A key challenge when learning language in naturalistic circumstances is to extract linguistic information from a continuous stream of speech. This study investigates the predictors of such implicit learning among adults exposed to a new language in a new modality. Sign-naïve participants were shown a 4-min weather forecast in Swedish Sign Language. Subsequently, we tested their ability to recognise 22 target sign forms that had been viewed in the forecast, amongst 44 distractor signs that had not been viewed. The target (...) items differed in their occurrence frequency in the forecast and in their degree of iconicity. The results revealed that both frequency and iconicity facilitated recognition of target signs cumulatively. The adult mechanism for language learning thus operates similarly on sign and spoken languages as regards frequency, but also exploits modality-salient properties, for example iconicity for sign languages. Individual differences in cognitive skills and language learning background did not predict recognition. The properties of the input thus influenced adults’ language learning abilities at first exposure more than individual differences. (shrink)

Silent gesture is not considered to be linguistic, on par with spoken and sign languages. It is claimed that silent gestures, unlike language, represent events holistically, without compositional structure. However, recent research has demonstrated that gesturers use consistent strategies when representing objects and events, and that there are behavioral and clinically relevant limits on what form a gesture may take to effect a particular meaning. This systematicity challenges a holistic interpretation of silent gesture, which predicts that there should be no (...) stable form‐meaning correspondence across event representations. Here, we demonstrate to the contrary that untrained gesturers systematically manipulate the form of their gestures when representing events with and without a theme (e.g., Someone popped the balloon vs. Someone walked), that is, transitive and intransitive events. We elicited silent gestures and annotated them for manual features active in coding transitivity distinctions in sign languages. We trained linear support vector machines to make item‐by‐item transitivity predictions based on these features. Prediction accuracy was good across the entire dataset, thus demonstrating that systematicity in silent gesture can be explained with recourse to subunits. We argue that handshape features are constructs co‐opted from cognitive systems subserving manual action production and comprehension for communicative purposes, which may integrate into the linguistic system of emerging sign languages. We further suggest that nonsigners tend to map event participants to each hand, a strategy found across genetically and geographically distinct sign languages, suggesting the strategy's cognitive foundation. (shrink)