Studies of human cognition often rely on brief, highly controlled tasks that emphasize group-level effects but poorly capture the rich variability within and between individuals. A suite of minigames built on the novel pixelDOPA platform was designed to overcome these limitations by embedding classic cognitive task paradigms in a 3D virtual interactive environment with continuous behavior logging. Four of the minigames explore constructs that overlap established NIH Toolbox tasks, including processing speed, rule shifting, inhibitory control and working memory. Across a clinical sample of 66 participants collected outside a controlled laboratory setting, large correlations (r = 0.47-0.92) between the pixelDOPA tasks and NIH Toolbox counterparts were found. Process-informed metrics improved both task convergence and data quality. Test-retest analyses revealed high reliability (ICC = 0.71-0.85) for all minigames. Beyond endpoint metrics, movement and gaze trajectories revealed stable, idiosyncratic profiles of gameplay strategy, with unsupervised clustering differentiating participants by their navigational and viewing behaviors. These trajectory-based features showed lower within-person variability than between-person variability, facilitating participant identification across repeated sessions. Game-based tasks can therefore retain the psychometric rigor of standard cognitive assessments while providing new insights into dynamic individual-specific behaviors. By leveraging a highly engaging, fully customizable game engine, comprehensive behavioral tracking can boost the power to detect individual differences without sacrificing group-level inference. This possibility reveals a path toward cognitive measures that are both psychometrically robust and deployable in less-than-ideal settings, while capturing richer behavioral data than traditional paradigms.

翻译：人类认知研究通常依赖于简短、高度受控的任务，这些任务虽能突出群体层面的效应，却难以捕捉个体内部及个体间丰富的变异性。基于新型pixelDOPA平台开发的一系列迷你游戏旨在突破这些局限，它将经典认知任务范式嵌入到具有连续行为记录功能的3D虚拟交互环境中。其中四款迷你游戏探索了与美国国立卫生研究院工具箱任务重叠的认知结构，包括处理速度、规则转换、抑制控制和工作记忆。在非受控实验室环境下收集的66名临床参与者样本中，发现pixelDOPA任务与NIH工具箱对应任务之间存在高度相关性（r = 0.47-0.92）。基于过程信息的指标同时提升了任务收敛性和数据质量。重测分析显示所有迷你游戏均具有高可靠性（ICC = 0.71-0.85）。除终点指标外，运动轨迹和注视轨迹揭示了稳定且具个体特异性的游戏策略特征，无监督聚类能根据参与者的导航和观察行为对其进行区分。这些基于轨迹的特征显示出低于个体间变异性的个体内变异性，有助于在重复测试中进行参与者身份识别。因此，基于游戏的任务既能保持标准认知评估的心理测量严谨性，又能为动态的个体特异性行为提供新见解。通过利用高度沉浸、完全可定制的游戏引擎，全面的行为追踪可在不牺牲群体层面推断能力的前提下，提升检测个体差异的效力。这一可能性为认知测量开辟了新路径：既保持心理测量学的稳健性，又能在非理想环境中部署，同时捕获比传统范式更丰富的行为数据。