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  1. Spoken language achieves robustness and evolvability by exploiting degeneracy and neutrality.Bodo Winter - 2014 - Bioessays 36 (10):960-967.
    As with biological systems, spoken languages are strikingly robust against perturbations. This paper shows that languages achieve robustness in a way that is highly similar to many biological systems. For example, speech sounds are encoded via multiple acoustically diverse, temporally distributed and functionally redundant cues, characteristics that bear similarities to what biologists call “degeneracy”. Speech is furthermore adequately characterized by neutrality, with many different tongue configurations leading to similar acoustic outputs, and different acoustic variants understood as the same by recipients. (...)
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  • Cue integration with categories: Weighting acoustic cues in speech using unsupervised learning and distributional statistics.Joseph C. Toscano & Bob McMurray - 2010 - Cognitive Science 34 (3):434.
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  • Tone Language Fluency Impairs Pitch Discrimination.Isabelle Peretz, Sébastien Nguyen & Stéphanie Cummings - 2011 - Frontiers in Psychology 2.
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  • A perceptual interference account of acquisition difficulties for non-native phonemes.Paul Iverson, Patricia K. Kuhl, Reiko Akahane-Yamada, Eugen Diesch, Yoh'ich Tohkura, Andreas Kettermann & Claudia Siebert - 2003 - Cognition 87 (1):B47-B57.
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  • Better than native: Tone language experience enhances English lexical stress discrimination in Cantonese-English bilingual listeners.William Choi, Xiuli Tong & Arthur G. Samuel - 2019 - Cognition 189 (C):188-192.
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  • The Reverse Hierarchy Theory of Visual Perceptual Learning.Merav Ahissar & Shaul Hochstein - 2004 - Trends in Cognitive Sciences 8 (10):457-464.
    Perceptual learning can be defined as practice-induced improvement in the ability to perform specific perceptual tasks. We previously proposed the Reverse Hierarchy Theory as a unifying concept that links behavioral findings of visual learning with physiological and anatomical data. Essentially, it asserts that learning is a top-down guided process, which begins at high-level areas of the visual system, and when these do not suffice, progresses backwards to the input levels, which have a better signal-to-noise ratio. This simple concept has proved (...)
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