Mindspeller 神经画像：任务表现、脑电图与联想证据如何支持基于 O*NET 的角色引导
Mindspeller Neuroprofiling. How task performance, EEG, and association evidence support O*NET-based role guidance
AI总结：
Mindspeller 利用任务表现与脑电图数据生成神经画像，结合自我报告与联想证据，支持基于 O*NET 的角色引导讨论，而非招聘决策。
AI中文摘要：
Mindspeller 从三个来源生成神经画像：理性自我报告、基于联想的语义定位，以及在认知任务期间记录的表现与脑电图。任务与脑电图流是唯一用于创建职业证据的来源。只有在参与者的任务表现支持预期构念，且相应的脑电图数据通过所需的质量与证据检查后，结果才能进入角色匹配环节。当前试点使用四个脑电图电极、十二个评分任务、23 项 O*NET 能力，以及一个包含 376 个职业的内部数据库。自我报告和联想证据有助于解释动机、偏好和一致性，但不生成角色。输出旨在支持关于认知适配的讨论。它不是招聘决策、实际工作技能的衡量标准，也不是工作表现的预测。角色置信度目前上限为“中等”，且外部心理测量与工作结果效度尚未建立。
英文摘要：
Mindspeller produces a Neuroprofile from three sources: rational self-report, association-based semantic positioning, and performance recorded during cognitive tasks together with EEG. The task-and-EEG stream is the only source used to create occupational evidence. A result can enter role matching only after the participant's task performance supports the intended construct and the corresponding EEG data pass the required quality and evidence checks. The current pilot uses four EEG electrodes, twelve scored tasks, 23 O*NET abilities, and an internal bank of 376 occupations. Self-report and association evidence help explain motivation, preference, and alignment, but do not generate roles. The output is intended to support discussion about cognitive fit. It is not a hiring decision, a measure of practical job skill, or a prediction of job performance. Role confidence is currently capped at Moderate, and external psychometric and job-outcome validity have not yet been established.
