Sorato Minami
Biographic Data
| ID | 8324374 |
|---|---|
| NAME | Sorato Minami |
| GIVEN NAMES | Sorato |
| FAMILY NAME | Minami |
| SIGNATURE | MINAMI S |
| ORCID | 0000-0002-0611-0994 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Physiological synchrony in elite esports matches driven by competitive motivation
Although physiological synchrony (PS) is well documented in cooperative settings, recent research indicates that it can also arise in competitive interactions. However, the factors that shape PS under competitive conditions remain unclear. In one-on-one esports, two psychological factors appear central—the drive to defeat an opponent and ability to track and understand the evolving game situation—both of which tend to increase with expertise. To …
Task context and player expertise modulate arm EMG linked to win-loss outcomes in esports
Explicit task cues and motivational context shape motor performance, yet the influence in naturalistic competitive settings remains underexplored. Laboratory tasks typically use discrete cues and monetary rewards, while real-world competition is driven by dynamic state feedback and win-loss incentives. Expertise modulates adaptability in these competitive contexts. Nevertheless, whether naturalistic explicit task cues and motivation alter the mus…
Prediction of esports competition outcomes using EEG data from expert players
Considerable efforts have focused on predicting sports event outcomes to enhance spectating, coaching, and betting. However, accurately predicting match results across various competitive scenes is challenging. Many researchers have achieved reasonable prediction accuracy using machine learning (ML) models and static data, such as player statistics up to the last match, to perform binary win/loss classification. However, the inherent uncertainty …
No prominent works on this page.
Prediction of esports competition outcomes using EEG data from expert players
Considerable efforts have focused on predicting sports event outcomes to enhance spectating, coaching, and betting. However, accurately predicting match results across various competitive scenes is challenging. Many researchers have achieved reasonable prediction accuracy using machine learning (ML) models and static data, such as player statistics up to the last match, to perform binary win/loss classification. However, the inherent uncertainty …
Physiological synchrony in elite esports matches driven by competitive motivation
Although physiological synchrony (PS) is well documented in cooperative settings, recent research indicates that it can also arise in competitive interactions. However, the factors that shape PS under competitive conditions remain unclear. In one-on-one esports, two psychological factors appear central—the drive to defeat an opponent and ability to track and understand the evolving game situation—both of which tend to increase with expertise. To …
Task context and player expertise modulate arm EMG linked to win-loss outcomes in esports
Explicit task cues and motivational context shape motor performance, yet the influence in naturalistic competitive settings remains underexplored. Laboratory tasks typically use discrete cues and monetary rewards, while real-world competition is driven by dynamic state feedback and win-loss incentives. Expertise modulates adaptability in these competitive contexts. Nevertheless, whether naturalistic explicit task cues and motivation alter the mus…
Action Observation and Synchronization (2 works) · Neural and Behavioral Psychology Studies (2 works) · Advanced Text Analysis Techniques (1 work) · Artificial Intelligence (1 work) · Cognitive psychology (1 work) · Computer Science (1 work) · Data science (1 work) · Electroencephalography (1 work) · Elite (1 work) · Machine learning (1 work)