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How learning to abstract shapes neural sound representations.

Ley A, Vroomen J, Formisano E - Front Neurosci (2014)

Bottom Line: We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models.Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations.With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Psychology and Neuropsychology, Tilburg School of Social and Behavioral Sciences, Tilburg University Tilburg, Netherlands ; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands.

ABSTRACT
The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.

No MeSH data available.


Related in: MedlinePlus

Illustration of the sensory-perceptual transformation of speech sounds. (A) Schematic representation of spectral patterns for the continuum between the phonemes /b/ and /d/. F1 and F2 reflect the first and second formant (i.e., amplitude peaks in the frequency spectrum). (B) Phoneme identification curves corresponding to the continuum in A. Curves are characterized by relatively stable percepts within a phoneme category and sharp transitions in between. Figure adapted from Liberman et al. (1957).
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Figure 1: Illustration of the sensory-perceptual transformation of speech sounds. (A) Schematic representation of spectral patterns for the continuum between the phonemes /b/ and /d/. F1 and F2 reflect the first and second formant (i.e., amplitude peaks in the frequency spectrum). (B) Phoneme identification curves corresponding to the continuum in A. Curves are characterized by relatively stable percepts within a phoneme category and sharp transitions in between. Figure adapted from Liberman et al. (1957).

Mentions: Speech sounds have been widely investigated in the context of sensory-perceptual transformation as they represent a prominent example of perceptual sound categories that comprise a large number of acoustically different sounds. Interestingly, there is not a clear boundary between two phoneme categories such as /b/ and /d/: the underlying acoustic features vary smoothly from one category to the next (Figure 1A). Remarkably though, if people are asked to identify individual sounds randomly taken from this spectrotemporal continuum as either /b/ or /d/ their percept does not vary gradually as suggested by the sensory input. Instead, the sounds from the first portion of the continuum are robustly identified as /b/, while the sounds from the second part are perceived as /d/ with an abrupt perceptual switch in between (Figure 1B). Performance on discrimination tests further suggests that people are fairly insensitive to the underlying variation of the stimuli within one phoneme category, mapping various physically different stimuli onto the same perceptual object (Liberman et al., 1957). At the category boundary, however, the same extent of physical difference is perceived as a change in stimulus identity. This difference in perceptual discrimination also affects speech production, which strongly relies on online monitoring of auditory feedback. Typically, a self-produced error in the articulation of a speech sound is instantaneously corrected for if, e.g., the output vowel differs from the intended vowel category. An acoustic deviation of the same magnitude and direction may however be tolerated if the produced sound and the intended sound fall within the same perceptual category (Niziolek and Guenther, 2013). This suggests that the within-category differences in the physical domain are perceptually compressed to create a robust representation of the phoneme category while between-category differences are perceptually enhanced to rapidly detect the relevant change of phoneme identity. This phenomenon is termed “Categorical Perception” (CP, Harnad, 1987) and has been demonstrated for stimuli from various natural domains apart from speech, such as music (Burns and Ward, 1978), color (Bornstein et al., 1976; Franklin and Davies, 2004) and facial expressions of emotion (Etcoff and Magee, 1992), not only for humans but also for monkeys (Freedman et al., 2001, 2003), chinchillas (Kuhl and Miller, 1975), songbirds (Prather et al., 2009), and even crickets (Wyttenbach et al., 1996). Thus, the formation of discrete perceptual categories from a continuous physical signal seems to be a universal reduction mechanism to deal with the complexity of natural environments.


How learning to abstract shapes neural sound representations.

Ley A, Vroomen J, Formisano E - Front Neurosci (2014)

Illustration of the sensory-perceptual transformation of speech sounds. (A) Schematic representation of spectral patterns for the continuum between the phonemes /b/ and /d/. F1 and F2 reflect the first and second formant (i.e., amplitude peaks in the frequency spectrum). (B) Phoneme identification curves corresponding to the continuum in A. Curves are characterized by relatively stable percepts within a phoneme category and sharp transitions in between. Figure adapted from Liberman et al. (1957).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4043152&req=5

Figure 1: Illustration of the sensory-perceptual transformation of speech sounds. (A) Schematic representation of spectral patterns for the continuum between the phonemes /b/ and /d/. F1 and F2 reflect the first and second formant (i.e., amplitude peaks in the frequency spectrum). (B) Phoneme identification curves corresponding to the continuum in A. Curves are characterized by relatively stable percepts within a phoneme category and sharp transitions in between. Figure adapted from Liberman et al. (1957).
Mentions: Speech sounds have been widely investigated in the context of sensory-perceptual transformation as they represent a prominent example of perceptual sound categories that comprise a large number of acoustically different sounds. Interestingly, there is not a clear boundary between two phoneme categories such as /b/ and /d/: the underlying acoustic features vary smoothly from one category to the next (Figure 1A). Remarkably though, if people are asked to identify individual sounds randomly taken from this spectrotemporal continuum as either /b/ or /d/ their percept does not vary gradually as suggested by the sensory input. Instead, the sounds from the first portion of the continuum are robustly identified as /b/, while the sounds from the second part are perceived as /d/ with an abrupt perceptual switch in between (Figure 1B). Performance on discrimination tests further suggests that people are fairly insensitive to the underlying variation of the stimuli within one phoneme category, mapping various physically different stimuli onto the same perceptual object (Liberman et al., 1957). At the category boundary, however, the same extent of physical difference is perceived as a change in stimulus identity. This difference in perceptual discrimination also affects speech production, which strongly relies on online monitoring of auditory feedback. Typically, a self-produced error in the articulation of a speech sound is instantaneously corrected for if, e.g., the output vowel differs from the intended vowel category. An acoustic deviation of the same magnitude and direction may however be tolerated if the produced sound and the intended sound fall within the same perceptual category (Niziolek and Guenther, 2013). This suggests that the within-category differences in the physical domain are perceptually compressed to create a robust representation of the phoneme category while between-category differences are perceptually enhanced to rapidly detect the relevant change of phoneme identity. This phenomenon is termed “Categorical Perception” (CP, Harnad, 1987) and has been demonstrated for stimuli from various natural domains apart from speech, such as music (Burns and Ward, 1978), color (Bornstein et al., 1976; Franklin and Davies, 2004) and facial expressions of emotion (Etcoff and Magee, 1992), not only for humans but also for monkeys (Freedman et al., 2001, 2003), chinchillas (Kuhl and Miller, 1975), songbirds (Prather et al., 2009), and even crickets (Wyttenbach et al., 1996). Thus, the formation of discrete perceptual categories from a continuous physical signal seems to be a universal reduction mechanism to deal with the complexity of natural environments.

Bottom Line: We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models.Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations.With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Psychology and Neuropsychology, Tilburg School of Social and Behavioral Sciences, Tilburg University Tilburg, Netherlands ; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands.

ABSTRACT
The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.

No MeSH data available.


Related in: MedlinePlus