This episode continues the work of the previous episode by examining the methods used to convert between decimal and binary and vice versa. We also take a look at the effects of shifting the bits of a binary number both left and right and how those operations can be used to simulate multiplication and division. Oh, and since we will be discussing a lot of different numbers, it couldn’t hurt to have a piece of paper and a pencil close by.

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Reward prediction errors are quantitative signed terms that express the difference between the value of an obtained outcome and the expected value that was placed on it prior to its receipt. Positive reward prediction errors constitute reward, negative reward prediction errors constitute punishment. Reward prediction errors have been shown to be powerful drivers of reinforcement learning in formal models and there is thus a strong reason to believe they are used in the brain. Isolating such neural signals stands to help elucidate how reinforcement learning is implemented in the brain, and may ultimately shed light on individual differences, psychopathologies of reward such as addiction and depression, and the apparently non-normative behaviour under risk described by behavioural economics. In the present thesis, I used the event related potential technique to isolate and study electrophysiological components whose behaviour resembled reward prediction errors. I demonstrated that a candidate component, “feedback related negativity”, occurring 250 to 350 ms after receipt of reward or punishment, showed such behaviour. A meta-analysis of the existing literature on this component, using a novel technique of “great grand averaging”, supported this view. The component showed marked asymmetries however, being more responsive to reward than punishment and more responsive to appetitive rather than aversive outcomes. I also used novel data-driven techniques to examine activity outside the temporal interval associated with the feedback related negativity. This revealed a later component responding solely to punishments incurred in a Pavlovian learning task. It also revealed numerous salience-encoding components which were sensitive to a prediction error’s size but not its sign.

Social media has provided a way for music artists to reach many people with their music, without having to rely on record labels to perform marketing tasks. Most previous research within the area has focused on how already established music artists can use social media as part of their marketing strategies and how digital technologies have transformed the music industry. This study focuses on how unsigned music artists’ followers and fans on social media have an impact on their music streaming numbers. The main research question of the study is: how does unsigned artists’ social media performance affect the number of streams of their music? To investigate this, a robust regression model was defined with the aim of predicting the number of artists’ music streams based on their social media data. The robust regression model showed that the social media variables did not have significant effects on the number of streams. Therefore, an analysis of each individual artist in the data was conducted. The results showed that the social media data in this study could not be used to explain changes in the number of streams for unsigned music artists. An analysis based on each individual artist and the content that each individual artist is posting on the different social media channels, is suggested instead. An information visualization tool was developed with the purpose of allowing analysts to get an overview of the social media data as well as allow analysts to look at each artist’s social media feeds to understand how artists’ social media activities affect their music streaming data. / Sociala medier har gjort det möjligt för musikartister att nå många människor med sin musik utan att behöva förlita sig på skivbolag. Tidigare forskning inom området har fokuserat på hur redan etablerade musikartister kan använda sociala medier som en del av sina marknadsstrategier och hur digital teknik har förändrat musikbranschen. Denna studie fokuserar på hur osignerade musikartisters antal anhängare och fans på sociala medier påverkar antalet streams av artisternas musik. Studiens huvudsakliga forskningsfråga är: Hur påverkar osignerade artisters prestationer på sociala medier antalet streams av deras musik? För att undersöka detta definierades en robust regressionsmodell i syfte att förutse antalet streams av artisternas musik baserat på deras sociala mediedata. Den robusta regressionsmodellen visade att socialamedievariablerna inte hade signifikanta effekter på antalet streams av artisternas musik. Därför genomfördes en analys av varje enskild artist i datan. Resultaten visade att sociala mediedatan i denna studie inte kunde användas för att förklara förändringar i antalet streams för osignerade musikartister. En analys baserad på varje enskild artist och innehållet som varje enskild artist lägger ut på de olika sociala mediekanalerna föreslås istället. Ett informationsvisualiseringsverktyg utvecklades med syftet att ge analytiker en möjlighet att få en överblick över sociala mediedatan samt låta analytiker titta på varje artists sociala medieflöden för att förstå hur artisternas sociala medier påverkar deras musikstreamingdata.