Multivariate sensitivity to voice during auditory categorization

Lee, Yune-Sang; Peelle, Jonathan E.; Kraemer, David J. M.; Lloyd, Samuel; Granger, Richard

doi:10.1152/jn.00407.2014

Cited by 14 publications

(9 citation statements)

References 55 publications

(58 reference statements)

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“…For each stimulus, we constructed activation vectors by concatenating timepoints that solely segregated signals driven by the stimulus from preceding stimuli. To this end, we relied on canonical hemodynamic function (HRF) such that those times points above the mean intensity of HRF were chosen (Lee et al, 2015). These typically corresponded to second (3 sec) and third TRs (6 sec) after stimulus presentation.…”

Section: Methodsmentioning

confidence: 99%

Neural bases of action abstraction

Quandt

Lee

Chatterjee

2017

Biological Psychology

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There has been recent debate over whether actions are processed primarily by means of motor simulation or cognitive semantics. The current study investigated how abstract action concepts are processed in the brain, independent of the format in which they are presented. Eighteen healthy adult participants viewed different actions (e.g., diving, boxing) in the form of verbs and schematic action pictograms while functional magnetic resonance imaging (fMRI) was collected. We predicted that sensorimotor and semantic brain regions would show similar patterns of neural activity for different instances of the same action (e.g., diving pictogram and the word ‘diving’). A representational similarity analysis revealed posterior temporal and sensorimotor regions where specific action concepts were encoded, independent of the format of presentation. These results reveal the neural instantiations of abstract action concepts, and demonstrate that both sensorimotor and semantic systems are involved in processing actions.

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Section: Methodsmentioning

confidence: 99%

Neural bases of action abstraction

Quandt

Lee

Chatterjee

2017

Biological Psychology

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“…Alternatively, all types of sound sources might be represented via the same neural substrates (e.g., Zatorre et al, 2004), with little or no category-specific operations until later levels of analysis. However, previous work has primarily examined either individual sources within a single category (Bonte et al, 2014;Formisano et al, 2008;Hjortkjaer et al, 2017), or broad category-level differences between large groups of sounds (Lee et al, 2015;Staeren et al, 2009). As a consequence, it remains unclear whether individual auditory objects or events from different categories are represented using the same or different neural substrates.…”

Section: Introductionmentioning

confidence: 99%

“…However, such diverse stimulus sets necessarily involve greater acoustic variability, which can be represented in patterns of activation (Allen et al, 2017;Giordano et al, 2012) that a classifier could exploit. Therefore, when using diverse stimulus sets, steps must be taken to account for these acoustic processes either by controlling the features of the stimuli themselves (Lee et al, 2015;Staeren et al, 2009), or by incorporating the acoustic variability into the analyses (Hjortkjaer et al, 2017;Giordano et al, 2014). Additionally, these studies involving musical timbre were primarily based on univariate activation.…”

Section: Introductionmentioning

confidence: 99%

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Separable neural representations of sound sources: Speaker identity and musical timbre

et al. 2019

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Human listeners can quickly and easily recognize different sound sources (objects and events) in their environment. Understanding how this impressive ability is accomplished can improve signal processing and machine intelligence applications along with assistive listening technologies. However, it is not clear how the brain represents the many sounds that humans can recognize (such as speech and music) at the level of individual sources, categories and acoustic features. To examine the cortical organization of these representations, we used patterns of fMRI responses to decode 1) four individual speakers and instruments from one another (separately, within each category), 2) the superordinate category labels associated with each stimulus (speech or instrument), and 3) a set of simple synthesized sounds that could be differentiated entirely on their acoustic features. Data were collected using an interleaved silent steady state sequence to increase the temporal signal-to-noise ratio, and mitigate issues with auditory stimulus presentation in fMRI. Largely separable clusters of voxels in the temporal lobes supported the decoding of individual speakers and instruments from other stimuli in the same category. Decoding the superordinate category of each sound was more accurate and involved a larger portion of the temporal lobes. However, these clusters all overlapped with areas that could decode simple, acoustically separable stimuli. Thus, individual sound sources from different sound categories are represented in separate regions of the temporal lobes that are situated within regions implicated in more general acoustic processes. These results bridge an important gap in our understanding of cortical representations of sounds and their acoustics.

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Section: Introductionmentioning

confidence: 99%

Separable neural representations of sound sources: Speaker identity and musical timbre

Ogg¹,

Moraczewski²,

Kuchinsky

et al. 2019

Preprint

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Human listeners can quickly and easily recognize different sound sources (objects and events) in their environment. Understanding how this impressive ability is accomplished can improve signal processing and machine intelligence applications along with assistive listening technologies. However, it is not clear how the brain represents the many sounds that humans can recognize (such as speech and music) at the level of individual sources, categories and acoustic features. To examine the cortical organization of these representations, we used patterns of fMRI responses to decode 1) four individual speakers and instruments from one another (separately, within each category), 2) the superordinate category labels associated with each stimulus (speech or instrument), and 3) a set of simple synthesized sounds that could be differentiated entirely on their acoustic features. Data were collected using an interleaved silent steady state sequence to increase the temporal signal-to-noise ratio, and mitigate issues with auditory stimulus presentation in fMRI. Largely separable clusters of voxels in the temporal lobes supported the decoding of individual speakers and instruments from other stimuli in the same category.Decoding the superordinate category of each sound was more accurate and involved a larger portion of the temporal lobes. However, these clusters all overlapped with areas that could decode simple, acoustically separable stimuli. Thus, individual sound sources from different sound categories are represented in separate regions of the temporal lobes that are situated within regions implicated in more general acoustic processes. These results bridge an important gap in our understanding of cortical representations of sounds and their acoustics.

show abstract

Multivariate sensitivity to voice during auditory categorization

Cited by 14 publications

References 55 publications

Neural bases of action abstraction

Neural bases of action abstraction

Separable neural representations of sound sources: Speaker identity and musical timbre

Separable neural representations of sound sources: Speaker identity and musical timbre

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