The electrophysiological response to words during the 'N400' time window (~300-500 ms postonset) is affected by the context in which the word is presented, but whether this effect reflects the impact of context on access of the stored lexical information itself or, alternatively, post-access integration processes is still an open question with substantive theoretical consequences. One challenge for integration accounts is that contexts that seem to require different levels of integration for incoming words (i.e., sentence frames versus prime words) have similar effects on the N400 component measured in ERP. In this study we compare the effects of these different context types directly, in a within-subject design using MEG, which provides a better opportunity for identifying topographical differences between electrophysiological components, due to the minimal spatial distortion of the MEG signal. We find a qualitatively similar contextual effect for both sentence frame and prime word contexts, although the effect is smaller in magnitude for shorter word prime contexts. Additionally, we observe no difference in response amplitude between sentence endings that are explicitly incongruent and target words that are simply part of an unrelated pair. These results suggest that the N400 effect does not reflect semantic integration difficulty. Rather, the data are consistent with an account in which N400 reduction reflects facilitated access of lexical information. Keywords N400; MEG; semantic priming; semantic anomaly; prediction The role of contextual information in the access of stored linguistic representations has been a major concern of language processing research over the past several decades. Results from many behavioral studies showing contextual effects on phonemic and/or lexical tasks

Impulsive-source active sonar systems are often plagued by false alarm echoes resulting from the presence of naturally occurring clutter objects in the environment. Sonar performance could be improved by a technique for discriminating between echoes from true targets and echoes from clutter. Motivated by anecdotal evidence that target echoes sound very different than clutter echoes when auditioned by a human operator, this paper describes the implementation of an automatic classifier for impulsive-source active sonar echoes that is based on perceptual signal features that have been previously identified in the musical acoustics literature as underlying timbre. Perceptual signal features found in this paper to be particularly useful to the problem of active sonar classification include: the centroid and peak value of the perceptual loudness function, as well as several features based on subband attack and decay times. This paper uses subsets of these perceptual signal features to train and test an automatic classifier capable of discriminating between target and clutter echoes with an equal error rate of roughly 10%; the area under the receiver operating characteristic curve corresponding to this classifier is found to be 0.975.

Currently available mathematical models of acoustic scattering from the ocean bottom generally fail to predict backscatter levels of sufficient magnitude for two limiting cases: as the bottom becomes increasingly smooth and as the grazing angle becomes small. In this paper a mathematical model of acoustic backscattering that attempts to address these shortcomings for the case of a sediment bottom is derived. In the model, scattering is caused by fluctuations in sediment porosity. The model allows for penetration of the incident wave into the bottom at subcritical grazing, and retransmission of scattered spherical waves through the (planar) interface. The frequency and grazing angle dependence of the acoustic backscatter are determined primarily by the correlation function of the porosity fluctuations, while the magnitude of the backscatter is controlled by the mean-square value of these fluctuations. Numerical results obtained from the model are tested against backscatter data available in the literature for several sediment bottoms over a wide range of frequencies (30–500 kHz). Agreement between the model and literature data is good.

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

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