Norris Dennis, McQueen James M, Cutler Anne
MRC Cognition and Brain Sciences Unit , Cambridge , UK.
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
Lang Cogn Neurosci. 2016 Jan 2;31(1):4-18. doi: 10.1080/23273798.2015.1081703. Epub 2015 Sep 4.
Speech perception involves prediction, but how is that prediction implemented? In cognitive models prediction has often been taken to imply that there is feedback of activation from lexical to pre-lexical processes as implemented in interactive-activation models (IAMs). We show that simple activation feedback does not actually improve speech recognition. However, other forms of feedback can be beneficial. In particular, feedback can enable the listener to adapt to changing input, and can potentially help the listener to recognise unusual input, or recognise speech in the presence of competing sounds. The common feature of these helpful forms of feedback is that they are all ways of optimising the performance of speech recognition using Bayesian inference. That is, listeners make predictions about speech because speech recognition is optimal in the sense captured in Bayesian models.
语音感知涉及预测，但这种预测是如何实现的呢？在认知模型中，预测通常被认为意味着存在从词汇层面到词汇前处理过程的激活反馈，就像在交互式激活模型（IAMs）中那样。我们表明，简单的激活反馈实际上并不能提高语音识别能力。然而，其他形式的反馈可能是有益的。特别是，反馈可以使听者适应不断变化的输入，并且有可能帮助听者识别异常输入，或在存在竞争声音的情况下识别语音。这些有用的反馈形式的共同特征是，它们都是使用贝叶斯推理来优化语音识别性能的方法。也就是说，听者对语音进行预测是因为语音识别在贝叶斯模型所捕捉的意义上是最优的。
