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In the last decade, educational neuroscience has become increasingly important in the context of instruction, and its applications have been transformed into new teaching methods. Although teachers are interested in educational neuroscience, communication between scientists and teachers is not always straightforward. Thus, misunderstandings of neuroscientific research results can evolve into so-called neuromyths. The aim of the present study was to investigate the prevalence of such music-related neuromyths among music teachers and music students. Based on an extensive literature research, 26 theses were compiled and subsequently evaluated by four experts. Fourteen theses were selected, of which seven were designated as scientifically substantiated and seven as scientifically unsubstantiated (hereafter labeled as “neuromyths”). One group of adult music teachers (n = 91) and one group of music education students (n = 125) evaluated the theses (forced-choice discrimination task) in two separate online surveys. Additionally, in both surveys person-characteristic variables were gathered to determine possible predictors for the discrimination performance. As a result, identification rates of the seven scientifically substantiated theses were similar for teachers (76%) and students (78%). Teachers and students correctly rejected 60 and 59%, respectively, of the seven neuromyths as scientifically unsubstantiated statements. Sensitivity analysis by signal detection theory revealed a discrimination performance of d' = 1.25 (SD = 1.12) for the group of teachers and d' = 1.48 (SD = 1.22) for the students. Both groups showed a general tendency to evaluate the theses as scientifically substantiated (teachers: c = −0.35, students: c = −0.41). Specifically, buzz words such as “brain hemisphere” or “cognitive enhancement” were often classified as correct. For the group of teachers, the best predictor of discrimination performance was having read a large number of media about educational neuroscience and related topics (R2 = 0.06). For the group of students, the best predictors for discrimination performance were a high number of read media and the hitherto completed number of semesters (R2 = 0.14). Our findings make clear that both teachers and students are far from being experts on topics related to educational neuroscience in music and would therefore benefit from current education-related research in psychology and neuroscience.
Over the last decades, the simulation of musical instruments by digital means has become an important part of modern music production and live performance. Since the first release of the Kemper Profiling Amplifier (KPA) in 2011, guitarists have been able to create and store a nearly unlimited number of “digital fingerprints” of amplifier and cabinet setups for live performances and studio productions. However, whether listeners can discriminate between the sounds of the KPA and the original amplifier remains unclear. Thus, we constructed a listening test based on musical examples from both sound sources. In a first approach, the psychoacoustic analysis using mel-frequency cepstrum coefficients (MFCCs) revealed a high degree of timbre similarity between the two sound sources. In a second step, a listening test with N = 177 showed that the overall discrimination performance was d’ = .34, which was a rather small difference (0.0 ≤ d’ ≤ 0.74). A weak relationship between the degree of general musical sophistication and discrimination performance was found. Overall, we suggest that listeners are rarely able to assign audio examples to the correct condition. We conclude that, at least on a perceptual level, our results give no support for a commonly accepted pessimistic attitude toward digital simulations of hardware sounds.
Signal detection theory gives a framework for determining how well participants can discriminate between two types of stimuli. This article first examines similarities and differences of forced-choice and A–Not A designs (also known as the yes-no or one-interval). Then it focuses on the latter, in which participants have to classify stimuli, presented to them one at a time, as belonging to one of two possible response categories. The A–Not A task can be, on a first level, replicated or non-replicated, and the sub-design for each can be, on a second level, either a monadic, a mixed, or a paired design. These combinations are explained, and the present article then focuses on the both the non-replicated and replicated paired A–Not A task. Data structure, descriptive statistics, inference statistics, and effect sizes are explained in general and based on example data (Düvel et al., 2020). Documents for the data analysis are given in an extensive online supplement. Furthermore, the important question of statistical power and required sample size is addressed, and several means for the calculation are explained. The authors suggest a standardized procedure for planning, conducting, and evaluating a study employing an A–Not A design.
As predicted by Holbrook and Schindler (1989) in a seminal study, popular music from the charts released when a person is roughly 23.47 years old (so-called Song-Specific Age, SSA) has a particularly positive impact on that person’s song evaluations decades later. In our replication study, N = 162 participants (M age = 59.1 years, SD = 17.3) indicated their preferences for 18 song excerpts randomly selected from a corpus of 87 German Top 10 chart hits, released between 1930 and 2017. The fitting of a quadratic curve (as in the original study) to the aggregated ratings revealed a much earlier overall SSA peak at 14.16 years (R 2 = .184). The best approximation to the original SSA peak of 23.47 years was found only for the elderly subgroup of participants aged 50+ years with an SSA value of 22.63 years, however, with a relatively low goodness-of-fit (R 2 = .225). To summarize, the original finding of an SSA peak in the phase of early adulthood (23.47 years) could not be confirmed in terms of a point estimate. Instead, various subgroups showed various SSA peaks. The decomposition of ratings on the song level by latent profile analysis revealed four basic rating patterns (constantly high, constantly low, increasing, and decreasing over time) that might explain the different findings of the overall course of SSA regression curves within our subgroups without reference to the concept of SSA. Results are discussed in favor of current dynamic models of lifelong changes in musical preferences. We conclude that today, the SSA proposition – at least in its original form – seems to be of only limited validity for the explanation of musical preferences.