认知负荷对驾驶员行为和交通安全的影响尚不清楚,需要进一步研究。可靠的认知负荷测量用于研究和,随后,因此,在驾驶员监控系统的开发和实施中寻求。生理测量是感兴趣的,因为它们可以提供驾驶员状态的连续记录。目前,然而,通常不会考虑与它们在这种情况下的使用有关的一些问题,尽管众所周知。首先,认知负荷是一个多维结构,由许多对增加的任务需求的心理反应(认知负荷成分)组成。然而,研究人员将其视为一维。第二,认知负荷不是孤立发生的;相反,它是在特定操作环境中对任务需求的复杂响应的一部分。第三,生理指标通常与一种以上的精神状态相关,限制可以单独从它们中得出的推论。我们建议承认这些问题并使用多种生理指标和自变量研究多种心理反应将大大提高认知负荷的可测量性。为了证明这种方法的潜力,我们使用了来自驾驶模拟器研究的数据,其中包括许多生理指标(心率,心率变异性,呼吸频率,皮肤电导,瞳孔直径,眨眼率,眨眼持续时间,脑电图α功率,和EEGθ功率)进行分析。参与者在驾驶三种不同的交通场景时,以两种难度执行了认知加载n-back任务,每个重复四次。通过考虑与多个自变量相关的多种生理指标的反应模式来评估认知负荷成分和其他重合的心理反应。通过这种方法,认知负荷的结构效度得到提高,这对于准确解释结果很重要。此外,使用多个测量值和自变量使测量值(当联合分析时)更具诊断性-也就是说,能够更好地区分不同的认知负荷成分。这反过来又提高了整体的外部有效性。更详细,诊断,和有效的认知负荷度量,可以更好地理解认知负荷对交通安全的影响,因此可能会缓解。
The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.