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Lexical decision (LD) - judging whether a sequence of letters constitutes a word - has been widely investigated. In a typical lexical decision task (LDT), participants are asked to respond whether a sequence of letters is an actual word or a nonword. Although behavioral differences between types of words/nonwords have been robustly detected in LDT, there is an ongoing discussion about the exact cognitive processes that underlie the word identification process in this task. To obtain data-driven evidence on the underlying processes, we recorded electroencephalographic (EEG) data and applied a novel machine-learning method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA). In the current study, participants performed an LDT in which we varied the frequency of words (high, low frequency) and "wordlikeness" of non-words (pseudowords, random non-words). The results revealed that models with six processing stages accounted best for the data in all conditions. While most stages were shared, Stage 5 differed between conditions. Together, these results indicate that the differences in word frequency and lexicality effects are driven by a single cognitive processing stage. Based on its latency and topology, we interpret this stage as a Decision process during which participants discriminate between words and nonwords using activated lexical information.

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PURPOSE: The aim of this study was to compare the effects of moderate intensity, low volume (MILV) vs. low intensity, high volume (LIHV) strength training on sport-specific performance, measures of muscular fitness, and skeletal muscle mass in young kayakers and canoeists. METHODS: Semi-elite young kayakers and canoeists (N = 40, 13 ± 0.8 years, 11 girls) performed either MILV (70-80% 1-RM, 6-12 repetitions per set) or LIHV (30-40% 1-RM, 60-120 repetitions per set) strength training for one season. Linear mixed-effects models were used to compare effects of training condition on changes over time in 250 and 2,000 m time trials, handgrip strength, underhand shot throw, average bench pull power over 2 min, and skeletal muscle mass. Both between- and within-subject designs were used for analysis. An alpha of 0.05 was used to determine statistical significance. RESULTS: Between- and within-subject analyses showed that monthly changes were greater in LIHV vs. MILV for the 2,000 m time trial (between: 9.16 s, SE = 2.70, p < 0.01; within: 2,000 m: 13.90 s, SE = 5.02, p = 0.01) and bench pull average power (between: 0.021 W⋅kg-1, SE = 0.008, p = 0.02; within: 0.010 W⋅kg-1, SE = 0.009, p > 0.05). Training conditions did not affect other outcomes. CONCLUSION: Young sprint kayakers and canoeists benefit from LIHV more than MILV strength training in terms of 2,000 m performance and muscular endurance (i.e., 2 min bench pull power).

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Dating back to the 19th century, the discovery of processing stages has been of great interest to researchers in cognitive science. The goal of this paper is to demonstrate the validity of a recently developed method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering stages directly from EEG data, in contrast to classical reaction-time-based methods. To test the validity of stages discovered with the HsMM-MVPA method, we applied it to two relatively simple tasks where the interpretation of processing stages is straightforward. In these visual discrimination EEG data experiments, perceptual processing and decision difficulty were manipulated. The HsMM-MVPA revealed that participants progressed through five cognitive processing stages while performing these tasks. The brain activation of one of those stages was dependent on perceptual processing, whereas the brain activation and the duration of two other stages were dependent on decision difficulty. In addition, evidence accumulation models (EAMs) were used to assess to what extent the results of HsMM-MVPA are comparable to standard reaction-time-based methods. Consistent with the HsMM-MVPA results, EAMs showed that nondecision time varied with perceptual difficulty and drift rate varied with decision difficulty. Moreover, nondecision and decision time of the EAMs correlated highly with the first two and last three stages of the HsMM-MVPA, respectively, indicating that the HsMM-MVPA gives a more detailed description of stages discovered with this more classical method. The results demonstrate that cognitive stages can be robustly inferred with the HsMM-MVPA.

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