ecological validity and therefore the predictive value of the testing procedure.
Understanding the brain's proactive nature and its ability to anticipate the future has been a longstanding pursuit in philosophy and scientific research. The predictive processing framework explains how the brain generates predictions based on environmental regularities and adapts to both predicted and unpredicted events. Prediction errors (PE) occur when sensory evidence deviates from predictions, triggering cognitive and neural processes that enhance learning and subsequent memory. However, the effects of PE on episodic memory have not been clearly explained. This dissertation aims to address three key questions to advance our understanding of PE and episodic memory. First, how does the degree of PE influence episodic memory, and how do expected and unexpected events interact in this process? Second, what insights can be gained from studying the electrophysiological activity associated with prediction violations, and what role does PE play in subsequent memory benefits? Lastly, how do memory processes change across the lifespan, and how does this impact the brain's ability to remember events? By answering these questions, this dissertation contributes to advancing our understanding of the cognitive and neural mechanisms underlying the relationship PE and episodic memory.
"Autonomy is the condition under which what one does reflects who one is" (Weinrib, 2019, p.8). This quote encapsulates the core idea of autonomy, namely the correspondence of one’s inner values with one’s actions. This is a beautiful idea. After all, who wants their actions to be determined or controlled from the outside?
The classical definition of autonomy is precisely about this independence from external circumstances, which Murray (1938) primarily coined. Among other things, Murray characterizes autonomy as resistance to influence and defiance of authority. Similarly, Piaget (1983) describes individuals as autonomous, independent of external influences, in their thinking and actions, and foremost, adult authority. Subsequent work criticized this equation of autonomy with separation or independence (Bekker, 1993; Chirkov et al., 2003; Hmel & Pincus, 2002). In lieu thereof, autonomy is defined as an ability (Chirkov, 2011; Rössler, 2017) and as an essential human need (Ryan & Deci, 2006). Focus is now
on self-governing while relying on rationally determined values to pursue a happy life (Chirkov, 2011). According to Social Determination Theory (SDT), autonomy is about a sense of initiative and responsibility for one’s own actions. The experience of interest and appreciation can strengthen autonomy, whereas experiences of external control, e.g., through rewards or punishments, limit autonomy (Ryan & Deci, 2020). In the psychological discourse of autonomy, SDT is strongly represented (Chirkov et al., 2003; Koestner & Losier, 1996; Weinstein et al., 2012). Notably, SDT distinguishes between autonomy and independence as follows. While a person can autonomously ask for help or rely on others, a person can also be involuntarily alone and independent. Interestingly, these definitions are again closer to its etymological meaning as self-governing, originating from Greek αυτòνoμζ (autonomous).
The two strands of autonomy as independence and autonomy as self-determination are also reflected in the vital differentiation into reactive and reflective autonomy by Koestner and Losier (1996). Resisting external influence, particularly interpersonal in fluence, is what reactive autonomy entails. This interpretation is closely related to the classical concept of autonomy as separation and independence from others (Murray, 1938). On the other hand, reflective autonomy concerns intrapersonal processes, such as self-governing or self-regulation, as defined in Self-Determination Theory (Ryan et al., 2021). In this dissertation, we investigated the concept in three different approaches while focusing on its assessment and operationalization: To begin, in Article 1, we compared the layperson’s and the scientific perspective to each other to gain insight into the characteristics of autonomy. Then, in Articles 2 and 3, we experimentally tested behavioral autonomy as resistance to external influences. Simultaneously, we investigated the link between various autonomy trait measures and autonomous behavior. As a result, in Article 2, we looked at how people reacted to the effects of message framing and sender authority on social distancing behavior during the early COVID-19 pandemic. Finally, in Article 3 we investigated the resistance to a descriptive norm in answering factual questions, in the context of autonomous personality. In our first article, we used a semi-qualitative bottom-up approach to gain insights into the laypersons’ perspective on autonomy and compare it to the scientific notion. We followed a design proposed by Kraft-Todd and Rand (2019) on the term heroism. We derived five components from philosophical and psychological literature: dignity, independence from others, morality, self-awareness, and unconventionality. In three preregistered online studies, we compared these scientific components to the laypersons’ understanding of autonomy. In Study 1, participants (N = 222) listed at least three and up to ten examples of autonomous (self-determined) behaviors. Here, the participants named 807 meaningful examples, which we systematically categorized into 34 representative items for Study 2. Next, new participants (N = 114) rated these regarding their autonomy. Finally, we transferred the five highest-rated autonomy and the five lowest-rated autonomy items to Study 3 (N = 175). We asked participants to rate how strongly the items represented dignity, independence from others, morality, self-awareness, and unconventionality. We found all components to distinguish between high and low autonomy items but not for unconventionality. Thus, we conclude that laypersons’ view corresponds with the scientific characteristics of dignity, independence from others, self-awareness, and morality. A qualitative analysis of the examples also showed that both reactive and reflective definitions of autonomy are prevalent.
