The abstractness of concepts is sometimes defined indirectly as lacking concreteness, this view provides little insight into their cognitive or neural basis. Multivariate pattern analytic techniques applied to functional magnetic resonance imaging data were used to characterize the neural representations of 28 individual abstract concepts. A classifier trained on the concepts’ neural signatures reliably decoded their neural representations in an independent subset of data for each participant. There was considerable commonality of the neural representations across participants as indicated by the accurate classification of each participant’s concepts based on the neural signatures obtained in other participants. Group-level factor analysis revealed 3 semantic dimensions underlying the 28 concepts, suggesting a brain-based ontology for this set of abstract concepts. The 3 dimensions corresponded to 1) the degree a concept was Verbally Represented; 2) whether a concept was External (or Internal) to the individual, and 3) whether the concept contained Social Content. Further exploration of the Verbal Representation dimension suggests that the degree a concept is verbally represented can be construed as a point on a continuum between language faculties and perceptual faculties. A predictive model, based on independent behavioral ratings of the 28 concepts along the 3 factor dimensions, provided converging evidence for the interpretations.

Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was processed as an error in vocalization, altered connections between right STG and left STG. Other regions that revealed differences in connectivity during error detection and correction included bilateral inferior frontal gyrus, and the primary and pre motor cortices. Results indicated that STG plays a critical role in voice control, specifically, during error detection and correction. Additionally, pitch perturbation elicits changes in the voice network that suggest the right hemisphere is critical to pitch modulation.

The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions. When tested on a held-out video, the classifiers were able to reliably identify the thematic role of an object from its associated fMRI activation pattern. Moreover, when trained on one subset of the study participants, classifiers reliably identified the thematic roles in the data of a left-out participant (mean accuracy = .66), indicating that the neural representations of thematic roles were common across individuals.ARTICLE HISTORY

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Speech samples and 16 PF scores were obtained from 104 male college students. The speech samples were rated for loudness and tempo and four voice quality profile types were identified: loud-fast; loud-slow; soft-fast; soft-slow. Analysis of variance indicated a significant difference between the four groups on the 16 PF. On the basis of this study and a previous study using the MMPI, actuarial descriptions of the specific personality traits associated with each voice quality profile type are presented.