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Im Physikunterricht wurde lange Zeit die Bedeutung quantitativer Zusammenhänge für das Physiklernen überbewertet, qualitative Zusammenhänge spielten dagegen eine eher untergeordnete Rolle. Dies führte dazu, dass das Wissen der Schüler zumeist oberflächlich blieb und nicht auf neue Situationen angewendet werden konnte. TIMSS und Pisa offenbarten diese Schwierigkeiten. In den Abschlussberichten wurde kritisiert, dass die Schüler kaum in der Lage seien, Lernstoff zu transferieren oder problemlösend zu denken. Um physikalische Abläufe deuten und entsprechende Probleme lösen zu können, ist qualitativ-konzeptuelles Wissen nötig. Dieses kann, wie Forschungsergebnisse belegen, am besten durch die konstruktivistisch motivierte Gestaltung von Lernsituationen sowie durch die Integration externer Repräsentationen von Versuchsaussagen in den Schulunterricht erreicht werden. Eine konkrete Umsetzung dieser Bedingungen stellt der Einsatz rechnergestützter Experimente dar, der heutzutage ohne allzu großen technischen Aufwand realisiert werden kann. Diese Experimente erleichtern es dem Lernenden, durch den direkten Umgang mit realen Abläufen, physikalische Konzepte zu erschließen und somit qualitative Zusammenhänge zu verstehen. Während man lange Zeit von einer grundsätzlichen Lernwirksamkeit animierter Lernumgebungen ausging, zeigen dagegen neuere Untersuchungen eher Gegenteiliges auf. Schüler müssen offensichtlich erst lernen, wie mit multicodierten Repräsentationen zu arbeiten ist. Die vorliegende Arbeit will einen Beitrag dazu leisten, herauszufinden, wie lernwirksam sogenannte dynamisch-ikonische Repräsentationen (DIR) sind, die physikalische Größen vor dem Hintergrund konkreter Versuchsabläufe visualisieren. Dazu bearbeiteten im Rahmen einer DFG-Studie insgesamt 110 Schüler jeweils 16 Projekte, in denen mechanische Konzepte (Ort, Geschwindigkeit, Beschleunigung und Kraft) aufgegriffen wurden. Es zeigte sich, dass die Probanden mit den eingesetzten DIR nicht erfolgreicher lernen konnten als vergleichbare Schüler, die die gleichen Lerninhalte ohne die Unterstützung der DIR erarbeiteten. Im Gegenteil: Schüler mit einem geringen visuellen Vorstellungsvermögen schnitten aufgrund der Darbietung einer zusätzlichen Codierung schlechter ab als ihre Mitschüler. Andererseits belegen Untersuchungen von Blaschke, dass solche Repräsentationen in der Erarbeitungsphase einer neu entwickelten Unterrichtskonzeption auch und gerade von schwächeren Schülern konstruktiv zum Wissenserwerb genutzt werden konnten. Es scheint also, dass die Lerner zunächst Hilfe beim Umgang mit neuartigen Repräsentationsformen benötigen, bevor sie diese für den weiteren Aufbau adäquater physikalischer Modelle nutzen können. Eine experimentelle Untersuchung mit Schülern der 10. Jahrgangsstufe bestätigte diese Vermutung. Hier lernten 24 Probanden in zwei Gruppen die mechanischen Konzepte zu Ort, Geschwindigkeit und Beschleunigung kennen, bevor sie im Unterricht behandelt wurden. Während die Teilnehmer der ersten Gruppe nur die Simulationen von Bewegungsabläufen und die zugehörigen Liniendiagramme sahen, wurden für die zweite Gruppe unterstützend DIR eingesetzt, die den Zusammenhang von Bewegungsablauf und Liniendiagramm veranschaulichen sollten. In beiden Gruppen war es den Probanden möglich, Fragen zu stellen und Hilfe von einem Tutor zu erhalten. Die Ergebnisse zeigten auf, dass es den Schülern durch diese Maßnahme ermöglicht wurde, die DIR erfolgreich zum Wissenserwerb einzusetzen und signifikant besser abzuschneiden als die Teilnehmer in der Kontrollgruppe. In einer weiteren Untersuchung wurde abschließend der Frage nachgegangen, ob DIR unter Anleitung eines Tutors eventuell bereits in der Unterstufe sinnvoll eingesetzt werden können. Ausgangspunkt dieser Überlegung war die Tatsache, dass mit der Einführung des neuen bayerischen G8-Lehrplans wesentliche Inhalte, die Bestandteil der vorherigen Untersuchungen waren, aus dem