Department of Neuroscience, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy.
Institute of Neuroscience, National Research Council, Pisa, Italy.
J Vis. 2022 Feb 1;22(2):3. doi: 10.1167/jov.22.2.3.
Continuous tracking is a newly developed technique that allows fast and efficient data acquisition by asking participants to "track" a stimulus varying in some property (usually position in space). Tracking is a promising paradigm for the investigation of dynamic features of perception and could be particularly well suited for testing ecologically relevant situations difficult to study with classical psychophysical paradigms. The high rate of data collection may be useful in studies on clinical populations and children, who are unable to undergo long testing sessions. In this study, we designed tracking experiments with two novel stimulus features, numerosity and size, proving the feasibility of the technique outside standard object tracking. We went on to develop an ideal observer model that characterizes the results in terms of efficiency of conversion of stimulus strength into responses, and identification of early and late noise sources. Our ideal observer closely modeled results from human participants, providing a generalized framework for the interpretation of tracking data. The proposed model allows to use the tracking paradigm in various perceptual domains, and to study the divergence of human participants from ideal behavior.
连续追踪是一种新开发的技术，通过要求参与者“追踪”在某些属性（通常是空间中的位置）上变化的刺激，实现快速高效的数据采集。追踪是一种很有前途的范式，可以研究感知的动态特征，特别适合测试经典心理物理范式难以研究的具有生态相关性的情况。高数据采集率可能对临床人群和儿童的研究有用，他们无法进行长时间的测试。在这项研究中，我们设计了具有两个新刺激特征（数量和大小）的追踪实验，证明了该技术在标准物体追踪之外的可行性。我们接着开发了一个理想观察者模型，根据刺激强度转换为反应的效率以及早期和晚期噪声源的识别来描述结果。我们的理想观察者非常接近人类参与者的结果，为解释追踪数据提供了一个通用框架。所提出的模型允许在各种感知领域使用追踪范式，并研究人类参与者与理想行为的偏差。
