Publications

Abstract

Although many studies have investigated the activation of perceptual representations during language comprehension, to our knowledge only one previous study has directly tested how perceptual features are combined into representations during comprehension. In their classic study, Potter and Faulconer [(1979). Understanding noun phrases. Journal of Verbal Learning and Verbal Behavior, 18, 509–521.] investigated the perceptual representation of adjective-noun combinations. However, their non-orthogonal design did not allow the differentiation between conjunctive vs. disjunctive representations. Using randomized orthogonal designs, we observe evidence for disjunctive perceptual representations when participants represent feature combinations simultaneously (in several experiments; N = 469), and we observe evidence for conjunctive perceptual representations when participants represent feature combinations sequentially (In several experiments; N = 628). Our findings show that the generation of conjunctive representations during comprehension depends on the concatenation of linguistic cues, and thus suggest the construction of elaborate perceptual representations may critically depend on language.

Abstract

A major theoretical debate in language acquisition research regards the learnability of hierarchical structures. The artificial grammar learning methodology is increasingly influential in approaching this question. Studies using an artificial centre-embedded AnBn grammar without semantics draw conflicting conclusions. This study investigates the facilitating effect of distributional biases in simple AB adjacencies in the input sample—caused in natural languages, among others, by semantic biases—on learning a centre-embedded structure. A mathematical simulation of the linguistic input and the learning, comparing various distributional biases in AB pairs, suggests that strong distributional biases might help us to grasp the complex AnBn hierarchical structure in a later stage. This theoretical investigation might contribute to our understanding of how distributional features of the input—including those caused by semantic variation—help learning complex structures in natural languages.