Interaction between auditory and motor systems in speech perception

Based on the Motor Theory of speech perception, the interaction between the auditory and motor systems plays an essential role in speech perception. Since the Motor Theory was proposed, it has received remarkable attention in the field. However, each of the three hypotheses of the theory still needs further verification. In this review, we focus on how the auditory-motor anatomical and functional associations play a role in speech perception and discuss why previous studies could not reach an agreement and particularly whether the motor system involvement in speech perception is task-load dependent. Finally, we suggest that the function of the auditory-motor link is particularly useful for speech perception under adverse listening conditions and the further revised Motor Theory is a potential solution to the ＂cocktail-party＂ problem.

Role of the hippocampus in contextual modulation of fear extinction

Fear extinction is an important form of emotional learning, and affects neural plasticity. Cue fear extinction is a classical form of inhibitory learning that can be used as an exposure-based treatment for phobia, because the long-term extinction memory produced during cue fear extinction can limit the over-expression of fear. The expression of this inhibitory memory partly depends on the context in which the extinction learning occurs. Studies such as transient inhibition, electrophysiology and brain imaging have proved that the hippocampus - an important structure in the limbic system - facilitates memory retrieval by contextual cues. Mediation of the hippocampus-medial prefrontal lobe circuit may be the neurobiological basis of this process. This article has reviewed the role of the hippocampus in the learning and retrieval of fear extinction. Contextual modulation of fear extinction may rely on a neural network consisting of the hippocampus, the medial prefrontal cortex and the amygdala.

Modelling envelope and temporal fine structure components of frequency-following responses in rat inferior colliculus

In studies of auditory perception, a dichotomy between envelope and temporal fine structure(TFS) has been emphasized. It has been shown that frequency-following responses(FFRs) in the rat inferior colliculus can be divided into the envelope component(FFREnv)and the temporal fine structure component(FFRTFS). However, the existing FFR models cannot successfully separate FFREnv and FFRTFS. This study was to develop a new FFR model to effectively distinguish FFREnv from FFRTFS by both combining the advantages of the two existing FFR models and simultaneously adding cellular properties of inferior colliculus neurons. To evaluate the validity of the present model, correlations between simulated FFRs and experimental data from the rat inferior colliculus were calculated. Different model parameters were tested, FFRs were calculated, and the parameters with highest prediction were chosen to establish an ideal FFR model. The results indicate that the new FFR model can provide reliable predictions for experimentally obtained FFREnv and FFRTFS.

A computational model for assessment of speech intelligibility in informational masking

The existing auditory computational mod- els for evaluating speech intelligibility can only account for energetic masking, and the effect of informational masking is rarely described in these models. This study was aimed to make a computational model considering the mechanism of informational masking. Several psy- choacoustic experiments were conducted to test the ef- fect of informational masking on speech intelligibility by manipulating the number of masking talker, speech rate, and the similarity of F0 contour between target and masker. The results showed that the speech recep- tion threshold for the target increased as the F0 contours of the masker became more similar to that of the tar- get, suggesting that the difficulty in segregating the tar- get harmonics from the masker harmonics may underlie the informational masking effect. Based on these stud- ies, a new auditory computational model was made by inducing the auditory function of harmonic extraction to the traditional model of speech intelligibility index （SII）, named as harmonic extraction （HF） model. The predictions of the HF model are highly consistent with the experimental results.

Interactive effects of morphine and dopaminergic compounds on spatial working memory in rhesus monkeys

Opiates and dopamine （DA） play key roles in learning and memory in humans and animals. Although interactions between these neurotransmitters have been found, their functional roles remain to be fully elucidated, and their dysfunction may contribute to human diseases and addiction. Here we investigated the interactions of morphine and dopaminergic neurotransmitter systems with respect to learning and memory in rhesus monkeys by using the Wisconsin General Test Apparatus （WGTA） delayed-response task. Morphine and DA agonists （SKF-38393, apomorphine and bromocriptine） or DA antagonists （SKF-83566, haloperidol and sulpiride） were co-administered to the monkeys 30 min prior to the task. We found that dose-patterned co-administration of morphine with D1 or D2 antagonists or agonists reversed the impaired spatial working memory induced by morphine or the compounds alone. For example, morphine at 0.01 mg/kg impaired spatial working memory, while morphine （0.01 mg/kg） and apomorphine （0.01 or 0.06 mg/kg） co-treatment ameliorated this effect. Our findings suggest that the interactions between morphine and dopaminergic compounds influence spatial working memory in rhesus monkeys. A better understanding of these interactive relationships may provide insights into human addiction.

Discriminative training of GMM-HMM acoustic model by RPCL learning

This paper presents a new discriminative approach for training Gaussian mixture models(GMMs)of hidden Markov models(HMMs)based acoustic model in a large vocabulary continuous speech recognition(LVCSR)system.This approach is featured by embedding a rival penalized competitive learning(RPCL)mechanism on the level of hidden Markov states.For every input,the correct identity state,called winner and obtained by the Viterbi force alignment,is enhanced to describe this input while its most competitive rival is penalized by de-learning,which makes GMMs-based states become more discriminative.Without the extensive computing burden required by typical discriminative learning methods for one-pass recognition of the training set,the new approach saves computing costs considerably.Experiments show that the proposed method has a good convergence with better performances than the classical maximum likelihood estimation(MLE)based method.Comparing with two conventional discriminative methods,the proposed method demonstrates improved generalization ability,especially when the test set is not well matched with the training set.