声化技术用于时间序列数据探索的有效性评估
Evaluation of the effectiveness of sonification for time series data exploration
AI总结：
本研究评估声化技术在天文学时间序列数据探索中的有效性，通过对比天文学家与非天文学家在视觉和听觉映射下的类凌星特征检测表现，发现视觉优于听觉但声化仍具潜力，并揭示了不同表示方式对检测准确性和反应偏差的影响。
摘要：
天文学是一门主要依赖视觉数据的学科。然而，人们正在探索替代性的数据表示技术，特别是“声化”，即将数据表示为声音。尽管天文学界对在研究和教育背景下使用声化的兴趣日益浓厚，但其全部潜力仍有待探索。本研究测量了天文学家和非天文学家在检测时间序列数据（即光变曲线）中类凌星特征时的表现，这些数据以视觉或听觉方式呈现，并采用了不同的数据到声音映射。我们还评估了参与者在不同条件下表现出的偏差。我们模拟了160条具有不同信噪比（SNR）的光变曲线数据。我们将它们表示为视觉图或听觉流，并使用不同的声音参数来表示亮度：音高、持续时间，或冗余的持续时间与音高。我们要求参与者在包含等量有和没有类凌星特征刺激的会话中，识别这四种条件下是否存在类凌星特征。在听觉刺激下，参与者检测凌星的表现高于偶然水平。然而，与听觉刺激相比，视觉刺激导致了整体更好的表现，并且天文学家的表现优于非天文学家。可视化导致了保守的反应偏差（不愿回答“是的，有凌星”），而声化导致了更自由的反应（倾向于回答“是的，有凌星”）。总体而言，本研究有助于理解时间序列数据的不同表示（视觉或听觉）和声音映射（音高、持续时间、持续时间与音高）如何影响检测准确性和偏差。
英文摘要：
Astronomy is a discipline primarily reliant on visual data. However, alternative data representation techniques are being explored, in particular ''sonification'', namely, the representation of data into sound. While there is increasing interest in the astronomical community in using sonification in research and educational contexts, its full potential is still to be explored. This study measured the performance of astronomers and non-astronomers to detect a transit-like feature in time series data (i.e., light curves), that were represented visually or auditorily, adopting different data-to-sound mappings. We also assessed the bias that participants exhibited in the different conditions. We simulated the data of 160 light curves with different signal-to-noise ratios (SNR). We represented them as visual plots or auditory streams with different sound parameters to represent brightness: pitch, duration, or the redundant duration & pitch. We asked the participants to identify the presence of transit-like features in these four conditions in a session that included an equal number of stimuli with and without transit-like features. With auditory stimuli, participants detected transits with performances above the chance level. However, visual stimuli led to overall better performances compared to auditory stimuli and astronomers outperformed non-astronomers. Visualisations led to a conservative response bias (reluctance to answer ''yes, there is a transit''), whereas sonifications led to more liberal responses (proneness to respond ''yes, there is a transit''). Overall, this study contributes to understanding how different representations (visual or auditory) and sound mappings (pitch, duration, duration & pitch) of time series data affect detection accuracy and biases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
