视觉模式完成方面的个体差异可预测对降级语音的适应性。

IF 2.1 2区 心理学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY Brain and Language Pub Date : 2024-08-01 DOI:10.1016/j.bandl.2024.105449
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引用次数: 0

摘要

识别声音退化的语音依赖于预测处理,即通过模式识别过程将不完整的听觉线索映射到存储的语言表征。虽然听者识别降级语音的能力各不相同,但当出现书面转录时,听者的表现会有所改善,因为书面转录可以将部分感官模式与预先存在的表征相结合。在将预测处理描述为模式完成的基础上,我们研究了领域通用模式识别与降级语音学习中个体差异之间的关系。受试者完成了一项视觉模式识别任务,以测量个体水平上的模式完成倾向。此外,我们还训练参与者识别带有书面转录内容的噪声编码语音,并使用基于检索的转录任务对训练前后的语音识别能力进行测试。经过训练后,听者识别语音的能力明显提高,而视觉任务中的模式完成度预示着对新项目的识别能力的提高。结果表明,模式完成是一种领域通用的学习机制,可促进在具有挑战性的语境中适应语音。
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Individual differences in visual pattern completion predict adaptation to degraded speech

Recognizing acoustically degraded speech relies on predictive processing whereby incomplete auditory cues are mapped to stored linguistic representations via pattern recognition processes. While listeners vary in their ability to recognize degraded speech, performance improves when a written transcription is presented, allowing completion of the partial sensory pattern to preexisting representations. Building on work characterizing predictive processing as pattern completion, we examined the relationship between domain-general pattern recognition and individual variation in degraded speech learning. Participants completed a visual pattern recognition task to measure individual-level tendency towards pattern completion. Participants were also trained to recognize noise-vocoded speech with written transcriptions and tested on speech recognition pre- and post-training using a retrieval-based transcription task. Listeners significantly improved in recognizing speech after training, and pattern completion on the visual task predicted improvement for novel items. The results implicate pattern completion as a domain-general learning mechanism that can facilitate speech adaptation in challenging contexts.

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来源期刊
Brain and Language
Brain and Language 医学-神经科学
CiteScore
4.50
自引率
8.00%
发文量
82
审稿时长
20.5 weeks
期刊介绍: An interdisciplinary journal, Brain and Language publishes articles that elucidate the complex relationships among language, brain, and behavior. The journal covers the large variety of modern techniques in cognitive neuroscience, including functional and structural brain imaging, electrophysiology, cellular and molecular neurobiology, genetics, lesion-based approaches, and computational modeling. All articles must relate to human language and be relevant to the understanding of its neurobiological and neurocognitive bases. Published articles in the journal are expected to have significant theoretical novelty and/or practical implications, and use perspectives and methods from psychology, linguistics, and neuroscience along with brain data and brain measures.
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