Sign recognition: the effect of parameters and features in sign mispronunciations.

We investigate the degree to which mispronounced signs can be accommodated by signers of French Sign Language (LSF). Using an offline judgment task, we examine both the individual contributions of three parameters - handshape, movement, and location - to sign recognition, and the impact of the individual features that were manipulated to obtain the mispronounced signs. Results indicate that signers judge mispronounced handshapes to be less damaging for well-formedness than mispronounced locations or movements. In addition to this macro-effect of parameter, individual features are found to carry different weights during sign recognition, mirroring what has been reported for phonological features in spoken languages. Together, these results thus further support an underlying a-modal phonological architecture for human language, including feature-based phonological representations.