Experience-Based Probabilities Modulate Expectations in a Gender-Coded Artificial Language.

ABSTRACT: The current study combines artificial language learning with visual world eyetracking to investigate acquisition of representations associating spoken words and visual referents using morphologically complex pseudowords. Pseudowords were constructed to consistently encode referential gender by means of suffixation for a set of imaginary figures that could be either male or female. During training, the frequency of exposure to pseudowords and their imaginary figure referents were manipulated such that a given word and its referent would be more likely to occur in either the masculine form or the feminine form, or both forms would be equally likely. Results show that these experience-based probabilities affect the formation of new representations to the extent that participants were faster a

SUBMITTER: Ottl A 

PROVIDER: S-EPMC4993866 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature