Deutsches Institut für Internationale Pädagogische Forschung (DIPF)
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Imageability and emotionality ratings for 2592 German nouns (3–10 letters, one to three phonological syllables) were obtained from younger adults (21–31 years) and older adults (70–86 years). Valid ratings were obtained on average from 20 younger and 23 older adults per word for imageability, and from 18 younger and 19 older adults per word for emotionality. The internal consistency (Cronbach’s α) and retest rank-order stability of the ratings were high for both age groups (α and r ≥ .97). Also, the validity of our ratings was found to be high, as compared to previously published ratings (r ≥ .86). The ratings showed substantial rank-order stability across younger and older adults (imageability, r = .94; emotionality, r = .85). At the same time, systematic differences between age groups were found in the mean levels of ratings (imageability, d = 0.38; emotionality, d = 0.20) and in the extent to which the rating scales were used (imageability, SD = 24 vs. 19, scale of 0 to 100; emotionality, SD = 26 vs. 31, scale of −100 to 100). At the descriptive level, our data hint at systematically different evaluations of semantic categories regarding imageability and emotionality across younger and older adults. Given that imageability and emotionality have been reported, for instance, as important determinants for the recognition and recall of words, our findings highlight the importance of considering age-specific information in age-comparative cognitive (neuroscience) experimental studies using word materials. The age-specific imageability and emotionality ratings for the 2592 German nouns can be found in the electronic supplementary material...
Multi-scale entropy (MSE) has been recently established as a promising tool for the analysis of the moment-to-moment variability of neural signals. Appealingly, MSE provides a measure of the predictability of neural operations across the multiple time scales on which the brain operates. An important limitation in the application of the MSE to some classes of neural signals is MSE's apparent reliance on long time series. However, this sparse-data limitation in MSE computation could potentially be overcome via MSE estimation across shorter time series that are not necessarily acquired continuously (e.g., in fMRI block-designs). In the present study, using simulated, EEG, and fMRI data, we examined the dependence of the accuracy and precision of MSE estimates on the number of data points per segment and the total number of data segments. As hypothesized, MSE estimation across discontinuous segments was comparably accurate and precise, despite segment length. A key advance of our approach is that it allows the calculation of MSE scales not previously accessible from the native segment lengths. Consequently, our results may permit a far broader range of applications of MSE when gauging moment-to-moment dynamics in sparse and/or discontinuous neurophysiological data typical of many modern cognitive neuroscience study designs.