<p>Mutations of the FOXP2 gene impair speech and language development in humans and shRNA-mediated suppression of the avian orthologue FoxP2 disrupts song learning in juvenile zebra finches. How diminished FoxP2 levels affect vocal control and alter the function of neural circuits important to learned vocalizations remains unclear. Using a combination of behavioral analysis, in vivo intracellular recordings in anaesthetized birds, pharmacology and extracellular recordings in singing birds, I addressed how FoxP2 knockdown in songbird striatum affects vocal control and signal propagation through circuits important for the control of learned vocalizations. In summary, I found that FoxP2 knockdown in the songbird striatum disrupts developmental and social modulation of song variability. Recordings in anaesthetized birds show that FoxP2 knockdown interferes with D1R-dependent modulation of activity propagation in a corticostriatal pathway important to song variability, an effect that may be partly attributable to reduced D1R and DARPP-32 protein levels. Furthermore, recordings in singing birds reveal that FoxP2 knockdown prevents social modulation of singing-related activity in this pathway. These findings show that reduced FoxP2 levels interfere with the dopaminergic modulation of vocal variability, which may impede song and speech development by disrupting reinforcement learning mechanisms.</p> / Dissertation

Wind is a fluctuating source of energy and, therefore, it can cause several technical impacts. These can be tackled by forecasting wind speed and thus wind power. The introduction of several statistical models in this field of research has brought to light promising results for improving wind speed predictions. However, there is not converging evidence on which is the optimal method. Over the last three decades, significant research has been carried out in the field of short-term forecasting using statistical models though less work focuses on longer timescales. The first part of this work concentrated on long-term wind speed variability over the UK. Two subsets have been used for assessing the variability of wind speed in the UK on both temporal and spatial coverage over a period representative of the expected lifespan of a wind farm. Two wind indices are presented with a calculated standard deviation of 4% . This value reveals that such changes in the average UK wind power capacity factor is equal to 7%. A parallel line of the research reported herein aimed to develop a novel statistical forecasting model for generating monthly mean wind speed predictions. It utilised long-term historic wind speed records from surface stations as well as reanalysis data. The methodology employed a SARIMAX model that incorporated monthly autocorrelation of wind speed and seasonality, and also included exogenous inputs. Four different cases were examined, each of which incorporated different independent variables. The results disclosed a strong association between the independent variables and wind speed showing correlations up to 0.72. Depending on each case, this relationship occurred from 4- up to 12-month lags. The inter comparison revealed an improvement in the forecasting accuracy of the proposed model compared to a similar model that did not take into account exogenous variables. This finding demonstrates the indisputable potential of using a SARIMAX for long-term wind speed forecasting.

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Previous research has shown that variability is a reinforceable dimension of operant behavior. Additionally, it has been demonstrated that learning is facilitated when variability in responding is high. In this research, variability was observed within an operant composed of any sequence of six left and right key presses. Variability was either a requirement for point delivery (VAR conditions) or points were delivered independent of variability (ANY conditions). Two groups of college undergraduates experienced different orders of conditions. One group began the experiment under VAR conditions, and the variability requirement was later removed. The other group began the experiment under ANY conditions, and the variability requirement was later added. A concurrently reinforced target sequence (i.e., an always-reinforced sequence of left and right key presses) was introduced to both groups after these orders of conditions had been experienced. A variety of outcomes resulted. Subjects learned the target sequence when variability was both high and low with non-target points concurrently available. Other subjects learned the target sequence after all non-target point deliveries had been suspended. One subject failed to acquire the target sequence at all. These results were compared to previous findings and possible explanations for the discrepancies were suggested.

Infants struggle to apply earlier-demonstrated sound-discrimination abilities to later word-learning, attending to non-constrastive acoustic dimensions (e.g., Hay et al., 2015), and not always to contrastive dimensions (e.g., Stager & Werker, 1997). One hint about the nature of infants' difficulties comes from the observation that input from multiple talkers can improve word learning (Rost & McMurray, 2009). This may be because, when a single talker says both of the to-be-learned words, consistent talker's-voice characteristics make the acoustics of the two words more overlapping (Apfelbaum & McMurray, 2011). Here, we test that notion. We taught 14-month-old infants two similar-sounding words in the Switch habituation paradigm. The same amount of overall talker variability was present as in prior multiple-talker experiments, but male and female talkers said different words, creating a gender-word correlation. Under an-acoustic-similarity account, correlated talker gender should help to separate words-acoustically and facilitate learning. Instead, we found that correlated talker gender impaired learning of word-object pairings compared with uncorrelated talker gender-even when gender-word pairings were always maintained in test-casting doubt on one account of the beneficial effects of talker variability. We discuss several alternate potential explanations for this effect.

