Background/aims: Lexically guided perceptual learning in speech is the updating of linguistic categories based on novel input disambiguated by the structure provided in a recognized lexical item. We test the range of variation that allows for perceptual learning by presenting listeners with items that vary from subtle within-category variation to fully remapped cross-category variation.
Methods: Experiment 1 uses a lexically guided perceptual learning paradigm with words containing noncanonical /s/ realizations from s/ʃ continua that correspond to "typical," "ambiguous," "atypical," and "remapped" steps. Perceptual learning is tested in an s/ʃ categorization task. Experiment 2 addresses listener sensitivity to variation in the exposure items using AX discrimination tasks.
Results: Listeners in experiment 1 showed perceptual learning with the maximally ambiguous tokens. Performance of listeners in experiment 2 suggests that tokens which showed the most perceptual learning were not perceptually salient on their own.
Conclusion: These results demonstrate that perceptual learning is enhanced with maximally ambiguous stimuli. Excessively atypical pronunciations show attenuated perceptual learning, while typical pronunciations show no evidence for perceptual learning. AX discrimination illustrates that the maximally ambiguous stimuli are not perceptually unique. Together, these results suggest that perceptual learning relies on an interplay between confidence in phonetic and lexical predictions and category typicality.
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Mol Autism
January 2025
Department of Special Education, University of Haifa, Haifa, Israel.
Background: Alterations in sensory perception, a core phenotype of autism, are attributed to imbalanced integration of sensory information and prior knowledge during perceptual statistical (Bayesian) inference. This hypothesis has gained momentum in recent years, partly because it can be implemented both at the computational level, as in Bayesian perception, and at the level of canonical neural microcircuitry, as in predictive coding. However, empirical investigations have yielded conflicting results with evidence remaining limited.
View Article and Find Full Text PDFJ Exp Psychol Learn Mem Cogn
December 2024
University of Massachusetts-Amherst, Department of Psychological and Brain Sciences.
Listeners can use both lexical context (i.e., lexical knowledge activated by the word itself) and lexical predictions based on the content of a preceding sentence to adjust their phonetic categories to speaker idiosyncrasies.
View Article and Find Full Text PDFJ Exp Psychol Learn Mem Cogn
December 2024
Technical University of Darmstadt, Institute of Psychology.
The goal of the present investigation was to perform a registered replication of Jones and Macken's (1995b) study, which showed that the segregation of a sequence of sounds to distinct locations reduced the disruptive effect on serial recall. Thereby, it postulated an intriguing connection between auditory stream segregation and the cognitive mechanisms underlying the irrelevant speech effect. Specifically, it was found that a sequence of changing utterances was less disruptive in stereophonic presentation, allowing each auditory object (letters) to be allocated to a unique location (right ear, left ear, center), compared to when the same sounds were played monophonically.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
: Accurate volumetric assessment of lung nodules is an essential element of low-dose lung cancer screening programs. Current guidance recommends applying specific thresholds to measured nodule volume to make the following clinical decisions. In reality, however, CT scans often have heterogeneous slice thickness which is known to adversely impact the accuracy of nodule volume assessment.
View Article and Find Full Text PDFJpn J Radiol
January 2025
Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
Purpose: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers.
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