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Cognitive neuroscience suffers from a unique and pervasive problem of generalizability. Since neural findings are often interpreted in the context of a specific manipulation during a carefully controlled task, it is hard to transfer knowledge from one task to another. In this report we address problems of generalizability with two methodological advancements. First, we aimed to transcend status quo experimental procedures with a continuous, engaging task environment. To this end, we created a novel 8-bit style continuous space shooter video game that elicits a multitude of goal-oriented events, such as crashing into a wall or blowing up an enemy with a missile. Second, we aimed to objectively define the psychological significance of these events. To achieve this aim, we used pattern classification of EEG data to derive predictive weights from carefully controlled pre-game exemplar events (oddball target detection and gambling wins and losses) and transferred those weights to EEG activities during video game events. All major goal-oriented events (crashes into the wall, crashes into an enemy, missile hit on an enemy) had a significant between-task transfer bias towards oddball target weights in the time range of the canonical P3, indicating the presence of similar salience detection processes. Missile hits on an enemy were specifically identified as gambling wins, confirming the hypothesis that this goal-oriented event was appetitive. These findings suggest that it is possible to identify the contribution of canonical neural activities during otherwise ambiguous and uncontrolled task performance.

作者:James F, Cavanagh;Joel, Castellanos

来源:NeuroImage 2016 年 133卷

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| 浏览:37
作者:
James F, Cavanagh;Joel, Castellanos
来源:
NeuroImage 2016 年 133卷
标签:
EEG Pattern classification Reward Salience Transfer learning Video game
Cognitive neuroscience suffers from a unique and pervasive problem of generalizability. Since neural findings are often interpreted in the context of a specific manipulation during a carefully controlled task, it is hard to transfer knowledge from one task to another. In this report we address problems of generalizability with two methodological advancements. First, we aimed to transcend status quo experimental procedures with a continuous, engaging task environment. To this end, we created a novel 8-bit style continuous space shooter video game that elicits a multitude of goal-oriented events, such as crashing into a wall or blowing up an enemy with a missile. Second, we aimed to objectively define the psychological significance of these events. To achieve this aim, we used pattern classification of EEG data to derive predictive weights from carefully controlled pre-game exemplar events (oddball target detection and gambling wins and losses) and transferred those weights to EEG activities during video game events. All major goal-oriented events (crashes into the wall, crashes into an enemy, missile hit on an enemy) had a significant between-task transfer bias towards oddball target weights in the time range of the canonical P3, indicating the presence of similar salience detection processes. Missile hits on an enemy were specifically identified as gambling wins, confirming the hypothesis that this goal-oriented event was appetitive. These findings suggest that it is possible to identify the contribution of canonical neural activities during otherwise ambiguous and uncontrolled task performance.