Physikunterricht der 11. Jgst. in die 7. Jahrgangsstufe verlegt wurden. So bot es sich an, mit den Inhalten auch die DIR in der Unterstufe einzusetzen. Die Untersuchungen einer quasiexperimentellen Feldstudie in zwei siebten Klassen belegten, dass die betrachteten Repräsentationen beim Aufbau entsprechender Konzepte keinesfalls hinderlich, sondern sogar förderlich sein dürften. Denn die Schülergruppe, die mit Hilfe der DIR lernte, schnitt im direkten hypothesenprüfenden Vergleich mit der Kontrollklasse deutlich besser ab. Ein Kurztest, der die Nachhaltigkeit des Gelernten nach etwa einem Jahr überprüfen sollte, zeigte zudem auf, dass die Schüler der DIR-Gruppe die Konzepte, die unter Zuhilfenahme der DIR erarbeitet wurden, im Vergleich zu Schülern der Kontrollklasse und zu Schülern aus 11. Klassen insgesamt überraschend gut verstanden und behalten hatten.
Sugar reward learning in Drosophila : neuronal circuits in Drosophila associative olfactory learning
(2006)
Genetic intervention in the fly Drosophila melanogaster has provided strong evidence that the mushroom bodies of the insect brain act as the seat of memory traces for aversive and appetitive olfactory learning (reviewed in Heisenberg, 2003). In flies, electroshock is mainly used as negative reinforcer. Unfortunately this fact complicates a comparative consideration with other inscets as most studies use sugar as positive reinforcer. For example, several lines of evidence from honeybee and moth have suggested another site, the antennal lobe, to house neuronal plasticity underlying appetitive olfactory memory (reviewed in Menzel, 2001; Daly et al., 2004). Because of this I focused my work mainly on appetitive olfactory learning. In the first part of my thesis, I used a novel genetic tool, the TARGET system (McGuire et al., 2003), which allows the temporally controlled expression of a given effector gene in a defined set of cells. Comparing effector genes which either block neurotransmission or ablate cells showed important differences, revealing that selection of the appropriate effector gene is critical for evaluating the function of neural circuits. In the second part, a new engram of olfactory memory in the Drosophila projection neurons is described by restoring Rutabaga adenlylate cyclase (rut-AC) activity specifically in these cells. Expression of wild-type rutabaga in the projection neurons fully rescued the defect in sugar reward memory, but not in aversive electric shock memory. No difference was found in the stability of the appetitive memories rescued either in projection neurons or Kenyon cells. In the third part of the thesis I tried to understand how the reinforcing signals for sugar reward are internally represented. In the bee Hammer (1993) described a single octopaminergic neuron – called VUMmx1 – that mediates the sugar stimulus in associative olfactory reward learning. Analysis of single VUM neurons in the fly (Selcho, 2006) identified a neuron with a similar morphology as the VUMmx1 neuron. As there is a mutant in Drosophila lacking the last enzymatic step in octopamine synthesis (Monastirioti et al., 1996), Tyramine beta Hydroxylase, I was able to show that local Tyramine beta Hydroxylase expression successfully rescued sugar reward learning. This allows to conclude that about 250 cells including the VUM cluster are sufficient for mediating the sugar reinforcement signal in the fly. The description of a VUMmx1 similar neuron and the involvement of the VUM cluster in mediating the octopaminergic sugar stimulus are the first steps in establishing a neuronal map for US processing in Drosophila. Based on this work several experiments are contrivable to reach this ultimate goal in the fly. Taken together, the described similiarities between Drosophila and honeybee regarding the memory organisation in MBs and PNs and the proposed internal representation of the sugar reward suggest an evolutionarily conserved mechanism for appetitive olfactory learning in insects.
