High-skilled esports players have unique cortical activity and gaze control, both of which are closely linked to their performance. To enhance key aspects of esports performance—shot time and accuracy—the current study implemented two training methods: a cortical activity-based method (Experiment 1) and a gaze control-based method (Experiment 2). In Experiment 1, a three-channel electroencephalogram headset was used to provide participants with real-time feedback on central lobe activity associated with sustained attention. Twenty-one participants were randomly assigned to the training group (10 participants) and sham group (11 participants). The training group received auditory feedback based on the power of theta and alpha power, while the sham group received auditory feedback not related to central lobe activity. In Experiment 2, an eye-tracker was used to measure the gaze movement and provide feedback. The feedback was provided to help participants maintain their gaze on-center. Twenty-one participants were randomly assigned to the training group (11 participants) and the sham group (10 participants). The training group received warning sounds when their gaze deviated from the off-center, promoting centralized gaze control, whereas the sham group received no such feedback. Participants‘ age and gaming experience were controlled to minimize confounding factors. The results demonstrated a significant decrease in shot time for the training group in both Experiment 1 (Mdn = -30.3ms) and Experiment 2 (Mdn = -47.2ms), with no significant changes in the sham group (all p > .05). Therefore, biofeedback training methods can reduce shot time, one of the critical performance indicators in esports. • We implement attention training and peripheral vision methods for enhancing the reaction speed and accuracy of esports players. • Attention training significantly reduced shot times (Mdn: -30.3ms) in esports players by improving their attention, and the result was supported by a reduction in central alpha power. • Peripheral vision training significantly reduced shot times (Mdn: -47.2ms) in esports players by enhancing peripheral vision skills, and this finding was supported by a narrower gaze distribution. • This reduction in shot time was achieved without a significant reduction in accuracy.

In contemporary gaming, players increasingly rely on numerical data to guide in-game decisions, interact with others, and enhance performance. Especially with the rise of esports and streaming practices, game “numbers” have become central to player development. This study aims to explore how game metrics affect the gaming experience of different kinds of players, investigating whether and how game data influence their performance and shape their sense of agency. With this aim, we adopted an interpretive qualitative approach and conducted forty semi-structured interviews with casual players, esports players, and streamers, asking participants to recount how they use and interpret game data. We then interpreted the collected material through the lens of the extended mind theory and analyzed it using thematic analysis. Study findings reveal that different types of players vary in how they track, understand, and trust game metrics, and that such metrics may extend their cognitive processes. Moreover, the findings show that a player's level of game knowledge influences how players process game data and adjust their behavior accordingly. These findings also suggest that an overemphasis on the “objectivity” of game metrics may lead players to rely excessively on external numerical validation, potentially diminishing their performance and sense of agency. By contrast, players who develop an in-depth understanding of game mechanics and refine their game sense retain greater control over their in-game decisions and behavior. In sum, this study contributes to the understanding of self-tracking in gaming and its implications for player agency, cognition, and performance. • We explore how 40 players use game metrics and track their game performance. • We compare casual players', esports players', and streamers' tracking practices. • We show that casual players may rely heavily on game metrics. • We highlight that esports players use their “game sense” to interpret game data. • We point out that streamers prioritize spectatorship over performance.

Large language models (LLMs) offer the potential to simulate human-like responses and behaviors, creating new opportunities for psychological science. In the context of self-regulated learning (SRL), if LLMs can reliably simulate survey responses at scale and speed, they could be used to test intervention scenarios, refine theoretical models, augment sparse datasets, and represent hard-to-reach populations. However, the validity of LLM-generated survey responses remains uncertain, with limited research focused on SRL and existing studies beyond SRL yielding mixed results. Therefore, in this study, we examined LLM-generated responses to the 44-item Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich \& De Groot, 1990), a widely used instrument assessing students' learning strategies and academic motivation. Particularly, we used the LLMs GPT-4o, Claude 3.7 Sonnet, Gemini 2 Flash, LLaMA 3.1-8B, and Mistral Large. We analyzed item distributions, the psychological network of the theoretical SRL dimensions, and psychometric validity based on the latent factor structure. Our results suggest that Gemini 2 Flash was the most promising LLM, showing considerable sampling variability and producing underlying dimensions and theoretical relationships that align with prior theory and empirical findings. At the same time, we observed discrepancies and limitations, underscoring both the potential and current constraints of using LLMs for simulating psychological survey data and applying it in educational contexts.