We investigated how printed and fingerspelled words are coded in short-term memory (STM) for deaf signers. Hearing people recode print into a phonological code for STM, but evidence for phonological coding in deaf readers is mixed. American Sign Language (ASL) also represents orthography with fingerspelling, but it is unclear how fingerspelling is coded in STM. 20 deaf signers and 20 hearing non-signers performed an immediate serial recall task with printed words and another 20 signers with fingerspelled words. Wordlists were manipulated for phonological and manual (fingerspelling) similarity. Both groups displayed a phonological similarity effect for printed words. Deaf signers also showed a phonological similarity effect for fingerspelled words, but there was no manual similarity effect. A follow up study will assess whether a manual similarity effect can be observed with a different similarity metric. Overall, the results suggest that fingerspelling is recoded into a speech-based phonological code in STM.

Sevcikova, Z. & Emmorey, K. (2014) Short-term Memory for Fingerspelling and Print. Paper presented at Center for Research in Language, University of California San Diego. 29 April 2014
http://crl.ucsd.edu/talks/pasttalks.php

Lexical and phonological properties of words, such as frequency and neighborhood density, affect many aspects of language processing. For many spoken languages, there are large databases that can be used to obtain this information, but for American Sign Language (and many other sign languages), no large corpora or normative datasets are currently available. Here we report the development of ASL-LEX, a soon to be publically available resource for sign language researchers, that contains lexical and phonological information for nearly 1,000 ASL signs. We collected subjective frequency ratings for each sign from 25-31 Deaf signers (native and early-exposed) using a 7-point scale (1 = very infrequent). We also collected iconicity ratings for each sign from 21-37 hearing non-signers using a 7-point scale (1 = not at all iconic). In addition, each entry has been coded for phonological features based on a modified version of the Prosodic Model (Brentari, 1998) from which neighborhood densities were calculated. The signs have also been coded for grammatical class and initialization, and the database contains time codes for the sign onset and offset within each video clip. ASL-LEX will soon be accessible online, and users will be able to search the database contents and access the sign videos using pre-defined search criteria.

Our analysis of the subjective frequency data from ASL-LEX reveals a strong correlation between the frequency ratings from native signers and early signers (exposed to ASL before age seven) (rs =.94, p<.001), replicating Mayberry et al. (2014). Thus, subjective frequency ratings are relatively stable across Deaf people who are proficient signers. Subjective frequency ratings for ASL signs (raw scores) were moderately correlated with the word frequencies of their English translations from SUBTLEX (rs =.58, p<.001). We observed a small, but significant correlation between frequency and iconicity (rs =–.17, p<.001), indicating a weak tendency for frequent signs to be rated as less iconic. This pattern is the opposite of that observed by Vinson et al. (2008) for British Sign Language, although they also observed a very weak correlation between subjective frequency and iconicity. Frequency ratings in ASL-LEX were normally distributed, while iconicity ratings were skewed toward the lower end of the scale (median iconicity rating = 2.71). Neighborhood density (ND) correlated weakly with frequency and with iconicity (frequency: rs =.11, p<.001; iconicity: rs =.12, p<.001). For this analysis, ND was defined as the number of sign neighbors sharing three out of four coded features (parallel to the way neighbors have been defined in the spoken language literature, i.e., words that share all but one phoneme). The weak correlation between ND and frequency parallels results from spoken languages (Frauenfelder et al., 1993). Overall, the results indicate weak relationships between iconicity, frequency, and neighborhood density for ASL. Although ASL-LEX is not a substitute for an ASL corpus, this database will be a valuable resource for designing tightly controlled experimental studies, as well as developing assessment and resource materials for education.

Sehyr Sevcikova, Z. Caselli, N. Cohen-Goldberg, A., & Emmorey, K. ASL-LEX: A lexical database of American Sign Language. Poster presented at The 12th Conference on Theoretical Issues in Sign Language Research (TISLR12), Melbourne Convention Centre, Australia, Jan 4-7 2016. Awarded Best Early Award Prize.
Poster abstract here
Poster PDF here

Paper to appear:
Caselli, N., Sehyr Sevcikova, Z., Cohen-Goldberg, A., & Emmorey, K. (2016) ASL-LEX: A lexical database of American Sign Language. Behavior Research Methods.

Please visit our project website:
http://asl-lex.org/

And keep an eye out on the project news here:
https://slhs.sdsu.edu/llcn/asl-lex/