It has been known for a long time that Drosophila can learn to discriminate not only between different odorants but also between different concentrations of the same odor. Olfactory associative learning has been described as a pairing between odorant and electric shock and since then, most of the experiments conducted in this respect have largely neglected the dual properties of odors: quality and intensity. For odorant-coupled short-term memory, a biochemical model has been proposed that mainly relies on the known cAMP signaling pathway. Mushroom bodies (MB) have been shown to be necessary and sufficient for this type of memory, and the MB-model of odor learning and short-term memory was established. Yet, theoretically, based on the MB-model, flies should not be able to learn concentrations if trained to the lower of the two concentrations in the test. In this thesis, I investigate the role of concentration-dependent learning, establishment of a concentration-dependent memory and their correlation to the standard two-odor learning as described by the MB-model. In order to highlight the difference between learning of quality and learning of intensity of the same odor I have tried to characterize the nature of the stimulus that is actually learned by the flies, leading to the conclusion that during the training flies learn all possible cues that are presented at the time. The type of the following test seems to govern the usage of the information available. This revealed a distinction between what flies learned and what is actually measured. Furthermore, I have shown that learning of concentration is associative and that it is symmetrical between high and low concentrations. I have also shown how the subjective quality perception of an odor changes with changing intensity, suggesting that one odor can have more than one scent. There is no proof that flies perceive a range of concentrations of one odorant as one (odor) quality. Flies display a certain level of concentration invariance that is limited and related to the particular concentration. Learning of concentration is relevant only to a limited range of concentrations within the boundaries of concentration invariance. Moreover, under certain conditions, two chemically distinct odorants could smell sufficiently similarly such, that they can be generalized between each other like if they would be of the same quality. Therefore, the abilities of the fly to identify the difference in quality or in intensity of the stimuli need to be distinguished. The way how the stimulus is analyzed and processed speaks in favor of a concept postulating the existence of two separated memories. To follow this concept, I have proposed a new form of memory called odor intensity memory (OIM), characterized it and compared it to other olfactory memories. OIM is independent of some members of the known cAMP signaling pathway and very likely forms the rutabaga-independent component of the standard two-odor memory. The rutabaga-dependent odor memory requires qualitatively different olfactory stimuli. OIM is revealed within the limits of concentration invariance where the memory test gives only sub-optimal performance for the concentration differences but discrimination of odor quality is not possible at all. Based on the available experimental tools, OIM seems to require the mushroom bodies the same as odor-quality memory but its properties are different. Flies can memorize the quality of several odorants at a given time but a newly formed memory of one odor interferes with the OIM stored before. In addition, the OIM lasts only 1 to 3 hours - much shorter than the odor-quality memory.
Zars and co-workers were able to localize an engram of aversive olfactory memory to the mushroom bodies of Drosophila (Zars et al., 2000). In this thesis, I followed up on this finding in two ways. Inspired by Zars et al. (2000), I first focused on the whether it would also be possible to localize memory extinction.While memory extinction is well established behaviorally, little is known about the underlying circuitry and molecular mechanisms. In extension to the findings by Zars et al (2000), I show that aversive olfactory memories remain localized to a subset of mushroom body Kenyon cells for up to 3 hours. Extinction localizes to the same set of Kenyon cells. This common localization suggests a model in which unreinforced presentations of a previously learned odorant intracellularly antagonizes the signaling cascades underlying memory formation. The second part also targets memory localization, but addresses appetitive memory. I show that memories for the same olfactory cue can be established through either sugar or electric shock reinforcement. Importantly, these memories localize to the same set of neurons within the mushroom body. Thus, the question becomes apparent how the same signal can be associated with different events. It is shown that two different monoamines are specificaly necessary for formation of either of these memories, dopamine in case of electric shock and octopamine in case of sugar memory, respectively. Taking the representation of the olfactory cue within the mushroom bodies into account, the data suggest that the two memory traces are located in the same Kenyon cells, but in separate subcellular domains, one modulated by dopamine, the other by octopamine. Taken together, this study takes two further steps in the search for the engram. (1) The result that in Drosophila olfactory learning several memories are organized within the same set of Kenyon cells is in contrast to the pessimism expressed by Lashley that is might not be possible to localize an engram. (2) Beyond localization, a possibible mechanism how several engrams about the same stimulus can be localized within the same neurons might be suggested by the models of subcellular organisation, as postulated in case of appetitive and aversive memory on the one hand and acquisition and extinction of aversive memory on the other hand.
