斯特恩伯格任务中工作记忆负荷的脑电图、瞳孔放大和其他生理指标

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Multimodal Technologies and Interaction Pub Date : 2024-04-19 DOI:10.3390/mti8040034
M. Ahmadi, Samantha W. Michalka, Marzieh Ahmadi Najafabadi, Burkhard C. Wünsche, M. Billinghurst
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引用次数: 0

摘要

最近的证据表明,生理线索,如瞳孔放大(PD)、心率(HR)、皮肤电导率(SC)和脑电图(EEG),可以显示使用者在执行任务时的认知负荷(CL)。本文旨在研究在斯腾伯格记忆任务中,随着维持和探究阶段难度的增加,对认知负荷(CL)的生理(多模态)测量。为此,我们设计了一个斯腾伯格记忆测试,该测试有四个难度级别,由需要记忆的单词中的字母数量决定。我们的行为表现结果表明,该任务的CL与非语义单词中的字母数量有关,这证实了该任务是衡量CL的合适指标(任务难度随着单词中字母数量的增加而增加)。我们有兴趣研究在 Sternberg 记忆任务的维持和探究阶段,多模态生理测量作为四种 CL 水平的相关指标是否合适。我们的动机是:(1) 设计并创建四级任务难度,使CL逐渐增加,而不仅仅是高CL和低CL;(2) 使用斯特恩伯格测试作为我们的测试平台;(3) 探索维持阶段和探究阶段的CL测量;(4) 探索生理线索(PD、HR、SC、EEG)与这两个阶段的CL的相关性。通过系统测试,我们发现在维持阶段和探测阶段,平均基线校正 PD 与 CL 之间都存在显著的正线性关系。我们还观察到,在维持阶段和探究阶段,随着单词中字母数量的增加,平均基线校正 SC 也有显著增加。然而,在维持阶段和探究阶段,HR 分析均未显示出与 CL 的增加有任何相关性。我们还进行了额外的分析,以研究这些生理信号在高 CL 负荷(七个字母的单词)和低 CL 负荷(四个字母的单词)时的相关性。我们对维持阶段的脑电图分析发现,大脑颞中叶、额叶和枕叶区的α波段上部的功率谱密度(PSD)与CL之间存在显著的正线性关系,额叶和枕叶区的α波段下部的功率谱密度(PSD)与CL之间也存在显著的正线性关系。然而,我们对探测阶段的脑电图分析并未显示任何区域的 PSD 与 CL 之间存在任何线性关系。这些结果表明,PD、SC 和 EEG 可以作为测量 Sternberg 记忆任务中认知负荷的合适指标。我们将讨论这一点、研究的局限性以及未来的工作方向。
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EEG, Pupil Dilations, and Other Physiological Measures of Working Memory Load in the Sternberg Task
Recent evidence shows that physiological cues, such as pupil dilation (PD), heart rate (HR), skin conductivity (SC), and electroencephalography (EEG), can indicate cognitive load (CL) in users while performing tasks. This paper aims to investigate physiological (multimodal) measurement of CL in a Sternberg memory task as the difficulty level increases in both maintenance and probe phases. For this purpose, we designed a Sternberg memory test with four levels of difficulty determined by the number of letters in the words that need to be remembered. Our behavioral performance results show that the CL of the task is related to the number of letters in non-semantic words, which confirms that this task serves as an appropriate metric of CL (the task difficulty increases as the number of letters in words increases). We were interested in investigating the suitability of multimodal physiological measures as correlates of four CL levels for both the maintenance and probe phases in the Sternberg memory task. Our motivation was to: (1) design and create four levels of task difficulty with a gradual increase in CL rather than just high and low CL, (2) use the Sternberg test as our test bed, (3) explore both the maintenance and probe phases for measurement of CL, and (4) explore the correlation of physiological cues (PD, HR, SC, EEG) with CL in both phases. Testing with the system, we found that for both the maintenance and probe phases, there was a significant positive linear relationship between average baseline corrected PD and CL. We also observed that the average baseline corrected SC showed significant increases as the number of letters in the words increased for both the maintenance and probe phases. However, the HR analysis did not show any correlation with an increase in CL in either of the maintenance or probe phases. An additional analysis was conducted to investigate the correlation of these physiological signals for high (seven-letter words) versus low (four-letter words) CL loads. Our EEG analysis for the maintenance phase found significant positive linear relationships between the power spectral density (PSD) and CL for the upper alpha bands in the centrotemporal, frontal, and occipitoparietal regions of the brain and significant positive linear relationships between the PSD and CL for the lower alpha band in the frontal and occipitoparietal regions. However, our EEG analysis of the probe phase did not show any linear relationship between the PSD and CL in any region. These results suggest that PD, SC, and EEG could be used as suitable metrics for the measurement of cognitive load in Sternberg memory tasks. We discuss this, limitations of the study, and directions for future work.
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来源期刊
Multimodal Technologies and Interaction
Multimodal Technologies and Interaction Computer Science-Computer Science Applications
CiteScore
4.90
自引率
8.00%
发文量
94
审稿时长
4 weeks
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