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

Temporal response modelling uncovers electrophysiological correlates of trial-by-trial error-driven learning

Humans learn from statistical regularities in the environment. We tested if prediction and prediction error may play a role in such learning in the brain. We used Error-Driven Learning (EDL) to simulate participants’ trial-by-trial learning during exposure to a bimodal distribution of non-native lexical tones. We simulated incremental trial-by-trial learning to get estimates of the degree of expectation of upcoming stimuli over the course of the experiment. The expectation estimates were combined with Temporal Response Function fitting to generate a prediction of the trial-by-trial ERP waveform. EDL simulations captured the data significantly better than chance and better than models based on either stimulus characteristics or statistical distributions. The results provide tentative evidence that trial-by-trial learning as measured in neural activity is error-driven

The imitation of coarticulatory timing patterns in consonant clusters for phonotactically familiar and unfamiliar sequences

This paper investigates to what extent speakers adapt to unfamiliar consonant cluster timing patterns. We exploit naturally occurring consonant overlap differences between German and Georgian speakers’ productions to probe the constraints that language-specific patterns put on the flexibility of cluster articulation. We recorded articulography data from Georgian and German speakers imitating CCV clusters as produced by a German and Georgian audio model, respectively. The German participants adapted their relative overlap towards the Georgian audio model to various degrees depending on whether the cluster was phonotactically familiar to them or not. A higher degree of adaptation was observed for clusters phonotactically illegal in German. Phonotactically legal clusters showed only an intermediate degree of articulatory adaptation, even though acoustically these clusters showed a rather strong move towards the Georgian audio model in terms of the aerodynamics of the interconsonantal transition period. Georgian speakers on the other hand failed to adapt to the German audio model articulatorily and acoustically, possibly because the German cluster inventory is a subset of the Georgian inventory. This means that Georgian speakers can draw on native speaker knowledge for all clusters, which is a factor known to constrain imitation. Also language-specific cue weighting effects may partly condition the results

Language and cluster-specific effects in the timing of onset consonant sequences in seven languages

In this paper, we draw on available data from previous experiments to explore cross-linguistic variation in articu- latory overlap in CC onset clusters, taking into account the role of cluster composition. Our sample includes artic- ulography recordings of eleven clusters for seven languages. We find that cross-linguistic variability is conditional on cluster composition. Previous suggestions that languages may have individual global articulatory timing profiles for consonant clusters in terms of an overall relatively lower or higher degree of overlap are not confirmed for our sample. All included languages converge on a relatively higher degree of overlap for some of the clusters, whereas only some of the languages additionally extend into the lower overlap range, particularly for stop-sonorant sequences. Manner and voicing are further identified as factors conditioning variation in consonantal overlap. Overall languages differ in their degree of overlap in multi-faceted ways, but the relative effects of cluster composition work in the same direction across languages