Les moyennes et hautes latitudes de l'hémisphère nord sont caractérisées par une forte variabilité climatique en hiver, incluant l'occurrence d'évènements extrêmes tels que les vagues de froid ou les tempêtes, et présentent une faible prévisibilité aux échéances mensuelle à saisonnière dans les systèmes opérationnels. Un nombre croissant d'études montre qu'au-delà du couplage océan-atmosphère, le couplage troposphère-stratosphère contribue également à la variabilité climatique à ces échelles de temps. Cette thèse vise à mieux comprendre l'influence de la stratosphère sur la variabilité climatique hivernale à nos latitudes, et à quantifier sa contribution potentielle à la prévisibilité climatique saisonnière en comparaison de la contribution océanique. Dans un premier temps, un état des lieux des connaissances sur le couplage troposphère-stratosphère est dressé et la variabilité inter-annuelle du vortex stratosphérique polaire est revisitée par le biais d'analyses composites sur la base des réanalyses atmosphériques du CEPMMT. Ensuite, les principaux outils de cette thèse sont présentés et validés, à savoir le modèle ARPEGE-Climat et la technique de « nudging » permettant de relaxer (guider) le modèle vers les réanalyses. Comme beaucoup de modèles, les versions 4 et 5 d'ARPEGE-Climat en configuration T63L31 simulent un vortex stratosphérique polaire nettement décalé vers le sud, ce qui peut avoir des conséquences négatives sur la variabilité simulée via la modification des interactions ondes-écoulement moyen. Si la faible résolution verticale dans la stratosphère est souvent mise en avant pour expliquer le manque de prévisibilité dans les modèles, nos travaux sur la version 5 d'ARPEGE-Climat montrent que l'augmentation de la résolution verticale et l'élévation du toit du modèle à 0.1 hPa ne suffisent pas pour obtenir un climat plus réaliste, que ce soit en termes d'état moyen, de variabilité ou de prévisibilité à l'échelle saisonnière. C'est pourquoi, tout au long de cette thèse, la technique de la relaxation de la stratosphère vers les réanalyses issues du CEPMMT a été exploitée afin de montrer, de manière idéalisée, sa forte influence sur la variabilité climatique hivernale aux extratropiques de l'hémisphère nord, par rapport au seul forçage par les températures de surface de la mer observées. L'étude des hivers 1976-1977 et 2009-2010 via la réalisation de simulations d'ensemble avec et sans nudging a permis de confirmer la contribution de la stratosphère à la phase négative de la NAO et aux fortes anomalies négatives de température observées sur l'Europe du nord. La généralisation des ensembles à la période 1958-2007 (avec initialisation au 1er Novembre) confirme l'impact positif du nudging extratropical mais montre un effet très limité du nudging équatorial qu'il conviendrait d'évaluer de manière plus précise en augmentant la taille des ensembles. Ainsi, si elle confirme l'importance de la stratosphère pour la prévision saisonnière hivernale à nos latitudes, cette thèse ouvre de nombreuses perspectives concernant les mécanismes qui sous-tendent le couplage troposphère-stratosphère et l'intérêt d'une prévision statistico-dynamique consistant à relaxer le modèle ARPEGE-Climat vers une stratosphère prévue de manière statistique. / In the Northern Extratropics, winter climate shows a large inter-annual variability compared to other regions and seasons, with the occurrence of extreme weather events such as cold spells, heavy snowfall and wind storms. Unfortunately, current dynamical seasonal forecasting systems still show low predictability in the northern mid-latitudes. Besides ocean-atmosphere coupling, there is growing observational and numerical evidence that troposphere-stratosphere coupling also contributes to climate variability on a wide range of scales. The aim of this thesis is to evaluate this additional forcing by focusing on the stratospheric polar vortex influence on the wintertime climate variability in the northern mid-latitudes at inter-annual and intra-seasonal timescales. We first make a synthesis of the knowledge about troposphere-stratosphere coupling. The inter-annual variability of the stratospheric polar vortex is assessed using composite analysis of atmospheric ECMWF reanalysis. Then, the main tools used during this thesis are described, namely the ARPEGE-Climat model and the nudging of the stratosphere towards the ECMWF reanalysis. Like many other models, ARPEGE-Climat has a polar jet which is too weak and displaced southward compared to reanalysis data, regardless his version, which could have negative consequences on the wintertime variability. Although the poorly resolved stratosphere is often suggested to explain the lack of previsibility in the models, our results show that, in the ARPEGE-Climat V5 model, the improved vertical resolution is not sufficient to simulate a more realistic climate variability and predictability. For these reasons, throughout this thesis, the stratospheric relaxation towards the reanalysis data from the ECMWF is used in order to compare his relative contribution to the tropospheric extra-tropical climate variability, compared to the sea surface temperature forcing. Case studies have been carried out for the 1976-1977 and 2009-2010 winters though ensemble simulations with free or nudged stratosphere. Results confirm the relative contribution of the stratosphere to the negative phase of the NAO and the temperature anomalies over the northern Europe. Similar initialized ensemble simulations over the 1958-2007 period confirm the positive impact of the extra-tropical nudging, but show a limited influence of the equatorial nudging, that needs to be analyzed more precisely with a larger ensemble. In conclusion, this thesis shows the importance of a realistic stratosphere in the extra-tropical seasonal forecast in winter, but a lot of questions remains opened, like mechanisms related to the troposphere-stratosphere coupling, and the interest of a statistico-dynamical forecast including a relaxation towards a statistical stratosphere

Despite the tremendous amount of phonetic variability in speech across talkers, listeners seem to effortlessly process acoustic signals while attending to both the linguistic content and talker-specific information. Previous studies have explained this phenomenon by providing evidence for talker normalization, a process in which our perceptual system strips away information about a talker so that the abstract, canonical linguistic units are all that remain for further linguistic analysis. However, it is currently unknown whether or how talker normalization is facilitated by familiar talkers. In this study, we investigated whether talker familiarity had an impact on the speed in which listeners perceived highly confusable words under varying contexts. Over the course of three days, listeners were explicitly trained on the voices of four talkers. Baseline and post-test measures were administered to determine the effect of talker training and to see whether this effect was impacted by the presence of a carrier phrase as well as the variability of talker presentation. The results demonstrated that listeners adapted to the talker regardless of familiarity. Having immediate information about a talker from preceding speech appeared to play a larger role in managing talker variability than a long-term familiarity with the talker’s voice. Our findings suggest that talker normalization is a feedforward process that does not rely on prior memory traces.