Max Planck Research Group "Auditory Cognition", Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany.
Mem Cognit. 2013 Jul;41(5):752-68. doi: 10.3758/s13421-013-0294-9.
Complex sounds vary along a number of acoustic dimensions. These dimensions may exhibit correlations that are familiar to listeners due to their frequent occurrence in natural sounds-namely, speech. However, the precise mechanisms that enable the integration of these dimensions are not well understood. In this study, we examined the categorization of novel auditory stimuli that differed in the correlations of their acoustic dimensions, using decision bound theory. Decision bound theory assumes that stimuli are categorized on the basis of either a single dimension (rule based) or the combination of more than one dimension (information integration) and provides tools for assessing successful integration across multiple acoustic dimensions. In two experiments, we manipulated the stimulus distributions such that in Experiment 1, optimal categorization could be accomplished by either a rule-based or an information integration strategy, while in Experiment 2, optimal categorization was possible only by using an information integration strategy. In both experiments, the pattern of results demonstrated that unidimensional strategies were strongly preferred. Listeners focused on the acoustic dimension most closely related to pitch, suggesting that pitch-based categorization was given preference over timbre-based categorization. Importantly, in Experiment 2, listeners also relied on a two-dimensional information integration strategy, if there was immediate feedback. Furthermore, this strategy was used more often for distributions defined by a negative spectral correlation between stimulus dimensions, as compared with distributions with a positive correlation. These results suggest that prior experience with such correlations might shape short-term auditory category learning.
复杂的声音在许多声学维度上都有所变化。这些维度可能会表现出与听众熟悉的相关性,因为它们在自然声音中经常出现,即语音。然而,能够整合这些维度的确切机制尚不清楚。在这项研究中,我们使用决策边界理论来研究在声学维度的相关性方面存在差异的新型听觉刺激的分类。决策边界理论假设,刺激是基于单个维度(基于规则)或多个维度的组合(信息整合)进行分类的,并且提供了评估多个声学维度成功整合的工具。在两项实验中,我们操纵了刺激分布,使得在实验 1 中,通过基于规则或信息整合的策略都可以完成最佳分类,而在实验 2 中,只有通过信息整合策略才能进行最佳分类。在这两项实验中,结果模式表明,单维策略具有很强的偏好性。听众专注于与音高最相关的声学维度,这表明基于音高的分类优先于基于音色的分类。重要的是,在实验 2 中,如果有即时反馈,听众还依赖于二维信息整合策略。此外,与具有正相关的分布相比,这种策略在维度之间具有负光谱相关性的分布中使用的频率更高。这些结果表明,先前对这种相关性的经验可能会影响短期听觉类别学习。