Analyzing Baseline Phase Data

This chapter discusses the analysis of the baseline phase. The baseline serves as the comparison for information collected during subsequent phases. It allows the researcher or practitioner to determine if the target behaviors are changing in a desirable or undesirable direction. Two different types of baselines are presented, concurrent and reconstructed. In a concurrent baseline, data are collected simultaneously, while other assessment activities are being conducted. A reconstructed baseline is an attempt to approximate naturally occurring behavior based on memories or case records. Issues related to comparing phases are discussed and illustrated, including stability of the baseline, trending data, and autocorrelation (or serial dependency). Guidance is provided on how each of these can be assessed and addressed, including the transformation of highly autocorrelated data. Examples are provided throughout to illustrate each concept.

Simulations of implicit learning of symmetries: The importance of prior knowledge and the nature of the memory buffer

The current research aimed to investigate the role that prior knowledge played in what structures could be implicitly learnt and also the nature of the memory buffer required for learning such structures. It is already established that people can implicitly learn to detect an inversion symmetry (i.e. a cross-serial dependency) based on linguistic tone types. The present study investigated the ability of the Simple Recurrent Network (SRN) to explain implicit learning of such recursive structures. We found that the SRN learnt the symmetry over tone types more effectively when given prior knowledge of the tone types (i.e. of the two categories tones were grouped into). The role of prior knowledge of the tone types in learning the inversion symmetry was tested on people: When an arbitrary classification of tones was used (i.e. in the absence of prior knowledge of categories), participants did not implicitly learn the inversion symmetry (unlike when they did have prior knowledge of the tone types). These results indicate the importance of prior knowledge in implicit learning of symmetrical structures. We further contrasted the learning of inversion symmetry and retrograde symmetry and showed that inversion was learnt more easily than retrograde by the SRN, matching our previous findings with people, thus showing that the type of memory buffer used in the SRN is suitable for modeling the implicit learning of symmetry in people.

Disentangling synchrony from serial dependency in complex climate networks: Comparing Event Synchronization and Event Coincidence Analysis

<p>The quantification of synchronization phenomena of extreme events has recently aroused a great deal of interest in various disciplines. Climatological studies therefore commonly draw on spatially embedded climate networks in conjunction with nonlinear time series analysis. Among the multitude of similarity measures available to construct climate networks, Event Synchronization and Event Coincidence Analysis (ECA) stand out as two conceptually and computationally simple nonlinear methods. While ES defines synchrony in a data adaptive local way that does not distinguish between different time scales, ECA requires the selection of a specific time scale for synchrony detection.</p><p>Herein, we provide evidence that, due to its parameter-free structure, ES has structural difficulties to disentangle synchrony from serial dependency, whereas ECA is less prone to such biases. We use coupled autoregressive processes to numerically study the sensitivity of results from both methods to changes of coupling and autoregressive parameters. This reveals that ES has difficulties to detect synchronies if events tend to occur temporally clustered, which can be expected from climate time series with extreme events exceeding certain percentiles.</p><p>These conceptual concerns are not only reproducible in numerical simulations, but also have implications for real world data. We construct a climate network from satellite-based precipitation data of the Tropical Rainfall Measuring Mission (TRMM) for the Indian Summer Monsoon, thereby reproducing results of previously published studies. We demonstrate that there is an undesirable link between the fraction of events on subsequent days and the degree density at each grid point of the climate network. This indicates that the explanatory power of ES climate networks might be hampered since trivial local properties of the underlying time series significantly predetermine the final network structure, which holds especially true for areas that had previously been reported as important for governing monsoon dynamics at large spatial scales. In contrast, ECA does not appear to be as vulnerable to these biases and additionally allows to trace the spatiotemporal propagation of synchrony in climate networks.</p><p>Our analysis rests on corrected versions of both methods that alleviate different normalization problems of the original definitions, which is especially important for short time series. Our finding suggest that careful event detection and diligent preprocessing is recommended when applying ES, while this is less crucial for ECA. Results obtained from ES climate networks therefore need to be interpreted with caution.</p>