Implicit learning of recursive context-free grammars

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning

Comments on "Facilitation and Coherence Between the Dynamic and Retrospective Perception of Segmentation in Computer-Generated Music," by Freya Bailes and Roger T. Dean

Although the study by Bailes & Dean (2007) addresses an underresearched area of auditory and musical perception, it raises questions concerning stimuli, methodology, and the study's relation to previous research, that are outlined in this commentary

Motivations To Produce User Generated Content: Differences Between Webloggers And Videobloggers

This explorational study seeks to elucidate the question of what motivates weblogger and videoblogger to produce user generated content. Particular focus was laid on the question whether motivational differences can be discerned between webloggers and video producers and why people do not produce content. The findings show that it is the intrinsic motivations that are responsible for today’s user generated content. Video producers and webloggers differ in their motivations. Video production is more associated with fun and time passing than is weblogging. Weblogging is regarded as being more useful in the dissemination of information. The main reasons for not producing content are opportunity costs and privacy issues

A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences

Within the framework of statistical learning, many behavioural studies investigated the processing of unpredicted events. However, surprisingly few neurophysiological studies are available on this topic, and no statistical learning experiment has investigated electroencephalographic (EEG) correlates of processing events with different transition probabilities. We carried out an EEG study with a novel variant of the established statistical learning paradigm. Timbres were presented in isochronous sequences of triplets. The first two sounds of all triplets were equiprobable, while the third sound occurred with either low (10%), intermediate (30%), or high (60%) probability. Thus, the occurrence probability of the third item of each triplet (given the first two items) was varied. Compared to high-probability triplet endings, endings with low and intermediate probability elicited an early anterior negativity that had an onset around 100 ms and was maximal at around 180 ms. This effect was larger for events with low than for events with intermediate probability. Our results reveal that, when predictions are based on statistical learning, events that do not match a prediction evoke an early anterior negativity, with the amplitude of this mismatch response being inversely related to the probability of such events. Thus, we report a statistical mismatch negativity (sMMN) that reflects statistical learning of transitional probability distributions that go beyond auditory sensory memory capabilities

A semi-automated workflow paradigm for the distributed creation and curation of expert annotations

The creation and curation of labeled datasets can be an arduous, expensive, and time-consuming task. We introduce a workflow paradigm for remote consensus-building between expert annotators, while considerably reducing the associated administrative overhead through automation. Most music annotation tasks rely heavily on human interpretation and therefore defy the concept of an objective and indisputable ground truth. Thus, our paradigm invites and documents inter-annotator controversy based on a transparent set of analytical criteria, and aims at putting forth the consensual solutions emerging from such deliberations. The workflow that we suggest traces the entire genesis of annotation data, including the relevant discussions between annotators, reviewers, and curators. It adopts a well-proven pattern from collaborative software development, namely distributed version control, and allows for the automation of repetitive maintenance tasks, such as validity checks, message dispatch, or updates of meta- and paradata. To demonstrate the workflow's effectiveness, we introduce one possible implementation through GitHub Actions and showcase its success in creating cadence, phrase, and harmony annotations for a corpus of 36 trio sonatas by Arcangelo Corelli. Both code and annotated scores are freely available and the implementation can be readily used in and adapted for other MIR projects

Pessimistic outcome expectancy does not explain ambiguity aversion in decision-making under uncertainty.

When faced with a decision, most people like to know the odds and prefer to avoid ambiguity. It has been suggested that this aversion to ambiguity is linked to people's assumption of worst possible outcomes. We used two closely linked behavioural tasks in 78 healthy participants to investigate whether such pessimistic prior beliefs can explain ambiguity aversion. In the risk-taking task, participants had to decide whether or not they place a bet, while in the beliefs task, participants were asked what they believed would be the outcome. Unexpectedly, we found that in the beliefs task, participants were not overly pessimistic about the outcome in the ambiguity condition and in fact closer to optimal levels of decision-making than in the risk conditions. While individual differences in pessimism could explain outcome expectancy, they had no effect on ambiguity aversion. Consequently, ambiguity aversion is more likely caused by general caution than by expectation of negative outcomes despite pessimism-dependent subjective weighting of probabilities

The diachronic development of Debussy’s musical style: a corpus study with Discrete Fourier Transform

Claude Debussy’s personal style is typically characterised as a departure from earlier diatonic tonality, including a greater variety of pitch-class materials organised in fragmented yet coherent compositions. Exploiting the music-theoretical interpretability of Discrete Fourier Transforms over pitch-class distributions, we performed a corpus study over Debussy’s solo-piano works in order to investigate the diachronic development of such stylistic features across the composer’s lifespan. We propose quantitative heuristics for the prevalence of different pitch-class prototypes, the fragmentation of a piece across different prototypes, as well as some aspect of the overall coherence of a piece. We found strong evidence for a decrease of diatonicity in favour of octatonicity, as well as for an increase of fragmentation accompanied by non-decreasing coherence. These results contribute to the understanding of the historical development of extended-tonal harmony, while representing a fertile testing ground for the interaction of computational corpus-based methods with traditional music analytical approaches