skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Asymetric Event-Related Potential Priming Effects Between English Letters and American Sign Language Fingerspelling Fonts
Letter recognition plays an important role in reading and follows different phases of processing, from early visual feature detection to the access of abstract letter representations. Deaf ASL–English bilinguals experience orthography in two forms: English letters and fingerspelling. However, the neurobiological nature of fingerspelling representations, and the relationship between the two orthographies, remains unexplored. We examined the temporal dynamics of single English letter and ASL fingerspelling font processing in an unmasked priming paradigm with centrally presented targets for 200 ms preceded by 100 ms primes. Event-related brain potentials were recorded while participants performed a probe detection task. Experiment 1 examined English letter-to-letter priming in deaf signers and hearing non-signers. We found that English letter recognition is similar for deaf and hearing readers, extending previous findings with hearing readers to unmasked presentations. Experiment 2 examined priming effects between English letters and ASL fingerspelling fonts in deaf signers only. We found that fingerspelling fonts primed both fingerspelling fonts and English letters, but English letters did not prime fingerspelling fonts, indicating a priming asymmetry between letters and fingerspelling fonts. We also found an N400-like priming effect when the primes were fingerspelling fonts which might reflect strategic access to the lexical names of letters. The studies suggest that deaf ASL–English bilinguals process English letters and ASL fingerspelling differently and that the two systems may have distinct neural representations. However, the fact that fingerspelling fonts can prime English letters suggests that the two orthographies may share abstract representations to some extent.  more » « less
Award ID(s):
1918556
PAR ID:
10481701
Author(s) / Creator(s):
; ; ;
Editor(s):
Corina, David P.
Publisher / Repository:
Neurobiology of Language
Date Published:
Journal Name:
Neurobiology of Language
Volume:
4
Issue:
2
ISSN:
2641-4368
Page Range / eLocation ID:
361 to 381
Subject(s) / Keyword(s):
American Sign Language (ASL), fingerspelling, N400, electroencephalography, event-related potentials, ERP, orthography, deaf signers
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract The lexical quality hypothesis proposes that the quality of phonological, orthographic, and semantic representations impacts reading comprehension. In Study 1, we evaluated the contributions of lexical quality to reading comprehension in 97 deaf and 98 hearing adults matched for reading ability. While phonological awareness was a strong predictor for hearing readers, for deaf readers, orthographic precision and semantic knowledge, not phonology, predicted reading comprehension (assessed by two different tests). For deaf readers, the architecture of the reading system adapts by shifting reliance from (coarse-grained) phonological representations to high-quality orthographic and semantic representations. In Study 2, we examined the contribution of American Sign Language (ASL) variables to reading comprehension in 83 deaf adults. Fingerspelling (FS) and ASL comprehension skills predicted reading comprehension. We suggest that FS might reinforce orthographic-to-semantic mappings and that sign language comprehension may serve as a linguistic basis for the development of skilled reading in deaf signers. 
    more » « less
  2. Little is known about how information to the left of fixation impacts reading and how it may help to integrate what has been read into the context of the sentence. To better understand the role of this leftward information and how it may be beneficial during reading, we compared the sizes of the leftward span for reading-matched deaf signers ( n = 32) and hearing adults ( n = 40) using a gaze-contingent moving window paradigm with windows of 1, 4, 7, 10, and 13 characters to the left, as well as a no-window condition. All deaf participants were prelingually and profoundly deaf, used American Sign Language (ASL) as a primary means of communication, and were exposed to ASL before age eight. Analysis of reading rates indicated that deaf readers had a leftward span of 10 characters, compared to four characters for hearing readers, and the size of the span was positively related to reading comprehension ability for deaf but not hearing readers. These findings suggest that deaf readers may engage in continued word processing of information obtained to the left of fixation, making reading more efficient, and showing a qualitatively different reading process than hearing readers. 
