Here, we sharpened our own thinking on prediction. This resulted in several conceptual, theoretical, and methodological papers. One achievement is the development of a conceptual model, in which István Winkler and me join two separate research fields, namely auditory scene analysis and auditory deviance/target detection Winkler & Schröger, 2015). Noting that both functions relate incoming information to what is already known about the environment, we argue that object representation and deviance detection can be described as relying on a common generative model of the auditory environment, called the Auditory Event Representation System (AERS). This framework was connected to a computational model of auditory stream segregation, called CHAINS, which has been developed by Sue Denham (UK) and colleagues (Schröger et al., 2014). In CHAINS, the auditory sensory event representation of each incoming sound is considered for being the continuation of likely combinations of the preceding sounds in the sequence, thus providing alternative interpretations of the auditory input. Detecting repeating patterns allows predicting upcoming sound events, thus providing a test and potential support for the corresponding interpretation. Alternative interpretations continuously compete for perceptual dominance. In a further theoretical approach we discussed a common framework for attention and prediction, two areas which were rarely considered together (Schröger et al., 2015). We suggest that “prediction” relates to predictions sent down from predictive models housed in higher levels of the processing hierarchy to lower levels and “attention” refers to gain modulation of the prediction error signal sent up to the higher level. As predictions encode contents and confidence in the sensory data, and as gain can be modulated by the intention of the listener and by the predictability of the input, various possibilities for interactions between attention and prediction were unfolded. From this perspective, the traditional distinction between bottom-up/exogenous and top-down/endogenous driven attention was revisited and the classic concepts of attentional gain and attentional trace were integrated.

Another milestone is the Edition of three Special Issues in peer-review journals, where we invited colleagues from different labs around to world to contribute to this emerging topic:

Schröger, E., Kotz, S. A., & SanMiguel, I. (Eds.). (2015). Special issue: Bridging prediction and attention in current research on perception and action. Brain Research, 1626 (23 contributions; 280 pp). [This volume belongs to the most cited volumes of Brain Research in 2015 (Editor-in-Chief-update Jan 2017; personal information)]

Tavano, A. & Scharinger, M. (Eds.). (2015). Special issue: Prediction in speech and language processing. Cortex, 68 (16 contributions; 182 pp). [Dr. Tavano was a five-years post-doc hired in the Koselleck-grant; Dr. Scharinger was a post-doc in my group on his own DFG-grant “Globale und lokale Aspekte zeitlicher und lexikalischer Prädiktionen für die Sprachverarbeitung”; he joined my group with his project was it is also on prediction; the special issue was compiled while Tavano and Scharinger were in my BioCog group]

Todd, J., Schröger, E., & Winkler, I. (Eds.). (2012). Predictive information processing in the brain: Principles, neural mechanisms and models. International. International Journal of Psychophysiology, 83 (21 contributions; 130 pp). [7 contributions to this special issue are listed as the most cited articles of this journal (van Petten & Luka; Bendixen, SanMiguel, Schröger; Winkler & Czigler; Friston; Kimura; Rohrmeier & Koelsch; Gomot & Wicker; see https://www.journals.elsevier.com/international-journal-of-psychophysiology/most-cited-articles)]

Together with two experts in visual processing (Motohiro Kimura, Japan, and István Czigler, Hungary), we elaborate to which extent automatically established predictive regularity representations are also at work in the visual modality and we outline communalities with related visual phenomena. Finally, a recent methodological paper discouraged the usage of filters for EEG/MEG data when information about onset, peak or offset is of interest. As this is a challenge to EEG/MEG-based mental chronometry, we (Andreas Widmann and Erich Schröger) together with Burkhard Maess (from the Max-Planck-Insitute for Behavioral and Brain Sciences had to outline the conditions under which filtering can be justified. With these methodological contributions, based on filter theory and simulation, we could successfully rebut a potential criticism of an analysis approach essential to our (and others) research.