Our mind has the function of representing the physical and social world we are in, so that we can efficiently interact with it. This results in a constant and dynamic interaction between mind and world that produces a balance when representations are at the same time accurate with respect to what the world is communicating to our organism, but also compatible with how our mind works.
A paradigmatic case of this interaction is offered by perception, which is the mental function that represents contingent aspects of the world built from what is captured by our senses. Indeed, the dominant philosophical view in cognitive science is that our perceptual states are representations of the world and not direct access to that world. These representational perceptual states therefor include the aspects of the world they represent and that initiate the perception by stimulating our sensory organs.
Perceptual representations are built using information from the sensory system, i.e., bottom-up information, but are also integrated with information previously acquired, i.e., top-down information, so that perception interacts with memory through language and other mental functions. Such organization is believed to reflect a general mechanism of our mind/brain, which is to acquire and use information to make efficient predictions about the future, continuously updating older information with present information.
This predictive processing works because the world is not random, but shows a regular structure from which reliable expectations can be built. One way that our minds make these predictions is by adapting to the structure of the world in an implicit, automatic and unconscious way, a process that has been called Implicit Statistical Learning (ISL). ISL is a learning process that does not require awareness and happens in an incidental and spontaneous way, with mere exposure to statistical regularities of the world. It is what happens when we learn a language during early childhood, and that allows us to be implicitly sensitive to the phonological structure of speech, or to associate speech patterns with objects and events to learn word meaning.
A specific case of ISL is the learning of spatial configuration in the visual world, which we apply to abstract arrays of items, but most importantly, also to more ecological settings such as the visual scenes we are immersed in during our everyday life. The knowledge we acquire about the structure of visual scenes has been called “Scene Grammar”, because it informs about presence and position of objects in a similar way to what linguistic grammar tells us about the presence and position of words. So, we implicitly acquire the semantics of scenes, learning which objects are consistent with a certain scene, as well as the syntax of scenes, learning where objects are positioned in a consistent way within a certain scene.
More recent developments have proposed that scene grammar knowledge might be organized based on a hierarchical system: objects are arranged in the scene, which offers the more general context, but within a scene we can identify different spatial and functional clusters of objects, called “phrases”, that offer a second level of context; within every phrase, then, objects have different status, with usually one object (“anchor object”) offering strong prediction of where and which are the other objects within the phrase (“local objects”). However, these further aspects of the organization of objects In scenes remain poorly understood.
Another problem relates to the way we measure the structure of scenes to compare the organization of the visual world with the organization in the mind. Typically, to decide if an object appears or not in a certain scene, and whether or not it appears in a certain position within a scene, researchers based their decision on intuition and common-sense, maybe validating those decisions with independent raters. But it has been shown that often these decisions can be limited and more complex information about objects’ arrangement in scenes can be lost.
A potential solution to this problem might be using large set of real-world images, that have annotations and segmentations of objects, to measures statistics about how objects are arranged in the environment. This idea exploits the nowadays larger availability of this kind of datasets due to increasing developments of computer vision algorithms, and also parallels with the established usage of large text corpora in language research.
The goals of the current investigation were to extract object statistics from this image datasets and test if they reliably predict behavioural responses during object processing, as well as to use these statistics to investigate more complex aspects of scene grammar, such as its hierarchical organization, to see if this organization is reflected in the organization of objects in our mind.