Publications

Abstract

Do individuals differ in how efficiently they process non-native sounds? To what extent do these differences relate to individual variability in sound-learning aptitude? We addressed these questions by assessing the sound-learning abilities of Dutch native speakers as they were trained on non-native tone contrasts. We used fMRI repetition suppression to the non-native tones to measure participants' neuronal processing efficiency before and after training. Although all participants improved in tone identification with training, there was large individual variability in learning performance. A repetition suppression effect to tone was found in the bilateral inferior frontal gyri (IFGs) before training. No whole-brain effect was found after training; a region-of-interest analysis, however, showed that, after training, repetition suppression to tone in the left IFG correlated positively with learning. That is, individuals who were better in learning the non-native tones showed larger repetition suppression in this area. Crucially, this was true even before training. These findings add to existing evidence that the left IFG plays an important role in sound learning and indicate that individual differences in learning aptitude stem from differences in the neuronal efficiency with which non-native sounds are processed.

Abstract

This commentary on Gow and Olson [2015. Sentential influences on acoustic-phonetic processing: A Granger causality analysis of multimodal imaging data. Language, Cognition and Neuroscience. doi:10.1080/23273798.2015.1029498] questions in three ways their conclusion that speech perception is based on interactive processing. First, it is not clear that the data presented by Gow and Olson reflect normal speech recognition. Second, Gow and Olson's conclusion depends on still-debated assumptions about the functions performed by specific brain regions. Third, the results are compatible with feedforward models of speech perception and appear inconsistent with models in which there are online interactions about phonological content. We suggest that progress in the neuroscience of speech perception requires the generation of testable hypotheses about the function(s) performed by inter-regional connections

Abstract

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.

Abstract

Several models of spoken word recognition postulate that recognition is achieved via a process of competition between lexical hypotheses. Competition not only provides a mechanism for isolated word recognition, it also assists in continuous speech recognition, since it offers a means of segmenting continuous input into individual words. We present statistics on the pattern of occurrence of words embedded in the polysyllabic words of the English vocabulary, showing that an overwhelming majority (84%) of polysyllables have shorter words embedded within them. Positional analyses show that these embeddings are most common at the onsets of the longer word. Although both phonological and syntactic constraints could rule out some embedded words, they do not remove the problem. Lexical competition provides a means of dealing with lexical embedding. It is also supported by a growing body of experimental evidence. We present results which indicate that competition operates both between word candidates that begin at the same point in the input and candidates that begin at different points (McQueen, Norris, & Cutler, 1994, Noms, McQueen, & Cutler, in press). We conclude that lexical competition is an essential component in models of continuous speech recognition.

Abstract

Spoken utterances contain few reliable cues to word boundaries, but listeners nonetheless experience little difficulty identifying words in continuous speech. The authors present data and simulations that suggest that this ability is best accounted for by a model of spoken-word recognition combining competition between alternative lexical candidates and sensitivity to prosodic structure. In a word-spotting experiment, stress pattern effects emerged most clearly when there were many competing lexical candidates for part of the input. Thus, competition between simultaneously active word candidates can modulate the size of prosodic effects, which suggests that spoken-word recognition must be sensitive both to prosodic structure and to the effects of competition. A version of the Shortlist model ( D. G. Norris, 1994b) incorporating the Metrical Segmentation Strategy ( A. Cutler & D. Norris, 1988) accurately simulates the results using a lexicon of more than 25,000 words.