Notre but était de mieux comprendre les mécanismes associés à la récupération en mémoire à court terme acoustique à l’aide de mesures électrophysiologiques. La tâche utilisée était une version modifiée de la tâche de Sternberg dans laquelle les participants devaient retenir deux, quatre ou six sons purs hors de la gamme musical bien tempérée, ne pouvant pas être facilement recodés verbalement. Après un intervalle de rétention silencieux, ils entendaient un son et devaient indiquer si celui-ci était présent ou absent dans l’ensemble mémoire. En utilisant plusieurs conditions de charge et en contrôlant pour les durées de stimulation, nous avons comparé les effets de position sérielle, ainsi que les effets de charge, sur les données comportementales et sur les données électrophysiologiques mesurées lors du balayage mnésique. Notamment, nous avons trouvé un effet de récence important peu importe le nombre d’items mémorisés, se traduisant par des temps de réponses courts et des taux de bonne réponse presque parfaits, ainsi que par l’augmentation de l’amplitude de la P3, une composante de potentiels reliés aux évènements (PRE). Les données liées aux autres positions sérielles étaient similaires, indépendamment de la charge mnésique, et montraient des performances moins bonnes et une P3 moins ample. Une méthode de décomposition du signal, l’analyse en composantes indépendantes (ACI) nous a permis d’observer et de décrire les différences électrophysiologiques, dans le temps, entre la récupération d’un son parfaitement retenu (le dernier) et celle d’un son peu retenu. Nos résultats suggèrent l’existence de deux sous-types de mémoire sensorielle. / We aimed to understand better the processes involved in acoustic short-term memory retrieval using electrophysiology. We used a modified Sternberg task in which participants had to encode two, four, or six pure non-musical tones (out of the well-tempered musical scale) that cannot be readily recoded verbally. After a silent retention interval, we presented them with a probe tone and they had to report its presence or absence in the memory set. By using multiple load conditions, and by controlling for stimulation duration, we compared the serial position effects, as well as the load effects, on behavioral and electrophysiological data during memory scanning. In particular, we found a recency effect, similar across loads, where shorter response times, near perfect accuracy, and an increased P3 amplitude in the event-related potential (ERP) data were observed. Serial position data for all other positions were similar regardless of the memory load and showed lower performances (response times and accuracy), as well as smaller P3 components. We also performed a signal decomposition analysis, the independent component analysis (ICA), which allowed us to observe and describe better the time courses of the electrophysiological data for the retrieval of a perfectly memorised tone (the last one), and a lesser memorised one (any other serial position). Our results suggest the existence of two subtypes of sensory memory.

Human capability to recall episodic memories depends on how much time has passed since the memory was encoded. This dependency is described by a memory retrieval curve that reflects an interesting phenomenon referred to as a reminiscence bump - a tendency for older people to recall more memories formed during their young adulthood than in other periods of life. This phenomenon can be modelled with an attractor neural network, for example, the firing-rate Bayesian Confidence Propagation Neural Network (BCPNN) with incremental learning. In this work, the mechanisms underlying the reminiscence bump in the neural network model are systematically studied. The effects of synaptic plasticity, network architecture and other relevant parameters on the characteristics of the reminiscence bump are systematically investigated. The most influential factors turn out to be the magnitude of dopamine-linked plasticity at birth and the time constant of exponential plasticity decay with age that set the position of the bump. The other parameters mainly influence the general amplitude of the lifespan retrieval curve. Furthermore, the recency phenomenon, i.e. the tendency to remember the most recent memories, can also be parameterized by adding a constant to the exponentially decaying plasticity function representing the decrease in the level of dopamine neurotransmitters. / Människans förmåga att återkalla episodiska minnen beror på hur lång tid som gått sedan minnena inkodades. Detta beroende beskrivs av en sk glömskekurva vilken uppvisar ett intressant fenomen som kallas ”reminiscence bump”. Detta är en tendens hos äldre att återkalla fler minnen från ungdoms- och tidiga vuxenår än från andra perioder i livet. Detta fenomen kan modelleras med ett neuralt nätverk, sk attraktornät, t ex ett icke spikande Bayesian Confidence Propagation Neural Network (BCPNN) med inkrementell inlärning. I detta arbete studeras systematiskt mekanismerna bakom ”reminiscence bump” med hjälp av denna neuronnätsmodell. Exempelvis belyses betydelsen av synaptisk plasticitet, nätverksarkitektur och andra relavanta parameterar för uppkomsten av och karaktären hos detta fenomen. De mest inflytelserika faktorerna för bumpens position befanns var initial dopaminberoende plasticitet vid födseln samt tidskonstanten för plasticitetens avtagande med åldern. De andra parametrarna påverkade huvudsakligen den generella amplituden hos kurvan för ihågkomst under livet. Dessutom kan den s k nysseffekten (”recency effect”), dvs tendensen att bäst komma ihåg saker som hänt nyligen, också parametriseras av en konstant adderad till den annars exponentiellt avtagande plasticiteten, som kan representera densiteten av dopaminreceptorer.

The aim of this study was to examine the validity and feasibility of sentence transcription testing (STT) for the purpose of examining the interplay between verbal working memory and central processing. The general area of interest is to understand working memory as a dynamic system that involves the management and integration of information from several temporal distances. Due to the world-wide conditions at the time this study was conducted (2020), the testing was online and computerized, which severely limited the controllability of the procedure leading to a high amount of exclusions and dubious results. The testing of 17 subjects, 9 females, 8 males with the average age of 30.5 (SD = 9.5) yielded mixed results, excluding gender, impulsivity and age as likely factors for the variance. Following these results, a post hoc analysis was added to interpret if transcription data has validity as a tool for observing effects of interference on memory recall and the task at hand. This analysis did reveal patterns that reinforce the view of language processing as a multimodal task. The type of errors seems to follow tendencies of primacy, recency and availability, as well as proactive and retroactive interference. These tendencies of memory recall seem to work in unison or is a manifestation of syntactic, lexical, and presumably semantic processing and can be used to measure individual differences in language processing and the tendency to linguistically “fill in the gaps”. The variation seen within the sample does make transcription testing appealing for further studies. The main variance within the sample can be described as replacing words with other previously attended to information, and or forgetting words during transcription. These tendencies, might reveal properties about the interaction between executive function (EF) and verbal working memory (V-WM) as a source of individual difference. However, more validation studies are proposed for weaving out factors that might skew the results in this type of testing and modelling.

This master thesis deals with design and implementation of betting agent. It covers issues such as theoretical background of an online betting, probability and statistics. In its first part it is focused on data mining and explains the principle of knowledge mining form data warehouses and certain methods suitable for different types of tasks. Second, it is concerned with neural networks and algorithm of back-propagation. All the findings are demonstrated on and supported by graphs and histograms of data analysis, made via SAS Enterprise Miner program. In conclusion, the thesis summarizes all the results and offers specific methods of extension of the agent.