No abstract

The concept of auditory-motor interaction pervades speech science research, yet the cortical systems supporting this interface have not been elucidated. Drawing on experimental designs used in recent work in sensory-motor integration in the cortical visual system, we used fMRI in an effort to identify human auditory regions with both sensory and motor response properties, analogous to single-unit responses in known visuomotor integration areas. The sensory phase of the task involved listening to speech (nonsense sentences) or music (novel piano melodies); the "motor" phase of the task involved covert rehearsal/humming of the auditory stimuli. A small set of areas in the superior temporal and temporal-parietal cortex responded both during the listening phase and the rehearsal/humming phase. A left lateralized region in the posterior Sylvian fissure at the parietal-temporal boundary, area Spt, showed particularly robust responses to both phases of the task. Frontal areas also showed combined auditory + rehearsal responsivity consistent with the claim that the posterior activations are part of a larger auditory-motor integration circuit. We hypothesize that this circuit plays an important role in speech development as part of the network that enables acoustic-phonetic input to guide the acquisition of language-specific articulatory-phonetic gestures; this circuit may play a role in analogous musical abilities. In the adult, this system continues to support aspects of speech production, and, we suggest, supports verbal working memory.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink‐related brain activity.

Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced. In this study we explore the possibility of devising a componential model of semantic representation based entirely on such functional divisions in the human brain. We propose a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences. We provide normative data on the salience of each attribute for a large set of English nouns, verbs, and adjectives, and show how these attribute vectors distinguish a priori conceptual categories and capture semantic similarity. Robust quantitative differences between concrete object categories were observed across a large number of attribute dimensions. A within- versus between-category similarity metric showed much greater separation between categories than representations derived from distributional (latent semantic) analysis of text. Cluster analyses were used to explore the similarity structure in the data independent of a priori labels, revealing several novel category distinctions. We discuss how such a representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and conceptual combination. In contrast to componential models based on verbal features, the proposed representation systematically relates semantic content to large-scale brain networks and biologically plausible accounts of concept acquisition.