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Due to the increasingly heterogeneous trajectories of aging, gerontology requires theoretical models and empirical methods that can meaningfully, reliably, and precisely describe, explain, and predict causes and effects within the aging process, considering particular contexts and situations. Human behavior occurs in contexts; nevertheless, situational changes are often neglected in context-based behavior research. This article follows the tradition of environmental gerontology research based on Lawton’s Person-Environment-Interaction model (P-E model) and the theoretical developments of recent years. The authors discuss that, despite an explicit time component, current P-E models could be strengthened by focusing on detecting P-E interactions in various everyday situations. Enhancing Lawton’s original formula via a situationally based component not only changes the theoretical perspectives on the interplay between person and environment but also demands new data collection approaches in empirical environmental research. Those approaches are discussed through the example of collecting mobile data with smartphones. Future research should include the situational dimension to investigate the complex nature of person environment interactions.
To a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading—visual, orthographic, phonological, and/or lexical-semantic—contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in “explaining away” predictable stimulus features, but rather in a “sharpening” of brain responses involved in word processing.