Tonal placement in Tashlhiyt

In most languages, words contain vowels, elements of high intensity with rich harmonic structure, enabling the perceptual retrieval of pitch. By contrast, in Tashlhiyt, a Berber language, words can be composed entirely of voiceless segments. When an utterance consists of such words, the phonetic opportunity for the execution of intonational pitch movements is exceptionally limited. This book explores in a series of production and perception experiments how these typologically rare phonotactic patterns interact with intonational aspects of linguistic structure. It turns out that Tashlhiyt allows for a tremendously flexible placement of tonal events. Observed intonational structures can be conceived of as different solutions to a functional dilemma: The requirement to realise meaningful pitch movements in certain positions and the extent to which segments lend themselves to a clear manifestation of these pitch movements

How an intonation system accommodates to adverse phonological environments

In most languages, words contain vowels, elements of high intensity with rich harmonic structure, enabling the perceptual retrieval of pitch. By contrast, in Tashlhiyt, a Berber language, words can be composed entirely of voiceless segments. When an utterance consists of such words, the phonetic opportunity for the execution of intonational pitch movements is exceptionally limited. This book explores in a series of production and perception experiments how these typologically rare phonotactic patterns interact with intonational aspects of linguistic structure. It turns out that Tashlhiyt allows for a tremendously flexible placement of tonal events. Observed intonational structures can be conceived of as different solutions to a functional dilemma: The requirement to realise meaningful pitch movements in certain positions and the extent to which segments lend themselves to a clear manifestation of these pitch movements

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

A community-sourced glossary of open scholarship terms

Open scholarship has transformed research, introducing a host of new terms in the lexicon of researchers. The Framework of Open and Reproducible Research Teaching (FORRT) community presents a crowd-sourced glossary of open scholarship terms to facilitate education and effective communication between experts and newcomers

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

Preregistration in experimental linguistics: applications, challenges, and limitations

Abstract The current publication system neither incentivizes publishing null results nor direct replication attempts, which biases the scientific record toward novel findings that appear to support presented hypotheses (referred to as “publication bias”). Moreover, flexibility in data collection, measurement, and analysis (referred to as “researcher degrees of freedom”) can lead to overconfident beliefs in the robustness of a statistical relationship. One way to systematically decrease publication bias and researcher degrees of freedom is preregistration. A preregistration is a time-stamped document that specifies how data is to be collected, measured, and analyzed prior to data collection. While preregistration is a powerful tool to reduce bias, it comes with certain challenges and limitations which have to be evaluated for each scientific discipline individually. This paper discusses the application, challenges and limitations of preregistration for experimental linguistic research

Researcher degrees of freedom in phonetic research

The results of published research critically depend on methodological decisions that have been made during data analysis. These so-called ‘researcher degrees of freedom’ (Simmons, Nelson, & Simonsohn, 2011) can affect the results and the conclusions researchers draw from it. It is argued that phonetic research faces a large number of researcher degrees of freedom due to its scientific object—speech—being inherently multidimensional and exhibiting complex interactions between multiple covariates. A Type-I error simulation is presented that demonstrates the severe inflation of false positives when exploring researcher degrees of freedom. It is argued that combined with common cognitive fallacies, exploitation of researcher degrees of freedom introduces strong bias and poses a serious challenge to quantitative phonetics as an empirical science. This paper discusses potential remedies for this problem including adjusting the threshold for significance; drawing a clear line between confirmatory and exploratory analyses via preregistration; open, honest, and transparent practices in communicating data analytical decisions; and direct replications