CROSSMODAL INTEGRATION IN THE IDENTIFICATION OF CONSONANT SEGMENTS
CROSSMODAL INTEGRATION IN THE IDENTIFICATION OF CONSONANT SEGMENTS
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DOI：
10.1080/14640749108400991
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发表时间：
1991-08-01
期刊：
影响因子：
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通讯作者：
BRAIDA, LD
中科院分区：
文献类型：
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作者：
BRAIDA, LD
虽然言语阅读可以通过听觉或触觉的补充来促进，但整合各种形式的线索的过程还没有得到很好的理解。本文介绍了两种“最佳处理”模型的类型的整合，可用于speechreading辅音段，并比较他们的预测与模糊逻辑模型的感知（FLMP，Massaro，1987年）。在“预标记”整合中，在分配响应标签之前，跨模态组合连续的感觉数据。在“后标记”整合中，将在单峰条件下做出的响应组合，并且从该对中导出联合响应。为了描述预标记集成，混淆矩阵的特点是由一个多维的决策模型，允许性能被描述的受试者的敏感性和偏见，在使用连续值的线索。线索空间的特征在于刺激和反应中心的位置。一对刺激中心之间的距离决定了在给定的实验中两个刺激可以区分得有多好。在多模态情况下，假设提示空间是与刺激模式对应的提示空间的乘积空间。在五个现代辅音识别研究中，多模态准确性的测量与前标记整合模型的预测比FLMP或后标记模型更一致。
Although speechreading can be facilitated by auditory or tactile supplements, the process that integrates cues across modalities is not well understood. This paper describes two "optimal processing" models for the types of integration that can be used in speechreading consonant segments and compares their predictions with those of the Fuzzy Logical Model of Perception (FLMP, Massaro, 1987). In "pre-labelling" integration, continuous sensory data is combined across modalities before response labels are assigned. In "post-labelling" integration, the responses that would be made under unimodal conditions are combined, and a joint response is derived from the pair. To describe pre-labelling integration, confusion matrices are characterized by a multidimensional decision model that allows performance to be described by a subject's sensitivity and bias in using continuous-valued cues. The cue space is characterized by the locations of stimulus and response centres. The distance between a pair of stimulus centres detemines how well two stimuli can be distinguished in a given experiment. In the multimodal case, the cue space is assumed to be the product space of the cue spaces corresponding to the stimulation modes. Measurements of multimodal accuracy in five modern studies of consonant identification are more consistent with the predictions of the pre-labelling integration model than the FLMP or the post-labelling model.