Most natural learning situations are of a complex nature and consist of a tight conjunction of the animal's behavior (B) with the perceived stimuli. According to the behavior of the animal in response to these stimuli, they are classified as being either biologically neutral (conditioned stimuli, CS) or important (unconditioned stimuli, US or reinforcer). A typical learning situation is thus identified by a three term contingency of B, CS and US. A functional characterization of the single associations during conditioning in such a three term contingency has so far hardly been possible. Therefore, the operational distinction between classical conditioning as a behavior-independent learning process (CS-US associations) and operant conditioning as essentially behavior-dependent learning (B-US associations) has proven very valuable. However, most learning experiments described so far have not been successful in fully separating operant from classical conditioning into single-association tasks. The Drosophila flight simulator in which the relevant behavior is a single motor variable (yaw torque), allows for the first time to completely separate the operant (B-US, B-CS) and the classical (CS-US) components of a complex learning situation and to examine their interactions. In this thesis the contributions of the single associations (CS-US, B-US and B-CS) to memory formation are studied. Moreover, for the first time a particularly prominent single association (CS-US) is characterized extensively in a three term contingency. A yoked control shows that classical (CS-US) pattern learning requires more training than operant pattern learning. Additionally, it can be demonstrated that an operantly trained stimulus can be successfully transferred from the behavior used during training to a new behavior in a subsequent test phase. This result shows unambiguously that during operant conditioning classical (CS-US) associations can be formed. In an extension to this insight, it emerges that such a classical association blocks the formation of an operant association, which would have been formed without the operant control of the learned stimuli. Instead the operant component seems to develop less markedly and is probably merged into a complex three-way association. This three-way association could either be implemented as a sequential B-CS-US or as a hierarchical (B-CS)-US association. The comparison of a simple classical (CS-US) with a composite operant (B, CS and US) learning situation and of a simple operant (B-US) with another composite operant (B, CS and US) learning situation, suggests a hierarchy of predictors of reinforcement. Operant behavior occurring during composite operant conditioning is hardly conditioned at all. The associability of classical stimuli that bear no relation to the behavior of the animal is of an intermediate value, as is operant behavior alone. Stimuli that are controlled by operant behavior accrue associative strength most easily. If several stimuli are available as potential predictors, again the question arises which CS-US associations are formed? A number of different studies in vertebrates yielded amazingly congruent results. These results inspired to examine and compare the properties of the CS-US association in a complex learning situation at the flight simulator with these vertebrate results. It is shown for the first time that Drosophila can learn compound stimuli and recall the individual components independently and in similar proportions. The attempt to obtain second-order conditioning with these stimuli, yielded a relatively small effect. In comparison with vertebrate data, blocking and sensory preconditioning experiments produced conforming as well as dissenting results. While no blocking could be found, a sound sensory preconditioning effect was obtained. Possible reasons for the failure to find blocking are discussed and further experiments are suggested. The sensory preconditioning effect found in this study is revealed using simultaneous stimulus presentation and depends on the amount of preconditioning. It is argued that this effect is a case of 'incidental learning', where two stimuli are associated without the need of reinforcement. Finally, the implications of the results obtained in this study for the general understanding of memory formation in complex learning situations are discussed.