Roark Casey L, Holt Lori L
Department of Psychology, Carnegie Mellon University, and the Center for the Neural Basis of Cognition, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
Atten Percept Psychophys. 2019 May;81(4):912-926. doi: 10.3758/s13414-019-01688-6.
Human category learning appears to be supported by dual learning systems. Previous research indicates the engagement of distinct neural systems in learning categories that require selective attention to dimensions versus those that require integration across dimensions. This evidence has largely come from studies of learning across perceptually separable visual dimensions, but recent research has applied dual system models to understanding auditory and speech categorization. Since differential engagement of the dual learning systems is closely related to selective attention to input dimensions, it may be important that acoustic dimensions are quite often perceptually integral and difficult to attend to selectively. We investigated this issue across artificial auditory categories defined by center frequency and modulation frequency acoustic dimensions. Learners demonstrated a bias to integrate across the dimensions, rather than to selectively attend, and the bias specifically reflected a positive correlation between the dimensions. Further, we found that the acoustic dimensions did not equivalently contribute to categorization decisions. These results demonstrate the need to reconsider the assumption that the orthogonal input dimensions used in designing an experiment are indeed orthogonal in perceptual space as there are important implications for category learning.
人类的类别学习似乎由双重学习系统支持。先前的研究表明，在学习需要对维度进行选择性注意的类别与需要跨维度整合的类别时，不同的神经系统会参与其中。这一证据主要来自对跨感知可分离视觉维度的学习研究，但最近的研究已将双重系统模型应用于理解听觉和语音分类。由于双重学习系统的不同参与与对输入维度的选择性注意密切相关，因此声学维度通常在感知上是整合的且难以选择性地关注这一点可能很重要。我们通过由中心频率和调制频率声学维度定义的人工听觉类别来研究这个问题。学习者表现出跨维度整合而非选择性关注的偏向，并且这种偏向具体反映了维度之间的正相关。此外，我们发现声学维度对分类决策的贡献并不等同。这些结果表明，需要重新考虑在设计实验时使用的正交输入维度在感知空间中确实是正交的这一假设，因为这对类别学习有重要影响。