    more » « less
  3. Abstract Limited language experience in childhood is common among deaf individuals, which prior research has shown to lead to low levels of language processing. Although basic structures such as word order have been found to be resilient to conditions of sparse language input in early life, whether they are robust to conditions of extreme language delay is unknown. The sentence comprehension strategies of post‐childhood, first‐language (L1) learners of American Sign Language (ASL) with at least 9 years of language experience were investigated, in comparison to two control groups of learners with full access to language from birth (deaf native signers and hearing L2 learners who were native English speakers). The results of a sentence‐to‐picture matching experiment show that event knowledge overrides word order for post‐childhood L1 learners, regardless of the animacy of the subject, while both deaf native signers and hearing L2 signers consistently rely on word order to comprehend sentences. Language inaccessibility throughout early childhood impedes the acquisition of even basic word order. Similar to the strategies used by very young children prior to the development of basic sentence structure, post‐childhood L1 learners rely more on context and event knowledge to comprehend sentences. Language experience during childhood is critical to the development of basic sentence structure. 
    more » « less
  4. Sign language is a complex visual language, and automatic interpretations of sign language can facilitate communication involving deaf individuals. As one of the essential components of sign language, fingerspelling connects the natural spoken languages to the sign language and expands the scale of sign language vocabulary. In practice, it is challenging to analyze fingerspelling alphabets due to their signing speed and small motion range. The usage of synthetic data has the potential of further improving fingerspelling alphabets analysis at scale. In this paper, we evaluate how different video-based human representations perform in a framework for Alphabet Generation for American Sign Language (ASL). We tested three mainstream video-based human representations: twostream inflated 3D ConvNet, 3D landmarks of body joints, and rotation matrices of body joints. We also evaluated the effect of different skeleton graphs and selected body joints. The generation process of ASL fingerspelling used a transformerbased Conditional Variational Autoencoder. To train the model, we collected ASL alphabet signing videos from 17 signers with dynamic alphabet signing. The generated alphabets were evaluated using automatic metrics of quality such as FID, and we also considered supervised metrics by recognizing the generated entries using Spatio-Temporal Graph Convolutional Networks. Our experiments show that using the rotation matrices of the upper body joints and the signing hand give the best results for the generation of ASL alphabet signing. Going forward, our goal is to produce articulated fingerspelling words by combining individual alphabets learned in this work. 
    more » « less
  5. null (Ed.)
    Deaf spaces are unique indoor environments designed to optimize visual communication and Deaf cultural expression. However, much of the technological research geared towards the deaf involve use of video or wearables for American sign language (ASL) translation, with little consideration for Deaf perspective on privacy and usability of the technology. In contrast to video, RF sensors offer the avenue for ambient ASL recognition while also preserving privacy for Deaf signers. Methods: This paper investigates the RF transmit waveform parameters required for effective measurement of ASL signs and their effect on word-level classification accuracy attained with transfer learning and convolutional autoencoders (CAE). A multi-frequency fusion network is proposed to exploit data from all sensors in an RF sensor network and improve the recognition accuracy of fluent ASL signing. Results: For fluent signers, CAEs yield a 20-sign classification accuracy of %76 at 77 GHz and %73 at 24 GHz, while at X-band (10 Ghz) accuracy drops to 67%. For hearing imitation signers, signs are more separable, resulting in a 96% accuracy with CAEs. Further, fluent ASL recognition accuracy is significantly increased with use of the multi-frequency fusion network, which boosts the 20-sign fluent ASL recognition accuracy to 95%, surpassing conventional feature level fusion by 12%. Implications: Signing involves finer spatiotemporal dynamics than typical hand gestures, and thus requires interrogation with a transmit waveform that has a rapid succession of pulses and high bandwidth. Millimeter wave RF frequencies also yield greater accuracy due to the increased Doppler spread of the radar backscatter. Comparative analysis of articulation dynamics also shows that imitation signing is not representative of fluent signing, and not effective in pre-training networks for fluent ASL classification. Deep neural networks employing multi-frequency fusion capture both shared, as well as sensor-specific features and thus offer significant performance gains in comparison to using a single sensor or feature-level fusion. 
    more » « less