Age-Related Patterns in Emotions Evoked by Music

We presented older and younger nonmusician adult listeners with (mostly) unfamiliar excerpts of film music. All listeners rated their emotional reaction using the Geneva Emotional Music Scale 9 (GEMS-9; Zentner, Grandjean, & Scherer, 2008), and also rated familiarity and liking. The GEMS-9 was factor-analyzed into 3 factors of Animacy, Valence, and Arousal. Although the 2 age groups liked the music equally well, and showed roughly the same pattern of responses to the different emotion categories, the younger group showed a wider range of emotional reactivity on all the factors. We found support for a type of positivity effect, in that older people found Happy music somewhat less happy than did younger people, but found Sad music much less sad than did younger people. Older people also rated Fearful music more positively than did younger people. We propose that the GEMS-9 scale is an efficient and effective device to collect evoked emotion data for a wide age range of listeners

David Temperley, Music and Probability

review of David Temperley's "Music and Probability". Cambridge, Massachusetts: MIT Press, 2007, ISBN-13: 978-0-262-20166-7 (hardcover) $40.00

Melodic expectations in 5- and 6-year-old children

It has been argued that children implicitly acquire the rules relating to the structure of music in their environment using domain-general mechanisms such as statistical learning. Closely linked to statistical learning is the ability to form expectations about future events. Whether children as young as 5 years can make use of such internalized regularities to form expectations about the next note in a melody is still unclear. The possible effect of the home musical environment on the strength of musical expectations has also been under-explored. Using a newly developed melodic priming task that included melodies with either “expected” or “unexpected” endings according to rules of Western music theory, we tested 5- and 6-year-old children (N = 46). The stimuli in this task were constructed using the information dynamics of music (IDyOM) system, a probabilistic model estimating the level of “unexpectedness” of a note given the preceding context. Results showed that responses to expected versus unexpected tones were faster and more accurate, indicating that children have already formed robust melodic expectations at 5 years of age. Aspects of the home musical environment significantly predicted the strength of melodic expectations, suggesting that implicit musical learning may be influenced by the quantity of informal exposure to the surrounding musical environment

Melodic expectations in 5- and 6-year-old children

It has been argued that children implicitly acquire the rules relating to the structure of music in their environment using domain-general mechanisms such as statistical learning. Closely linked to statistical learning is the ability to form expectations about future events. Whether children as young as 5 years can make use of such internalized regularities to form expectations about the next note in a melody is still unclear. The possible effect of the home musical environment on the strength of musical expectations has also been under-explored. Using a newly developed melodic priming task that included melodies with either “expected” or “unexpected” endings according to rules of Western music theory, we tested 5- and 6-year-old children (N = 46). The stimuli in this task were constructed using the information dynamics of music (IDyOM) system, a probabilistic model estimating the level of “unexpectedness” of a note given the preceding context. Results showed that responses to expected versus unexpected tones were faster and more accurate, indicating that children have already formed robust melodic expectations at 5 years of age. Aspects of the home musical environment significantly predicted the strength of melodic expectations, suggesting that implicit musical learning may be influenced by the quantity of informal exposure to the surrounding musical environment

Local travel plan groups : a practical guide to setting up an effective group

Shortly after becoming Mayor of London in July 2000, Ken Livingstone noted that “the single biggest problem for London and Londoners is the gridlock of our transport system” and that “remedying this will be my first priority”. Although predominantly concerned with the inadequacy of public transport in the capital, the Mayor added that “traffic speeds in central London are now just 10 miles per hour, while congestion costs London business £5 billion per year. Residents and commuters alike suffer from delays, stress, discomfort and the overall poor urban environment.”i To help address these problems,Transport for London (TfL) is encouraging businesses and other organisations to develop workplace travel plans. Developing and implementing a workplace travel plan requires resources and expertise, so it can be easier for businesses located in the same area to get together and form a local travel plan group. This good practice guide sets out the process of establishing a local travel plan group, based on research conducted for the Optimum2 project in the London Borough of Southwark, in which the Better Bankside Travel Plan Group was established (see Acknowledgments for further information)

Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks

Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance functions of networks with activation functions used in modern networks remain unknown. Furthermore, while the kernels of deep networks can be computed iteratively, theoretical understanding of deep kernels is lacking, particularly with respect to fixed-point dynamics. Firstly, we derive the covariance functions of MLPs with exponential linear units and Gaussian error linear units and evaluate the performance of the limiting Gaussian processes on some benchmarks. Secondly, and more generally, we introduce a framework for analysing the fixed-point dynamics of iterated kernels corresponding to a broad range of activation functions. We find that unlike some previously studied neural network kernels, these new kernels exhibit non-trivial fixed-point dynamics which are mirrored in finite-width neural networks.Comment: 18 pages, 9 figures, 2 tables. Corrected name particle capitalisation and formattin

Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation

The ability to anticipate forthcoming events has clear evolutionary advantages, and predictive successes or failures often entail significant psychological and physiological consequences. In music perception, the confirmation and violation of expectations are critical to the communication of emotion and aesthetic effects of a composition. Neuroscientific research on musical expectations has focused on harmony. Although harmony is important in Western tonal styles, other musical traditions, emphasizing pitch and melody, have been rather neglected. In this study, we investigated melodic pitch expectations elicited by ecologically valid musical stimuli by drawing together computational, behavioural, and electrophysiological evidence. Unlike rule-based models, our computational model acquires knowledge through unsupervised statistical learning of sequential structure in music and uses this knowledge to estimate the conditional probability (and information content) of musical notes. Unlike previous behavioural paradigms that interrupt a stimulus, we devised a new paradigm for studying auditory expectation without compromising ecological validity. A strong negative correlation was found between the probability of notes predicted by our model and the subjectively perceived degree of expectedness. Our electrophysiological results showed that low-probability notes, as compared to high-probability notes, elicited a larger (i) negative ERP component at a late time period (400–450 ms), (ii) beta band (14–30 Hz) oscillation over the parietal lobe, and (iii) long-range phase synchronization between multiple brain regions. Altogether, the study demonstrated that statistical learning produces information-theoretic descriptions of musical notes that are proportional to their perceived expectedness and are associated with characteristic patterns of neural activity